Towards an understanding of fault-system mechanics: from single earthquakes on isolated faults to millenial-scale collective plate-boundary fault-system behavior

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Content TOWARDS AN UNDERSTANDING OF FAULT-SYSTEM MECHANICS: FROM SINGLE
EARTHQUAKES ON ISOLATED FAULTS TO MILLENNIAL-SCALE COLLECTIVE PLATEBOUNDARY FAULT-SYSTEM BEHAVIOR
by
Judith Gauriau
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(GEOLOGICAL SCIENCES)
May 2024
Copyright 2024 Judith Gauriau

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ACKNOWLEDGEMENTS
I would like to acknowledge all the persons without whom this PhD adventure would not have been
possible. First of all, I would like to thank my advisor James Dolan, who offered me this PhD opportunity
at the University of Southern California, whose trust and belief in me helped me get to where I am today.
This adventure started with an online PhD advert to which I replied with enthusiasm, in November 2018.
A few months later, I was officially offered to start a PhD in California. I started this adventure at the same
time as my colleagues Dannielle Fougere and Chris Anthonissen. As international students, we had this
common journey of being foreigners in the western US, and starting a new life from scratch as graduate
students, in the second biggest city of this country. I would like to thank both of them for their support as
colleagues and office mates. Caje Weigandt joined us later, and was an excellent add to our group. I would
like to thank him for bringing such good spirits and muscles during our field trips, and, in particular, for
being such a good field assistant in New Zealand. Field work requires helpful hands and patience, and I
would like to thank all my colleagues – Dannielle, Chris, Caje and Masters’ student Luke Gordon - for
helping out for sampling in hot, dry environments, and for allowing me to help them in similar places when
they needed.
I would like to thank my co-advisor Sylvain Barbot, member of my Qualifying Examination committee
and PhD Defense committee. He allowed me to get a sense of earthquake numerical simulations, and helped
me get sufficient technical knowledge to carry out a project with him. I am grateful for the time he spent
explaining basic principles of earthquake mechanics and of his motorcycle code. On this note, I would like
to thank Sharadha Sathiakumar and Binhao Wang who also helped get through some challenges related to
the use of earthquake simulations.
I would like to thank my other Qualifying Examination committee members: John Vidale, Joshua West
and Steve Nutt, who provided excellent feedback on my research projects at the early stages of my PhD.

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Many thanks to my PhD Defense external member Steve Nutt, from Viterbi Engineering School, who
kindly accepted to serve on my defense committee.
I would like to thank my collaborators from New Zealand, Russ Van Dissen and Tim Little, who
provided great help in organizing challenging field work in New Zealand, after three years of waiting time
before the kiwi borders would reopen to international visitors. Their enthusiasm and positive attitude were
very inspiring, and I am delighted to have been collaborating with them. Sharing musical and culinary
moments with them was awesome. I thank Russ and his wife for their very kind hospitality in their fairytale
home. I hope to pursue this pleasant collaboration with Russ and Tim on still ongoing research!
I would like to thank Tom Rockwell for helping start slip-rate investigations on the Elsinore and San
Jacinto faults, and providing me with all necessary documentation to generate a slip-rate record for the
Elsinore fault.
I would like to thank Edward Rhodes as well as Andrew Ivester for their collaboration on the dating of
river sediment samples. They provided (and will provide) age results that are key to the research presented
in this manuscript.
USC staff who make the Earth Sciences department running have been very helpful to render my
experience at USC smoother: Vardui Tersimonian, Cindy Waite, John Yu, Steve Lin, Miguel Rincon. In
addition, I would like to thank Darlene Garza for helping me organizing my PhD defense, and for keeping
track of where I was standing in the whole process. I would also like to thank Karen Young and Alexandra
Aloia for being great money handlers, and for being available to answer grant-related questions.
Finally, I would like to thank my family, my friends, and my life partner, who shared this adventure
with me.

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This dissertation includes parts of the following manuscripts:
1. Gauriau, J., & Dolan, J. F. (2021). Relative Structural Complexity of Plate‐Boundary Fault Systems
Controls Incremental Slip‐Rate Behavior of Major Strike‐Slip Faults. Geochemistry, Geophysics,
Geosystems, 22(11), e2021GC009938.
2. Gauriau, J., & Dolan, J. F. (2024). Comparison of geodetic slip-deficit and geologic fault slip rates
reveals that variability of elastic strain accumulation and release rates on strike-slip faults is
controlled by the relative structural complexity of plate-boundary fault systems. Seismica, 3:1.
3. Gauriau, J., Barbot, S., Dolan, J. F. (2023). Islands of chaos in a sea of periodic earthquakes. Earth
and Planetary Science Letters, 618: 118274.

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TABLE OF CONTENTS
ACKNOWLEDGEMENTS..........................................................................................................................ii
List of Tables .........................................................................................................................................xii
List of Figures ........................................................................................................................................ xiv
ABSTRACT .......................................................................................................................................xliv
CHAPTER 1 Introduction....................................................................................................................... 1
1.1. Earthquake cycle conundrums..........................................................................................................1
1.2. Earthquake-scale fault behavior........................................................................................................7
1.3. Millennial-scale fault behavior .........................................................................................................8
1.3.1. Determining fault offsets ......................................................................................................8
1.3.2. Dating fault offsets..............................................................................................................11
1.3.3. Modeling incremental slip rates..........................................................................................13
1.4. Comparison of slip-rate behavior with structural complexity.........................................................14
1.5. Comparison of geologic slip rates with geodetic slip-deficit rates.................................................15
1.6. Motivation for this research ............................................................................................................17
CHAPTER 2 Relative structural complexity of plate-boundary fault systems controls incremental
slip-rate behavior of major strike-slip faults............................................................................................... 19
2.1. Abstract...........................................................................................................................................19
2.2. Introduction.....................................................................................................................................20
2.3. Data and Results .............................................................................................................................25
2.3.1. The Alpine-Marlborough fault system, New Zealand ........................................................25
2.3.2. The San Andreas fault system, California...........................................................................30
2.3.3. The North Anatolian fault, Turkey......................................................................................34
2.3.4. The Dead Sea fault, Middle East ........................................................................................38
2.4. Comparison of CoCo and slip-rate variability ................................................................................42
2.5. Discussion.......................................................................................................................................48

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2.6. Conclusions.....................................................................................................................................56
CHAPTER 3 Comparison of geodetic slip-deficit and geologic fault slip rates reveals that
variability of elastic strain accumulation and release rates on strike-slip faults is controlled by the
relative structural complexity of plate-boundary fault systems.................................................................. 58
3.1. Abstract...........................................................................................................................................58
3.2. Introduction.....................................................................................................................................59
3.3. Studied faults and terminology .......................................................................................................61
3.4. Consideration of elapsed time since most recent event relative to sampling geodetic slip-deficit
rates67
3.5. Relative structural complexity of the surrounding fault network in interpretation of geodetic slipdeficit rate and geologic slip-rate comparisons.......................................................................................68
3.6. Comparison of geologic slip rates and geodetically based slip-deficit rates on strike-slip faults...69
3.7. Fault loading rates….......................................................................................................................74
3.7.1. …are constant on low-CoCo faults.....................................................................................74
3.7.2. …vary on high-CoCo faults................................................................................................74
3.8. Ductile shear zone behavior…........................................................................................................83
3.8.1. …on high-CoCo faults........................................................................................................83
3.8.2. …on low-CoCo faults.........................................................................................................84
3.9. Fault’s near-future behavior, and further applications for PSHA ...................................................86
3.10.Conclusions.....................................................................................................................................88
CHAPTER 4 Co-seismic displacements on the Kekerengu fault during the past three to five
earthquakes at Bluff Station, New Zealand ................................................................................................ 91
4.1. Abstract...........................................................................................................................................91
4.2. Introduction.....................................................................................................................................91
4.3. The Kekerengu fault and the Bluff Station site...............................................................................93
4.4. Offset measurements.......................................................................................................................96
4.5. Luminescence and radiocarbon dating............................................................................................98
4.6. Matching terrace offsets with abandonment ages.........................................................................102
4.7. Comparing terrace surface abandonment ages to paleoearthquake ages ......................................103
4.8. Elements of discussion and preliminary conclusions....................................................................106
4.8.1. Implications for Holocene slip rate of the Kekerengu fault..............................................106
4.8.2. Variability of coseismic slip at a point..............................................................................107

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4.8.3. Timing of earthquakes in coastal Kaikōura ranges...........................................................112
4.9. Conclusions...................................................................................................................................116
4.10.Acknowledgements.......................................................................................................................116
CHAPTER 5 Latest Pleistocene-Holocene incremental slip rates of the Kekerengu fault, South
Island, New Zealand ................................................................................................................................. 118
5.1. Abstract.........................................................................................................................................118
5.2. Introduction...................................................................................................................................118
5.3. Geologic overview........................................................................................................................119
5.3.1. Tectonic setting.................................................................................................................119
5.3.2. Slip-rate sites.....................................................................................................................121
5.4. The Bluff Station site ....................................................................................................................122
5.4.1. Bluff Station offsets..........................................................................................................124
5.4.2. Bluff Station age determinations.......................................................................................132
5.5. The Shag Bend site .......................................................................................................................135
5.5.1. Shag Bend offsets..............................................................................................................136
5.5.2. Shag Bend age determinations..........................................................................................140
5.6. The Black Hut site ........................................................................................................................141
5.6.1. Black Hut 310 ± 30 m offset.............................................................................................143
5.6.2. Kaikōura earthquake coseismic displacement at Black Hut .............................................144
5.6.3. Black Hut sample locations...............................................................................................144
5.7. McLean Stream site ......................................................................................................................145
5.7.1. McLean Stream offsets .....................................................................................................148
5.7.2. McLean Stream age determinations..................................................................................154
5.8. Combined incremental slip-rate history of the Kekerengu fault...................................................155
5.9. Elements of discussion and conclusions.......................................................................................156
5.10.Acknowledgements.......................................................................................................................157
CHAPTER 6 Holocene incremental slip rate of the central Wairarapa fault at Waiohine River, New
Zealand ....................................................................................................................................... 158
6.1. Abstract.........................................................................................................................................158
6.2. Introduction...................................................................................................................................159
6.3. Tectonic context............................................................................................................................161

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6.4. The Waiohine River site ...............................................................................................................164
6.4.1. Geomorphology of the Waiohine River site .....................................................................164
6.4.2. Previous offset and slip-rate studies..................................................................................165
6.5. Offset measurements.....................................................................................................................166
6.5.1. Measurement of right-lateral displacements.....................................................................166
6.5.2. Measurement of vertical displacements............................................................................170
6.5.3. Comparison of horizontal and vertical displacements......................................................173
6.6. Determination of terrace ages.......................................................................................................175
6.7. Determination of incremental slip rates........................................................................................177
6.8. Elements of discussion and conclusion.........................................................................................180
6.9. Acknowledgements.......................................................................................................................184
CHAPTER 7 Pleistocene slip rate of the northern Elsinore fault at Glen Eden.................................. 185
7.1. Abstract.........................................................................................................................................185
7.2. Introduction...................................................................................................................................185
7.3. Regional tectonic setting...............................................................................................................186
7.4. The Glen Eden site........................................................................................................................190
7.5. Offset measurements.....................................................................................................................192
7.6. Age determinations.......................................................................................................................194
7.7. Slip-rate determination..................................................................................................................195
7.8. Elements of discussion..................................................................................................................196
7.9. Acknowledgements.......................................................................................................................197
CHAPTER 8 Islands of chaos in a sea of periodic earthquakes: How chaotic earthquake recurrence
patterns explain behavior of one of the most regular strike-slip faults in the world................................. 198
8.1. Abstract.........................................................................................................................................198
8.2. Introduction...................................................................................................................................199
8.3. Methods.........................................................................................................................................202
8.3.1. Governing equations and physical assumptions ...............................................................202
8.3.2. Non-dimensional parameters ............................................................................................203
8.3.3. Model architecture ............................................................................................................205
8.3.4. Model exploration strategy ...............................................................................................208
8.4. Results...........................................................................................................................................208

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8.5. Discussion.....................................................................................................................................214
8.6. Conclusion ....................................................................................................................................219
CHAPTER 9 Conclusions................................................................................................................... 220
References ....................................................................................................................................... 222
Appendix A. Supplements for CHAPTER 2 ...................................................................................... 255
A.1. Supplementary figures..................................................................................................................255
A.2. Supplementary tables....................................................................................................................260
A.3. Fault databases used in this study .................................................................................................313
A.3.1. New Zealand fault database ..............................................................................................313
A.3.2. California fault database ...................................................................................................313
A.3.3. North Anatolian fault system database .............................................................................314
A.3.4. Dead Sea fault system database ........................................................................................316
A.4. Additional details about how slip-rate data were used in the computation of CoCo ....................317
A.5. A note about the potential importance of the scale used in fault trace compilations when
computing the CoCo metric ..................................................................................................................319
Appendix B. Supplements for CHAPTER 3 ...................................................................................... 320
B.1. Calculation of CoCo values ..........................................................................................................320
B.2. Remarks on the behavior of faults with intermediate CoCo values..............................................320
B.3. Comparison of geodetic rates with geologic rates ........................................................................321
B.4. Most recent events and recurrence intervals.................................................................................323
B.5. Choice of geodetic rates................................................................................................................326
B.6. Measure of the dispersion in Figure 3.3b......................................................................................326
B.7. Dispersion of data points in Figure 3.2c .......................................................................................327
Appendix C. Supplements for CHAPTER 4 ...................................................................................... 329
C.1. Map and restoration figures supporting Chapter 4........................................................................329
C.2. Method for correlating T7 surface upstream and downstream of the fault...................................335
C.3. Definition of input probability density functions (PDFs) in RISeR..............................................337

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C.3.1. Definition of paleoearthquake age PDFs ..........................................................................338
C.3.2. Definition of PDFs for the displacements.........................................................................338
Appendix D. Supplements for CHAPTER 5 ...................................................................................... 346
Appendix E. Supplements for CHAPTER 6 ...................................................................................... 360
E.1. Offset restorations.........................................................................................................................360
E.2. Method for measuring vertical displacements ..............................................................................369
E.3. Field photographs..........................................................................................................................370
Appendix F. Supplements for CHAPTER 7 ...................................................................................... 372
Appendix G. Supplements for CHAPTER 8 ...................................................................................... 378
G.1. Explored parameter spaces............................................................................................................378
G.2. Slip per event and slip rate at Hokuri Creek .................................................................................378
G.3. Earthquake recurrence patterns for different effective normal stress values ................................380
G.4. Fitting earthquake simulations to the Hokuri Creek data..............................................................383
G.4.1. Trimming of the events.....................................................................................................383
G.4.2. Browsing all possible intervals of 23 recurrence intervals ...............................................384
G.4.3. Ways of ranking................................................................................................................385
G.5. Ranking simulations......................................................................................................................386
G.6. Recurrence time intervals, average slip per event and CoV for selected best fits ........................389
G.7. Seismic simulations for best obtained results ...............................................................................394
Appendix H. How plate-like are the Nazca and the Pacific plates at the 10,000-year time scale?
Preliminary steps towards characterization of variable mid-ocean ridge spreading rates. ....................... 400
H.1. Introduction...................................................................................................................................400
H.2. Study area and data .......................................................................................................................401
H.3. Bathymetric profiles......................................................................................................................402
H.4. Cross-correlations between profiles..............................................................................................403
H.5. Hierarchical clustering ..................................................................................................................408
H.6. Dynamic time warping..................................................................................................................411

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H.6.1. Relationship between abyssal hills spacing and half spreading rates ...............................411
H.6.2. Dynamic time warping as a tool for observing variations of spreading along the ridge...413
H.7. Conclusions...................................................................................................................................414

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List of Tables
Table 2.1: Plate motion rates used to standardize CoCo values, and extreme slip rate values inferred
from available slip-rate records at all sites from which the slip-rate variability is inferred (slip rate (SR)
variability equals the ratio between the highest incremental slip rate of the record and the lowest one).
See Table A.5 and Table A.6 for uncertainties and comments, and Figure A.1 to Figure A.4 for
visualization of published incremental slip-rate records.............................................................................43
Table 3.1: Summary of data from the different fault sections used in this study, including smalldisplacement (SD), large-displacement (LD) averaged geologic slip rates (in mm/yr) with
corresponding time and displacement ranges over which they are averaged, and geodetic slip-deficit
rates (in mm/yr). The rate values are reported as they were in their original source publications, unless
specified otherwise......................................................................................................................................63
Table 4.1: Radiocarbon ages of samples collected from T6B. All pieces of sample were charcoal........101
Table 6.1: Horizontal and vertical measurements from cumulative displacements at Waiohine River,
and related horizontal to vertical component ratios (H/V ratios). Comparison between our results and
the ones published in Carne et al. (2011)..................................................................................................174
Table A.1: Active fault database of California - references for each fault and slip rates used for CoCo
values computation. ..................................................................................................................................260
Table A.2: Active fault database of Turkey - references for each fault trace and its related slip rate.
EMME refers to the Earthquake Model of the Middle East. ....................................................................270
Table A.3: Active fault database used for the Middle East (Dead Sea fault system). Geographic
coordinates of end points of each fault segment are given for sake of spatial understanding, as many of

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the faults are still unnamed. The end points of fault segments are referred as A and B, and the
coordinates are given in the WGS84/Pseudo-Mercator system. References for each fault trace and its
related slip rate are given as well..............................................................................................................287
Table A.4: Coefficients of complexity (CoCo) values for all study sites of the four strike-slip plate
boundary systems, for different values of radii.........................................................................................308
Table A.5: Plate motion rates and slip-rate variabilities for all sites. The slip-rate variability is the ratio
between the fastest and slowest incremental slip rates (in mm/yr) from the available slip-rate records.
The uncertainties are given in terms of 1-σ confidence values.................................................................309
Table A.6: Notable specificities for slip-rate records available for all sites, and implications for sliprate variability. ..........................................................................................................................................311
Table B.1: Date of most recent earthquakes that occurred on the studied strike-slip faults. ...................324
Table H.1: Difference for average half spreading rates (values from Gee et al. 2000) and average
abyssal hill spacing between the eastern flank and the western flank of the EPR....................................412

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List of Figures
Figure 1.1: Schematic explanation of the elastic rebound theory of Reid (1910), showing (a) an
undeformed configuration of a fault being crossed by a fence (as in the original examination of the
displacement of the ground surface that accompanied the 1906 San Francisco earthquake), and by a
geomorphic marker (a stream), (b) the accumulation of elastic strain on each side of the fault, (c) the
release of that elastic strain during an earthquake, with the generation of seismic waves resulting in
ground shaking (hypocenter shown as a star), and (d) the rebound to an undeformed shape with both
the fence and the geomorphic marker recording the earthquake displacement. ...........................................2
Figure 1.2: Recurrence models, adapted from Shimazaki and Nakata (1980). τ1 and τ2 are final and
initial stresses of faulting, respectively.........................................................................................................3
Figure 1.3: Block diagrams showing fluvial terrace terminology and models of lower-terrace and
upper-terrace reconstructions for linking riser offsets with terrace abandonment ages for the
determination of strike-slip fault slip rates. Modified after Cowgill (2007)...............................................10
Figure 1.4: Schematic block diagram showing basic geometric features of fault-displaced terrace risers.
Diagram depicts dextral offsets with a vertical component, and showcases the geomorphological terms
used for river terraces (tread, riser, channel, active floodplain). The setting illustrated represents the
Waiohine River site, described in CHAPTER 6. ........................................................................................11
Figure 1.5: Schematic diagram showing the three-dimensional architecture of a fault system with brittle
upper crustal faults underlain by ductile shear zones below a brittle-ductile transition zone. The lower
graphs are inspired by Chester, 1995; Cole et al., 2007; and Barbot and Fialko, 2010. The frictional rate
behavior is defined by the values of frictional parameter a-b (Ruina, 1983)..............................................16

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Figure 2.1: Schematic 3D diagrams of two idealized plate boundaries that exhibit markedly different
fault-system complexity. (a) Structurally simple plate-boundary fault network, dominated by a single
fast-slipping strike-slip fault surrounded by few other active faults. (b) Structurally complex plateboundary fault network characterized by multiple fast-slipping strike-slip faults, including reverse
faults and conjugate strike-slip faults. The gray star denotes a hypothetical incremental fault slip-rate
site on the fault under consideration in our CoCo analysis. (c) Example of a structurally complex plateboundary fault network, the Marlborough fault system in South Island, New Zealand. ............................22
Figure 2.2: Active fault maps of the four studied strike-slip boundaries showing incremental slip-rate
study sites as white diamonds. (a) Topographic and tectonic map of South Island, New Zealand and the
Alpine fault system, with location of Hokuri Creek and the Marlborough fault system study sites,
indicated in inset (b). (c) Topographic and tectonic map of California and the San Andreas fault (SAF)
system, with location of Van Matre-Wallace Creek and Wrightwood study sites on the SAF, and the
Quincy site on the San Jacinto fault. (d) Topographic and tectonic map of Turkey and the North
Anatolian fault system, with location of Demir Tepe, Düzce, and Ganos Güzelköy sites. (e)
Topographic and tectonic map of the Middle East and the Dead Sea fault system, showing locations of
the northern Wadi Araba and Beteiha incremental slip rate sites. ..............................................................25
Figure 2.3: CoCo results for the Alpine-Marlborough fault system. (a) Active fault map of South
Island, New Zealand, with faults color-coded by slip rate (Litchfield et al., 2014). Circles of 100 km
radius are shown around the Hokuri Creek and Hossack Station sites to illustrate which portions of the
surrounding faults are included in the CoCo calculation for r=100 km. (b) Histogram of the CoCo
values obtained at Hokuri Creek on the southwestern Alpine fault (in blue), Hossack Station (Hope
fault), Branch River-Dunbeath (Wairau fault), Saxton River (Awatere fault), Tophouse Road (Clarence
fault) for different radii of observation (r=50, 60, 70, 80, 90, 100, 120, 150, 200 km). .............................29

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Figure 2.4: CoCo results for the San Andreas fault system. (a) Active fault map of California with
faults color-coded by slip rate (see Appendix A.2 for references). Circles of 100 km radius are shown
around the Wrightwood (red) and Van Matre-Wallace Creek (blue) sites to illustrate which surrounding
faults are included in the CoCo calculation for an area of r=100 km. (b) Histogram of the CoCo values
obtained at the Quincy, Wrightwood, and Van Matre-Wallace Creek sites for different areas of
observation (r=50, 60, 70, 80, 90, 100, 120, 150, 200 km).........................................................................32
Figure 2.5: CoCo results for the North Anatolian fault system. (a) Active fault map of Turkey with
faults color-coded by slip rate (see Table A.2 in Appendix A). Circles of 100 km radius are shown
around the Ganos, Güzelköy, and Demir Tepe sites to illustrate which surrounding faults are included
in the CoCo calculation for an area of r=100 km. (b) Histogram of the CoCo values obtained at Demir
Tepe, Düzce, and Ganos Güzelköy sites, for different radii of observation. ..............................................38
Figure 2.6: CoCo results for the Dead Sea fault system. (a) Tectonic map of the Middle East with faults
color-coded by slip rate (see Table A.3 for fault database compilation). Circles of 100 km radius are
shown around the northern Wadi Araba (blue) and Beteiha (red) sites to illustrate which surrounding
faults are included in the CoCo calculation for an area of r=100 km. (b) Histogram of the CoCo values
at these two sites, for different radii of observation. Note that because the rate of relative plate motion
along the Dead Sea transform system is much slower than that for the other three plate boundaries we
discuss, the CoCo values along the DSF are not directly comparable to those from the other three plate
boundaries. See text for discussion.............................................................................................................40
Figure 2.7: CoCo values “standardized” by respective plate rate (i.e., CoCo value divided by relative
plate-motion rate; see Table A.5) plotted against slip-rate variability (i.e., highest slip rate of the
respective record divided by slowest slip rate of the record) for all sites, for a radius of 100 km. For the
sake of visualization, the slip-rate variability is shown on a logarithmic scale. The actual unit of the
ratio CoCo/respective plate rate is km-1
, which is a rather arcane unit that we chose not to display on

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the graph. North Anatolian fault sites are indicated as: DT: Demir Tepe; Dü: Düzce; Gü: Güzelköy.
Dead Sea fault sites are indicated as AV1: Araba Valley (Niemi et al., 2001); AV2: Araba Valley
(Klinger et al., 2000); B: Beteiha. San Andreas fault system sites are shown as: VM: Van Matre-Wallace
Creek; Q: Quincy; W: Wrightwood. The Alpine-MFS sites are indicated as: HC: Hokuri Creek; HS:
Hossack Station; TR: Tophouse Road; SR: Saxton River; BR: Branch River-Dunbeath. Dashed arrows
indicate how the slip-rate variability would evolve if the current, open interseismic interval since the
last earthquake were to extend into the future. The regression line for the whole dataset is shown,
suggesting a logarithmic relationship between the standardized CoCo and the slip-rate variability..........46
Figure 2.8: Three different scenarios illustrating potential abruptness of transitions in variable
incremental slip rates and the impact of incremental slip-rate records based on relatively few data. (a)
Hypothetical, well-populated incremental slip-rate record in which adjacent incremental rates do not
change abruptly from one measurement to the next. The largest variation in adjacent incremental slip
rates in this scenario is only a factor of four times (0.75 to 3.3 mm/yr). (b) Hypothetical incremental
slip-rate record in which adjacent incremental rates do change abruptly from one increment to the next.
The largest variation in adjacent rates in this scenario is 27 times (0.6 to 16.0 mnm/yr). (c) Same overall
incremental slip-rate record as shown in (a) but populated with fewer data. The resulting incremental
rates vary by a factor of 14 times relative to variations in incremental rate in (a) of only four times........54
Figure 3.1: Schematic explanation of the rationale of the Coefficient of Complexity (CoCo) analysis
for a hypothetical fault network. The calculation of CoCo for a given radius is shown on top. The radius
over which CoCo is calculated is 100 km. Within a structurally complex fault system (numerous, and
relatively fast-slipping faults), shown to the left, the CoCo value will be higher than within a structurally
simple fault system (few or zero neighboring faults), shown to the right. The quantification of
complexity, done with the CoCo analysis, correlates with the relative steadiness of geologic slip-rate
record, as shown in our recent study (Gauriau and Dolan, 2021)...............................................................62

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Figure 3.2: Geodetic slip-deficit rate and geologic slip-rate comparisons for major strike-slip faults.
The geologic rates are shown as either averaged over a large displacement, or over a small
displacement. The data points are color-coded according to their respective values of the Coefficient of
Complexity (CoCo), standardized by the plate rate contained within a 100 km radius, as defined in
Gauriau and Dolan (2021). The strike-slip faults considered in this study are: (1) Garlock, (2) San
Andreas, Mojave segment, (3) San Andreas, Carrizo Plain segment, (4) San Jacinto, Claremont
segment, (5) Owens Valley, (6) Calico, (7) Hope, (8) Wairau, (9) Clarence, (10) Awatere, (11) Alpine,
(12) Dead Sea, Wadi Araba Valley, (13) Dead Sea, Beteiha, (14) Yammouneh, (15) Queen Charlotte,
(16) Denali, central section, (17) Denali, western section, (18) Altyn Tagh, (19) Kunlun, (20) Haiyuan,
(21) North Anatolian, Demir Tepe, (22) North Anatolian, Tahtaköprü, (23) Northern North Anatolian,
(24) East Anatolian, Pazarcık (references listed in Table 1). (a) shows all the compiled faults in the
same diagram. (b) shows all faults characterized by CoCo values that are less than 0.0016 yr-1
(referred
to as low-CoCo faults). (c) shows all faults characterized by CoCo values that are more than 0.0016 yr1
(referred to as high-CoCo faults)..............................................................................................................70
Figure 3.3: Variations of geodetic to geologic slip-rate ratios against CoCo values standardized by
plate rate over a 100 km radius. (a) Ratios of geodetic slip-deficit rate to geologic rate plotted against
CoCo. The geologic rate values are averaged over large or small displacement (as in Figure 3.2). The
numbering of the fault sites is referred to in Figure 3.2 and Table 3.1. The dashed arrows refer to a ratio
of geodetic/geologic rate that would reach infinity, with a geological rate close or equal to 0 mm/yr, if
the fault has not slipped for a long time since the MRE (see text for details). (b) Diagram showing the
dispersion of the ratio (geodetic to geologic rates) values varying with the CoCo values. The higher the
CoCo value, the more scattered the data (i.e., the farther from the 1:1 ratio line they tend to plot). The
measure of the dispersion is detailed in Appendix B. Although we cannot calculate an exact CoCo value
for the Queen Charlotte fault (15), because of our inability to include all active faults within a 100 km

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radius of the slip-rate site, we assign it a CoCo value of zero, since this fault accommodates >95 % of
the total Pacific/North America plate-motion rate (NUVEL-1A; DeMets and Dixon, 1999). ...................72
Figure 3.4: Observed modes of fault behavior, with time shown as the horizontal dimension of the
block, and with relative slip rate displayed with a color gradient. In (a), we show that whatever the time
over which its behavior is averaged, a low-CoCo fault’s slip rate is constant and thus equals its elastic
strain accumulation rate, as shown in the left hand-side, hence the same color at each point in time and
in the brittle and ductile parts of the fault. Note that we are not considering single-earthquake time
scales. In contrast, high-CoCo faults (b and c) exhibit several types of behaviors, as discussed in the
text. In (b), we illustrate a fault that has a short-term (small-displacement) geologic slip rate that is
slower than its long-term (large-displacement) rate. For this fault, the current elastic strain accumulation
(ductile shear of the ductile roots) is slower than the short-term geologic slip rate, and therefore might
be entering what we refer to as a slow mode. In (c), we show another example of a fault whose longterm geologic slip rate is faster than its short-term geologic slip rate. This fault is entering a fast mode
since its elastic strain accumulation is much faster than its short-term geologic slip rate..........................76
Figure 3.5: Schematic illustration of modes of behavior defined in this chapter, according to the CoCo
values and the geodetic/geologic rate ratio, and their potential meaning in terms of near-future hazard...88
Figure 4.1: (a) Index map of the New Zealand plate boundary. The Marlborough fault system (MFS)
consists of active dextral-slip faults that transfer slip between the Hikurangi subduction zone (to the
north) and Alpine fault (to the south). (b) Major active faults of the MFS. For location, see inset (a). (c)
Jordan Thrust-Kekerengu fault system shown on hillshaded topography. For location, see part (b).The
black box is the location of several paleoseismic trenches from which Little et al. (2018) and Morris et
al. (2022) determined a paleoearthquake chronology for the fault. Red fault traces indicate the surface
rupture of the 2016 Kaikōura earthquake in both (b) and (c). (d) Geomorphic surfaces (fluvial terrace
treads) mapped on hillshaded lidar (sourced from the LINZ Data Service and licensed for reuse under

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the CC BY 4.0 licence) at Bluff Station. The labelling scheme for these (e.g., T3B) is explained in the
text. White dots are luminescence sample pits, labeled by pit number. White crosses locate co-seismic
dextral displacement measurements from Kearse et al. (2018) that we use to obtain the average coseismic displacement at Bluff Station.........................................................................................................95
Figure 4.2: Incremental displacements of Kekerengu River alluvial terraces at Bluff Station. (a)
Current configuration of the Bluff Station site, after the 2016 Kaikōura earthquake. White dots are
sample pits, labeled by pit number. (b) Configuration prior to the 2016 Kaikōura earthquake, restored
using Digital Terrain Model acquired after the earthquake (collected by Zekkos, 2018; processed by
GNS Science). We combined the displacement measurements of three markers (a hedge row, shown
by the white arrows, and two farm tracks) documented by Kearse et al. (2018) and get an average rightlateral displacement of 9.7 ± 0.8 m. (c) Restoration of the T6/T7A riser at 20 ± 4 m. (d) Restoration of
the T3B/T6 riser at 33 +3/-4 m......................................................................................................................98
Figure 4.3: IRSL samples collected at Bluff Station, plotted by elevation and relative position.
Sediment types were logged in each pit. The sample numbers indicated in bold are samples that have
been dated. ..................................................................................................................................................99
Figure 4.4: Summary figure showing time constraints on paleoearthquake ages (Morris et al., 2020)
and terrace surface age. We display the radiocarbon age from sample location 23-02 on T6B in teal
green (see Table 4.1). Once determined, the IRSL ages will provide further constraints on plausible
slip-per-event scenarios, and specifically, the number of earthquakes possibly responsible for the 20 m
and 33 m offsets........................................................................................................................................104
Figure 4.5: Slip per event for each of the six possible scenarios. The boxes shown in blue refer to the
earthquakes for which we know the assumed cumulative displacement. The boxes in gray refer to the

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ones for which the displacement is hypothetical. We define the probability distributions of these
displacements in Appendix C. ..................................................................................................................106
Figure 4.6: Distribution of single-event displacements for the six plausible scenarios presented in
Figure 4.5. These distributions are the result of 10,000 Monte Carle simulations run using the RISeR
code (Zinke et al., 2017, 2019), with the definition of probability density functions (PDFs) of allowable
cumulative displacements and earthquake ages for each of the six scenarios. The definition of the PDFs
for the displacements and earthquake ages can be found in Appendix C.................................................108
Figure 4.7: Distribution of coefficients of variation (CoV) for the six scenarios presented in Figure 4.5.
These distributions are the result of 10,000 Monte Carle simulations run using the RISeR code (Zinke
et al., 2017, 2019), with the definition of PDFs of allowable cumulative displacements and earthquake
ages for each of the six scenarios. The definition of the PDFs for the displacements and earthquake ages
can be found in Appendix C. The gray distributions display the CoVs of slip per event excluding the
Kaikōura earthquake 9.7±0.3 m coseismic displacement. The blue distributions display the CoVs of
slip per event including the Kaikōura earthquake coseismic slip. ............................................................110
Figure 4.8: Summary figure of paleoseismic records in coastal Kaikōura ranges. This figure compares
paleoearthquakes from the Hope fault (Hatem et al., 2019), the Papatea fault (Langridge et al., 2023),
the Kekerengu fault (Morris et al., 2022), and Kaikōura coast paleoarthquakes at Kaikōura peninsula,
Waipapa Bay, Parikawa, Cape Campbell, and Mataora-Wairau lagoon (Howell and Clark, 2022, and
references therein).....................................................................................................................................113
Figure 5.1: (a) Map of active tectonics of New Zealand. The Marlborough fault system (MFS) transfers
slip between the Hikurangi subduction zone and the Alpine fault. Gray arrows show Pacific/Australia
relative convergence (e.g., Beavan et al., 2002) (b) Major active faults of the MFS. The grey squares
are the locations where the incremental slip-rate records for the Wairau, Awatere, Clarence and Hope

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faults have been developed. The white squares are the sites studied in this paper. (c) The Jordan-ThrustKekerengu fault system shown on top of topography, including the hillshaded Digital Elevation Model
from the lidar data acquired in 2016 along the fault system (sourced from the LINZ Data Service and
licensed for reuse under the CC BY 4.0 license). The two yellow arrows refer to the 9-13 km offset that
restores the Clarence River path. Fault traces colored in red refer to the surface rupture of the 2016
Kaikōura earthquake. ................................................................................................................................121
Figure 5.2: Fluvial terraces mapped on hillshaded lidar (sourced from the LINZ Data Service and
licensed for reuse under the CC BY 4.0 license) at Bluff Station. The aggradational terraces are named
after the nomenclature defined by Van Dissen et al. (2017) and their OSL sampling campaign dating
back to 1999-2000. Grey circles are the OSL sample pit locations from Van Dissen et al. (2017), white
circles are the IRSL sample pit locations performed by our team in March 2019 and March 2023.........123
Figure 5.3: Interpreted hillshaded lidar maps showing (a) unrestored configuration and (b) restored
configuration at 915 m for the offset Chaffey/Kulnine riser. The gray box masks fault-related
topography to aid visualization of the offset geomorphic features. ..........................................................125
Figure 5.4: Restoration at 860 m of the Kulnine abandoned channel offset. The gray box masks faultrelated topography to aid visualization of the offset geomorphic features. ..............................................126
Figure 5.5: Interpreted hillshaded lidar maps showing (a) unrestored configuration and (b) restored
configuration at 625 m for the offset Kulnine/Winterholme riser gully offset. The gray box masks faultrelated topography to aid visualization of the offset geomorphic features. ..............................................127
Figure 5.6: (a) 225 m restoration of Winterholme (T1BS)/T3BS offset. (b) Zoom into the crossing
between Glencoe Stream and the Kekerengu fault that describes the potential channel path taken by
Glencoe Stream at the time of this configuration. Contour lines are every 50 cm. ..................................129

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Figure 5.7: (a) Unrestored configuration of the T3BBS/T6BS riser offset. (b) Configuration restored at
33 m. The background is a 30-cm-resolution Digital Elevation Model acquired after T3BBS was
extensively bulldozed................................................................................................................................130
Figure 5.8: (a) Unrestored configuration of the T6BS/T7ABS riser offset. (b) Configuration restored at
20 m. The background is a 30-cm-resolution Digital Elevation Model acquired after T3BBS was
extensively bulldozed................................................................................................................................131
Figure 5.9: (a) Current configuration at the Bluff Station cottage. Coseismic displacement
measurements (with 2σ uncertainty) from Kearse et al. (2018) of hedge row, and two farm tracks are
shown by the black crosses. I used these three displacements to infer the average coseismic slip (9.7 ±
0.8 m) at the Bluff Station site. (b) Modern configuration before the Kaikōura earthquake, restored at
9.7 m (the white arrows are pointing at edge of displaced hedge)............................................................132
Figure 5.10: Schematic diagram of IRSL sample locations at Bluff Station, and their relative elevation.
The horizontal distances are arbitrary. The morphostratigraphic profiles located above the elevation
line refer to sampling sites located upstream of the fault, whereas profiles located below the elevation
line refer to sampling locations downstream of the fault. The sample numbers indicated in bold are
samples that have been dated....................................................................................................................133
Figure 5.11: Fluvial terraces mapped on hillshaded lidar (sourced from the LINZ Data Service and
licensed for reuse under the CC BY 4.0 license) at Shag Bend, with inset zoomed into the T5SB lower
terraces. The white dots indicate the IRSL sampling locations. The white hexagon marks the
radiocarbon sample collected below the T4SB surface. The black crosses are the Kaikōura earthquake
coseismic displacements (with 2σ uncertainty) measured by Kearse et al. (2018) that I use to infer the
average coseismic displacement at the Shag Bend site.............................................................................136

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Figure 5.12: (a) Unrestored configuration at Shag Bend. (b) Restoration of the bedrock/T3SB contact
at 78 m. This is likely a minimum restoration, because of the presence of a landslide potentially hiding
the contact between the bedrock and T3SB. The gray box masks fault-related topography to aid
visualization of the offset geomorphic features. .......................................................................................137
Figure 5.13: (a) Unrestored configuration at Shag Bend. (b) Restoration of T3SB/T4SB riser at 41 m.
The gray box masks fault-related topography to aid visualization of the offset geomorphic features. ....138
Figure 5.14: (a) Unrestored configuration at Shag Bend. (b) Minimum restored configuration of the
T4SB/T5SB riser offset at 22 m. The gray box masks fault-related topography to aid visualization of the
offset geomorphic features........................................................................................................................139
Figure 5.15: Schematic diagram of IRSL sample locations visited at Shag Bend, and their relative
elevation. The horizontal distances are arbitrary. The morphostratigraphic profiles located above the
elevation line refer to sampling sites located upstream of the fault, whereas profiles located below the
elevation line refer to sampling locations downstream of the fault. The sample numbers indicated in
bold are samples that have been dated. .....................................................................................................140
Figure 5.16: Hillshaded lidar map (sourced from the LINZ Data Service and licensed for reuse under
the CC BY 4.0 license) showing the Black Hut site, with interpreted terraces. The white dots indicate
IRSL sampling locations. The black crosses are the Kaikōura earthquake coseismic displacements
measured by Kearse et al. (2018) that I use to infer the average coseismic displacement at the Black
Hut site......................................................................................................................................................142
Figure 5.17: (a) Unrestored configuration at Black Hut, with 2-m contour lines highlighting the channel
located along TaBH/TbBH riser. (b) Restoration of TaBH/TbBH riser offset at 310 m..................................143

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Figure 5.18: Relative elevations of terraces at the Black Hutt site, and luminescence sample locations.
..................................................................................................................................................................144
Figure 5.19: Geomorphology of McLean stream site mapped on hillshaded lidar (sourced from
the LINZ Data Service and licensed for reuse under the CC BY 4.0 license). White circles are the IRSL
sample locations visited in March 2019 and March 2023. White crosses are measurements (in m, with
2σ uncertainty) from Kearse et al. (2018) of coseismic dextral displacement of the Kaikōura rupture. In
(a), the map displays the two-stranded Kekerengu fault system, with the southern strand becoming the
Jordan thrust. The Fidget fault is indicated with dashed red line, and further continues to the southwest
of the displayed map. (b) is a zoom into the white rectangle displayed in (a), showing the displaced
lower terraces at McLean Stream..............................................................................................................147
Figure 5.20: (a) Unrestored configuration at McLean Stream site. (b) 480 m offset that restores the
main drainages across the southern strand................................................................................................149
Figure 5.21: (a) Unrestored configuration at McLean Stream site. (b) 380 m offset that restores the
canyons and drainages across the northern strand. ...................................................................................150
Figure 5.22: (a) Unrestored configuration at McLean stream, east of the T1 terraces. (b) Double offset
restored on the two strands of the Kekerengu fault (total of 176 m). .......................................................151
Figure 5.23: (a) Unrestored configuration of fill cut terraces at McLean Stream, with 50-cm contour
intervals. (b) 20 m offset restores T2ML/T3AML riser. The gray area masks fault-related topography to
aid visualization of the offset geomorphic features. .................................................................................152
Figure 5.24: (a) Unrestored configuration of fill cut terraces at McLean Stream, with interpreted
terraces, with 50-cm contour intervals. (b) 12 m offset restores both T3ML/T4ML and T4ML/T6ML offsets.
The gray area masks fault-related topography to aid visualization of the offset geomorphic features.....153

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Figure 5.25: Morphostratigraphic diagram of sample pits and ages relative to elevation at McLean
Stream. Horizontal scale is arbitrary. The morphostratigraphic profiles displayed below the elevation
profile refer to sampling sites located south of the southern strand. The ones displayed above the
elevation profile refer to sampling sites located north of the northern strand. The sample numbers
indicated in bold are samples that have been dated. .................................................................................154
Figure 6.1: (a) Tectonic context of New Zealand. MFS: Marlborough Fault System; NIDFB: North
Island Dextral Fault Belt. Arrows show Pacific/Australia relative convergence (e.g., Beavan et al.,
2002). (b) Major faults of northern South Island and southern North Island. The right-lateral
Kekerengu-Needles fault system directly connects to the Wairarapa fault. The brown lines represent
the depth of the Hikurangi slab interface (after Williams et al., 2013).....................................................163
Figure 6.2: Geomorphology at the Waiohine River site shown by hillshaded lidar. Inset (a) shows the
location of the Waiohine River and the trace of the right-lateral Wairarapa fault. (b) shows the mapped
fluvial terraces and location of IRSL sample pits. Terrace treads are labeled T1 to T9. The two offset
channels are labeled Ch1 and Ch2. ...........................................................................................................165
Figure 6.3: Offset markers restored to their preferred values for (a) T1/T2 riser, (b) T2/T3, Channel 1
and T3/T4 risers, (c) Riser down to T8, and (d) T8/T9 riser and Channel 2. The arrows indicate the
primary features restored, along with the dashed lines that represent the thalweg trend of the channels.
The restoration presented here are the preferred values, indicated in each inset. The shaded sections
mask fault-related topography to aid visualization of the offset geomorphic features. ............................167
Figure 6.4: Elevation profiles across the fault used to measure vertical offsets of the displaced terraces.
(a) and (g) are topographic maps displaying the drawing of the profiles, with the red sections referring
to the red sections of the displayed profiles (in insets b to f, and h to j), which are used to estimate the

xxvii
gradient of the paleo-floodplain. Calculation of the uncertainties for the vertical component of the fault
displacement is described in Appendix E. Errors are 2σ intervals............................................................171
Figure 6.5: Morphostratigraphic diagram of sample pits and ages relative to elevation and respective
geomorphic surfaces. Sediment types are illustrated in relationship with the depth location of IRSL
samples......................................................................................................................................................176
Figure 6.6: Made-up incremental slip-rate record for the Wairarapa fault at Waiohine River. The ages
used in this plot are fake, apart from the age of the MRE and the estimate of the Waiohine surface age
(used here as 12±1 ka). The displacements reported are the ones determined in this study. The black
lines refer to the slip-rate functions between two successive Monte Carlo sampling of displacements
and ages (code from Zinke et al., 2017). The slip-rate values and the age/displacement values (within
black rectangles) are shown with 95% uncertainties. ...............................................................................178
Figure 7.1: Tectonic setting of southern California, showing (a) the main active faults of southern
California, including the San Andreas fault system and the Eastern California shear zone; and (b) map
of the Elsinore fault system. The background is the 30-m digital elevation model of California (Anon,
2000). ........................................................................................................................................................189
Figure 7.2: (a) Geomorphology of the northern Elsinore fault around Glen Ivy (lidar data from NOAA
2003). (b) Geomorphological interpretation of the Glen Eden study site using terrace designations of
Millman (1988). The background image uses an orthorectified 1953 air photograph (UCSB Air
Photograph Database; Flight AXM_1953B, Frame 13K-7) that was taken prior to development. White
dots are sample locations. .........................................................................................................................191
Figure 7.3: Glen Eden site geomorphology. The background layer is a 1953 air photograph (UCSB Air
photo Database, Flight AXM_1953B, Frame 13K-7), and the geomorphological interpretation is
inspired from the original mapping of Millman (1988). (a) Configuration at 44±5 m of back-slip, which

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restores the inner edge of the Qf3 terrace preserved on both sides of the fault (black dashed line and
white arrows). The sample pit location is indicated, and the related ages will provide the age for that
offset, using a lower-terrace reconstruction (see text for explanations). (b) Configuration at 190+25/-15
m of back-slip that restores the inner edge of the Qf5 terrace deposits that cap the local top of the incised
canyon wall, north-east of the fault, with the linear eroded wall of Indian Canyon upstream of the fault.
Location of samples GE22-05 to GE22-08 are indicated. Once obtained, their age will provide the age
of that offset. .............................................................................................................................................193
Figure 7.4: Three-dimensional schematic diagram showing relationship between the fluvial terraces
studied at the Glen Eden site and the locations of IRSL sample locations on surfaces Qf3 and Qf5.......195
Figure 8.1: Overview of the Hokuri Creek (HC) paleoseismic site and data (modified after Berryman
et al., 2012b). (a) Location of the HC paleoseismic site within the Alpine fault system, New Zealand
(Langridge et al., 2016). (b) Ages of 24 surface-rupturing earthquakes on the Alpine Fault at HC.
Intervals are the 95% brackets inferred from radiocarbon ages. The red cross refers to the most recent
event (MRE) in 1717 C.E. (De Pascale and Langridge, 2012). (c) Recurrence time intervals, with
probability density functions. (d) Slip deficit of the HC record through time, with inferred average slip
per event of 7.5 ± 2.5 m, and its relation to the time-predictable (upper bound) and slip-predictable
(lower bound) models, as defined by Shimazaki and Nakata (1980). (e) Plot of the time to succeeding
event against the time since preceding event for the HC record...............................................................201
Figure 8.2: Representation of the Alpine fault zone model with a reference map (a) indicating the
location of the Hokuri Creek site. (b) Distribution of the rate dependence at steady state b-a (kept at -
4·10-3
in the velocity-strengthening zone, and at +4·10-3
in the velocity-weakening zone, for all models
in this study) and parameters a (kept at 10-2
for all models) and b (varied for different values of Rb)
throughout the total modeled length of the fault. (c) Example plot of the cumulative slip along the fault
for Ru=95, Rb=0.286, and μ0=0.50. The cumulative slip is plotted for a total of 17 events in the selected

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simulation, between years 12,285 and 13,714 (within a whole 20,000-year-long simulation). A proxy
for the location of HC is shown and the vertical line above it shows where the sampling is done. The
orange isochrons feature cumulative coseismic slip every 20 seconds, and the gray contours show slip
isochrons every 20 years in the interseismic periods................................................................................206
Figure 8.3: Overview of the periodicity styles obtained for σ =13 MPa in the parameter spaces formed
by Ru and μ0 and by Ru and Rb at the sampling point (marked HC in Figure 8.2c). In (a) and (b), each
color refers to a type of earthquake repeat time periodicity, and insets (c) to (h) show the evolution of
slip deficit through time for the last 8,000 years of the whole simulation. (f) refers to the period-5 model
that fits well the HC data, using both ranking criteria we use in Figure 8.5c and Figure 8.5d. (h) refers
to the chaotic model, also shown in Figure 8.2c, Figure 8.5e, and Figure 8.5f, that fits the HC data well,
using both ranking criteria. .......................................................................................................................209
Figure 8.4: Results of fitting of best sequences of 23 recurrence intervals to the Hokuri Creek
paleoseismic data (a) for the parameter spaces formed by Ru and μ0 and (b) by Ru and Rb. (a) and (b)
display the highest number of successive recurrence intervals that fall within the 95% confidence
intervals of the HC paleoseismic time recurrence intervals for each simulation. The best results are
obtained for simulations with a period-2 earthquake recurrence behavior (shown as black squares), and
are displayed in insets (c) to (f). (c) shows the recurrence time sequence of the selected interval for the
period-2 simulation of parameters Ru=80, μ0=0.55, Rb=0.286, compared with the HC data. For this
selected sequence, the CoV is 0.264 and the average recurrence time is 277.9 yr. (d) shows the entire
20,000-year-long simulation for the preferred chaotic model and the selected interval in light of the HC
data. (e) and (f) show the same series of plots for period-2 simulation of parameters Ru=50, μ0=0.50,
Rb=0.50. This selected sequence is characterized by a CoV of 0.322 and an average recurrence time
interval of 278.0 yr....................................................................................................................................211

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Figure 8.5: Additional method for ranking the selected sequence of 23 intervals (Figure 8.4), with the
use of p-values of the related tested null-hypothesis H0: “The HC record and the selected simulated
record were drawn from the same distribution”, using a two-sample Kolmogorov-Smirnov test. The pvalues are displayed in (a) for the parameter space formed by Ru and μ0 and in (b) for the parameter
space formed by Ru and Rb. The highest p-value is obtained for the simulation of parameters Ru=90,
Rb=0.286, and μ0=0.60, characterized by a period-5 recurrence time behavior, which also has a good fit
with the HC data, using the ranking criterion of Figure 4. Insets (c) and (d) show the recurrence time
sequence of the selected interval for this period-5 model, compared to the HC record. For this sequence,
the CoV is 0.333 and the average recurrence time is 307.4 yr. The other simulation that gathers a high
p-value and a large number of successive recurrence times falling within 95% uncertainties of the HC
record is the chaotic simulation of parameters: Ru=95, Rb=0.286, and μ0=0.50. (e) and (f) show the
recurrence time sequence of the selected interval for this chaotic model, compared to the HC record.
For this selected sequence, CoV=0.380, the average repeat recurrence time is 277.4 yr. ........................213
Figure A.1: Incremental slip-rate data used for the Alpine-Marlborough fault system in New Zealand.255
Figure A.2: Incremental slip-rate data used for the San Andreas fault system in California...................256
Figure A.3: Incremental slip-rate data used for the North Anatolian fault system in Turkey. ................256
Figure A.4: Incremental slip-rate data used for the Dead Sea fault system in Turkey. ...........................257
Figure A.5: CoCo values “standardized” by respective plate rate (i.e., CoCo value divided by relative
plate-motion rate – unit is km-1
) plotted against slip-rate variability (i.e., highest slip rate of the
respective record divided by slowest slip rate of the record) for all sites, for a radius of 100 km. Fault
site acronyms are the same as the ones in Figure 2.7. The number of slip-rate increments in the
published records are shown by colors, and the durations of the entire slip-rate record are shown by the
size of the dots. .........................................................................................................................................258

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Figure A.6: CoCo values “standardized” by respective plate rate (i.e., CoCo value divided by relative
plate-motion rate – unit is km-1
) plotted against slip-rate variability (i.e., highest slip rate of the
respective record divided by slowest slip rate of the record) for all sites, for the three largest explored
radii (100, 150, 200 km). The durations of the entire slip-rate record are shown by the size of the dots.
Fault site acronyms are the same as the ones in Figure 2.7. .....................................................................259
Figure B.1: Geodetic rate and geological slip rate comparisons for selected strike-slip faults. (a) and
(b) for low-CoCo faults, (c) and (d) for high-CoCo faults. The dark line and the two faded lines show
the linear fits with 67% confidence intervals with slopes indicated on each plot.....................................323
Figure B.2: Illustration of the measurement of data dispersion shown in Figure 3.3b (CHAPTER 3). ..327
Figure C.1: Hillshade map (1-m resolution lidar data; sourced from the LINZ Data Service and
licensed for reuse under the CC BY 4.0 licence) of the Bluff Station site................................................330
Figure C.2: Co-seismic dextral measurements (in meters) at the Bluff Station site from Kearse et al.
(2018) on top of Digital Terrain Model (acquired after the earthquake by Zekkos, 2018; processed by
GNS Science). The locations of the measurements are marked by red arrows. The displaced features
are indicated as well as the uncertainties on the displacement measurements (2σ). For the coseismic
displacement at Bluff Station, we use the average of the measurements from the two offset farm tracks
and the offset hedge row. We did not use the measurement from the cottage foundation, which likely
does not directly inform on the actual displacement at this location. .......................................................331
Figure C.3: Restorations of T3B/T6 riser. White shaded areas hide the fault zone to allow better
visualization of restoration........................................................................................................................332
Figure C.4: Restorations of T6/T7A riser................................................................................................333

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Figure C.5: (a) Context for the location where charcoal samples were found at pit 23-02 (eastern edge
of terrace T6B). (b) Close photograph showing the sampling location for the charcoal samples, prior to
sampling. (c) and (d) are photographs taken from a microscope, showing the size and shape of the
charcoal bits. .............................................................................................................................................334
Figure C.6: Photographs of each sampling pit at the Bluff Station site, with location of each IRSL
sample. ......................................................................................................................................................335
Figure C.7: Maps illustrating the steps for the correlation of terrace T7 across the fault........................337
Figure C.8: Probability density functions of cumulative displacements entered as inputs in RISeR code,
for Scenario A...........................................................................................................................................340
Figure C.9: Probability density functions of cumulative displacements entered as inputs in RISeR code,
for Scenario B. ..........................................................................................................................................341
Figure C.10: Probability density functions of cumulative displacements entered as inputs in RISeR
code, for Scenario C..................................................................................................................................342
Figure C.11: Probability density functions of cumulative displacements entered as inputs in RISeR
code, for Scenario D. ................................................................................................................................343
Figure C.12: Probability density functions of cumulative displacements entered as inputs in RISeR
code, for Scenario E..................................................................................................................................344
Figure C.13: Probability density functions of cumulative displacements entered as inputs in RISeR
code, for Scenario F. .................................................................................................................................345
Figure D.1: Large restoration offset of the Clarence River path along the Kekerengu fault. The offset
is 9 to 13 km. Map background is from Google Earth. Inset map shows a slope map of the Marlborough

xxxiii
fault system and highlights the two “elbows” formed by the Clarence river (highlighted by the white
arrows). Active faults are indicated in black (Langridge et al., 2016). AM: Awatere Mountains; IKR:
Inland Kaikoura Range; SKR: Seaward Kaikoura Range; HH: Hundalee Hills. The main geomorphic
features restored by this configuration are indicated by white arrows......................................................347
Figure D.2: Restorations of Chaffey/Kulnine riser. Background maps include lidar and topographic
map (contour lines every meter). ..............................................................................................................348
Figure D.3: Restorations of Kulnine abandoned channel. Background maps include lidar and
topographic map (contour lines every meter). The gray area masks fault-related topography to aid
visualization of the offset geomorphic features. .......................................................................................349
Figure D.4: Restorations of Kulnine/Winterholme terrace riser. Background maps include lidar and
topographic map (contour lines every meter). The gray boxes mask fault-related topography to aid
visualization of the offset geomorphic features. .......................................................................................350
Figure D.5: Restorations of Winterholme (T1BS)/T3BS terrace riser. Background maps include lidar and
topographic map (contour lines every meter). ..........................................................................................351
Figure D.6: Restorations for the offset contact bedrock/T3SB. Background maps include lidar,
topographic map (contour lines every 50 cm) and slope map highlighting the steep areas (i.e., risers),
and smooth areas (i.e., terrace treads).......................................................................................................352
Figure D.7: Restorations for T3SB/T4SB riser. Background maps include lidar, topographic map
(contour lines every 50 cm) and slope map highlighting the steep areas (i.e., risers), and smooth areas
(i.e., terrace treads). The black boxes mask fault-related topography to aid visualization of the offset
geomorphic features..................................................................................................................................353

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Figure D.8: Restorations for T4SB/T5SB. Background maps include lidar, topographic map (contour
lines every 30 cm) and slope map highlighting the steep areas (i.e., risers), and smooth areas (i.e., terrace
treads)........................................................................................................................................................354
Figure D.9: Restorations for the Black Hut offset. Background maps include lidar, topographic map
(contour lines every 2 m) and slope map highlighting the steep areas (i.e., risers), and smooth areas
(i.e., terrace treads)....................................................................................................................................355
Figure D.10: Restorations for the double offset at McLean Stream. Background maps include lidar,
topographic map (contour lines every meter) and slope map highlighting the steep areas (i.e., risers),
and smooth areas (i.e., terrace treads).......................................................................................................356
Figure D.11: Restorations for the T2ML/T3ML offset at McLean Stream. Background maps include lidar,
topographic map (contour lines every 50 cm) and slope map highlighting the steep areas (i.e., risers),
and smooth areas (i.e., terrace treads).......................................................................................................357
Figure D.12: Restorations for T3ML/T4ML and T4ML/T6ML offset at McLean Stream. Background maps
include lidar, topographic map (contour lines every 50 cm) and slope map highlighting the steep areas
(i.e., risers), and smooth areas (i.e., terrace treads)...................................................................................358
Figure D.13: Determination of the T3ML/T4ML riser displacement prior to the Kaikōura earthquake.
Geomorphic evidence based on air photos taken with Google Earth at different times: before the
construction of the dirt road that crosses the T3ML/T4ML riser near the fault trace, after the construction
of that road, and after the 2016 Kaikōura earthquake. This set of photos provides the evidence for an
offset that displaces both T3ML/T4ML riser and T4ML/T5ML riser, prior the Kaikōura earthquake. It also
shows that the construction of the road did not change the aspect of the T3ML/T4ML riser directly north
of the fault.................................................................................................................................................359

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Figure E.1: Restorations of T1/T2 offset, including minimum, preferred and maximum versions.
Background maps include lidar, topographic map (contour lines every 10 cm) and interpreted landscape
with topography (contour lines every 50 cm). The gray areas mask fault-related topography to aid
visualization of the offset geomorphic features. .......................................................................................361
Figure E.2: Restorations of T2/T3 riser offset, including minimum, preferred and maximum versions.
Background maps include lidar, topographic map (contour lines every 10 cm) and interpreted landscape
with topography (contour lines every 50 cm). The gray areas mask fault-related topography to aid
visualization of the offset geomorphic features. .......................................................................................362
Figure E.3: Restorations of channel 1 offset, including minimum, preferred and maximum versions.
Background maps include lidar, topographic map (contour lines every 10 cm) and slope map
highlighting the steep areas (i.e., risers), and smooth areas (i.e., terrace treads). The gray areas mask
fault-related topography to aid visualization of the offset geomorphic features.......................................363
Figure E.4: Restorations of T3/T4 riser offset, including minimum, preferred and maximum versions.
Background maps include lidar, topographic map (contour lines every 10 cm) and slope map
highlighting the steep areas (i.e., risers), and smooth areas (i.e., terrace treads). We use a different color
scheme for the slope map than for Figure E.3, to better highlight the morphology of riser T3/T4. The
gray areas mask fault-related topography to aid visualization of the offset geomorphic features............364
Figure E.5: Restorations of T4/T8 riser offset, including minimum, preferred and maximum versions.
Background maps include lidar, topographic map (contour lines every 10 cm) and slope map
highlighting the steep areas (i.e., risers), and smooth areas (i.e., terrace treads). The gray areas mask
fault-related topography to aid visualization of the offset geomorphic features.......................................365
Figure E.6: Restorations of T8/T9 riser offset, including minimum, preferred and maximum versions,
using the upper edge of the riser as a marker. Background maps include lidar, topographic map (contour

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lines every 10 cm), interpreted landscape with topography (contour lines every 50 cm), aspect map
(which enhances the various directions of the slopes derived from the DTM), and slope map. The black
boxes mask fault-related topography to aid visualization of the offset geomorphic features...................366
Figure E.7: Restorations of Ch2 offset, including minimum, preferred and maximum versions.
Background maps include lidar, topographic map (contour lines every 10 cm), interpreted landscape
with topography (contour lines every 50 cm), aspect map (which enhances the various directions of the
slopes derived from the DTM), and Strahler order map (which highlights the location of drainages and
helps determine the preferential direction of water, and therefore the exact position of the deepest part
of potential thalwegs, such as for the characterization of Channels 1 and 2). The black boxes mask faultrelated topography to aid visualization of the offset geomorphic features. ..............................................367
Figure E.8: Three elevation profiles traced on terrace T1 (Waiohine surface) used to obtain the vertical
component of the offset whose age would be defined by the abandonment age of T1. The red sections
are used to determine the trend of the gradient north and south of the fault, to then determine the range
of vertical component of offset. North of the fault, the gradient of the paleo-floodplain does not take
into account a channel incised into T1. Profile SS’ is located 30 m away from the T1/T2 riser edge.
Profile TT’ is located 20 m away from the T1/T2 riser edge. Profile UU’ is located 10 m away from the
T1/T2 riser edge. Averaging the three vertical measurements, we obtain 18.80 ± 0.10 m.......................368
Figure E.9: Schematic explanation of calculation of vertical displacements ..........................................370
Figure E.10: Photographs of sample pits and corresponding sample numbers. ......................................371
Figure F.1: Air photographs of Glen Eden taken at three different periods (1953, 1967, 1981),
highlighting the progressive land development. .......................................................................................373

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Figure F.2: Digital elevation models (DTM) from 2019 lidar data (Anon, 2019) and from stereoscopic
analysis of 1981 air photos. ......................................................................................................................374
Figure F.3: Offset restorations for terrace riser Qf5/Qf3.........................................................................375
Figure F.4: Offset restorations for terrace riser Qf10+/Qf5.....................................................................376
Figure F.5: Photographs of each sampling location at the Glen Eden site, with location of each IRSL
sample. ......................................................................................................................................................377
Figure G.1: Illustration of the explored parameter spaces, in a 3D diagram. ..........................................378
Figure G.2: Overview of the periodicity styles obtained for different values of at the sampling
location (HC), and on the entire fault (left side). The first two rows show graphs of =13 MPa presented
in the main text for HC, with the first row presenting the results for the parameter space {Ru; Rb} with
a fixed value of μ0=0.50, and the second presenting the results for the parameter space {Ru; μ0} with
fixed value of Rb=0.286. The two last rows show Ru versus μ0 at =14 MPa and 16.4 MPa. This figure
emphasizes the importance of sampling of the fault and the various periodicity style of recurrence
patterns according to different values of the non-dimensional parameters...............................................381
Figure G.3: Bifurcation diagrams showing periodicity of earthquake recurrence for all the simulations
we ran for =13 MPa, as presented in Figure 3 in the main text. We show the different values of
recurrence time intervals obtained at each value of Ru numbers with fixed Rb=0.286 and varying μ0
(from 0.30 to 0.60 with 0.05 increments) in the first two rows, and fixed μ0=0.50 and varying Rb (from
0.35 to 0.70 with 0.05 increments) in the two bottom rows. The size of the markers refers to the relative
size of the events, using the seismic moment. ..........................................................................................382
Figure G.4: Results of fitting of best sequences of 23 time recurrence intervals to the Hokuri Creek
paleoseismic data, for =13 MPa, at the sampling location (HC), for different values of Ru and μ0, at

xxxviii
fixed Rb=0.286. The recurrence interval time sequences obtained in each simulation are filtered to suit
paleoseismic hypotheses explained above. Best 23-event sequences of each filtered simulation are
selected based on three different ways of ranking the simulations, as explained above. For (a), (d) and
(g), we use color gradients that refer to the value of the RMSE, the number of events within 95%
confidence intervals, and the number of successive events that fall within 95% confidence intervals,
respectively. (b) and (c) show the results in light of the HC data for one of the best fitting simulations
according to the first way of ranking the results (RMSE) for μ0 versus Ru. The 21st interval of the
simulation for {Ru=60, μ0=0.60} gives one of the lowest possible RMSE, a total of seventeen events
within the 95% confidence interval, and a maximum of four events in a row that fall within the 95%
confidence intervals. The earthquake recurrence behavior is period-11. Insets (e) and (f) present the
results in light of the HC data for the best fitting simulation using the second way of ranking the results.
The maximum number of events that fall within the 95% confidence intervals is 19 and is reached for
{Ru=95, μ0=0.50}, one of our two best fitting models (see CHAPTER 8), which refers to a chaotic
behavior. Insets (h) and (i) show the results for one of the best fitting simulations using the third way
of ranking, for simulation {Ru=80, μ0=0.50}, characterized by a period-2 recurrence behavior..............387
Figure G.5: Results of fitting of best sequences of 23 recurrence intervals to the Hokuri Creek
paleoseismic data, for =13 MPa, at sampling point HC, for varying values of Rb at fixed μ0=0.50. For
(a), (d) and (g), we use color gradients that refer to the value of the RMSE, the number of events within
the 95% confidence intervals, and the number of successive events that fall within the 95% confidence
intervals, respectively. Insets (b) and (c) show the best result based on the ranking criterion of the
lowest RMSE for {Ru=70, Rb=0.35}. The earthquake recurrence behavior is period-22. Sixteen events
in total and eight successive recurrence time intervals fall within the 95% error bars of the data. Insets
(e) and (f) show the second-best result for the second way of ranking: for {Ru=50, Rb=0.50}, 18
recurrence times in total and 13 successive recurrence times fall within the 95% confidence intervals.
The earthquake recurrence behavior is period-2 in this case. The best result for this ranking criterion is

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already shown in Figure G.4. Insets (h) and (i) show one good result for the third way of ranking, for
which 11 successive recurrence times and 15 recurrence times in total fall within the 95% confidence
intervals. The earthquake recurrence behavior is chaotic in this case. .....................................................388
Figure G.6: Colored matrices showing average slip per event in parameter space {Ru, μ0} of the bestfitting 23-time recurrence intervals for the three ranking methods, at the location representing Hokuri
Creek. (a) is for the best sequences using RMSE, (b) is for best sequences using number of events
within the 95% confidence intervals, and (c) is for best sequences using maximum number of successive
events within the 95% confidence intervals. Black stars refer to average values of slip corresponding to
7.5 ± 2.5 m (arbitrary uncertainty used in Figure 8.1 to build the slip deficit plot of the Hokuri Creek
record).......................................................................................................................................................389
Figure G.7: Colored matrices showing average earthquake repeat time in parameter space {Ru, μ0} of
the best-fitting 23-time recurrence intervals for the three ranking methods, at the location representing
Hokuri Creek. (a) is for the best sequences using RMSE, (b) is for best sequences using number of
events within the 95% confidence intervals, and (c) is for best sequences using maximum number of
successive events within the 95% confidence intervals. Black stars refer to average values of recurrence
time interval between 261 and 397 years (the 1σ interval of the HC mean recurrence interval, according
to (Berryman et al., 2012b).......................................................................................................................390
Figure G.8: Colored matrices showing CoV of best-fitting 23-time recurrence intervals in parameter
space {Ru, μ0} for the three ranking methods, at the location representing Hokuri Creek. (a) is for the
best sequences using RMSE, (b) is for best sequences using number of events within the 95%
confidence intervals, and (c) is for best sequences using maximum number of successive events within
the 95% confidence intervals. Black stars refer to values of CoV between 0.2 and 0.4...........................391

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Figure G.9: Colored matrices showing average slip per event in parameter space {Ru, Rb} of the bestfitting 23-time recurrence intervals for the three ranking methods, at the location representing Hokuri
Creek. (a) is for the best sequences using RMSE, (b) is for best sequences using number of events
within the 95% confidence intervals, and (c) is for best sequences using maximum number of successive
events within the 95% confidence intervals. Black stars refer to average values of slip corresponding to
7.5 ± 2.5 m. ...............................................................................................................................................392
Figure G.10: Colored matrices showing average earthquake repeat time in parameter space {Ru, Rb}
of the best-fitting 23-time recurrence intervals for the three ranking methods, at the location
representing Hokuri Creek. (a) is for the best sequences using RMSE, (b) is for best sequences using
number of events within the 95% confidence intervals, and (c) is for best sequences using maximum
number of successive events within the 95% confidence intervals. Black stars refer to average values
of recurrence time interval between 261 and 397 years............................................................................393
Figure G.11: Colored matrices showing CoV of best-fitting 23-time recurrence intervals in parameter
space {Ru, Rb} for the for the three ranking methods, at the location representing Hokuri Creek. (a) is
for the best sequences using RMSE, (b) is for best sequences using number of events within the 95%
confidence intervals, and (c) is for best sequences using maximum number of successive events within
the 95% confidence intervals. Black tars refer to values of CoV between 0.2 and 0.4. ...........................394
Figure G.12: Representation of the Alpine fault zone model with reference map in inset (a) and plot
of the cumulative for the following parameters: =13 MPa, Ru=90, Rb=0.286 and μ0=0.60, in inset (b).
The cumulative slip is plotted for a total of 25 events in this period-5 simulation, between years 1,689
and 3,866 (within a whole 20,000-year-long simulation). Location of Hokuri Creek (HC) is shown as
the black vertical line. The orange isochrons feature cumulative coseismic slip every 20 seconds. The
gray contours show slip isochrons every 10 years....................................................................................395

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Figure G.13: Seismic cycle simulation (20,000 years) represented by the slip velocity for the following
parameters: =13 MPa, Ru=95, Rb=0.286 and μ0=0.50. The x- and y-axes represent distance along the
fault and adaptive time steps, respectively. The area between the blue dashed lines is the velocityweakening domain. The white vertical line refers to the proxy of Hokuri Creek's location. This
highlights two kinds of rupture styles: full, unilateral ruptures (break the entire fault) and partial
ruptures (break less than ~100 km)...........................................................................................................396
Figure G.14: Seismic cycle simulation (20,000 years) represented by the slip velocity for the following
parameters: =13 MPa, Ru=90, Rb=0.286 and μ0=0.60. The x- and y-axes represent distance along the
fault and adaptive time steps, respectively. The area between the blue dashed lines is the velocityweakening domain. The white vertical line refers to the proxy of Hokuri Creek's location. This
highlights two kinds of rupture styles: full, unilateral ruptures (break the entire fault) and partial
ruptures (break less than ~100 km)...........................................................................................................398
Figure G.15: Cumulative frequency distribution (Nc) of seismic moments for the simulations using
following parameters: (a) =13 MPa, Ru=90, Rb=0.286 and μ0=0.60, and (b) =13 MPa, Ru=95,
Rb=0.286 and μ0=0.50...............................................................................................................................399
Figure H.1: Area of study and location of the east-west profiles across the East Pacific Rise (EPR). (a)
The names of the profiles are indicated on their respective edge. Profiles named in red are the ones
originally discussed in Gee et al. (2000). (b) Zoomed area on the Western part of profiles P8E, P9E and
P10E, with the digitized segments showing summits of abyssal hills. Figure made with GeoMapApp
(www.geomapapp.org). ............................................................................................................................402
Figure H.2: Comparisons for two pairs of profiles on the eastern side of the EPR: P12E-P13E located
3.44 km apart and P10E-P16E, 70.92k m apart. The dots represent the intersections between the profiles

xlii
and the digitized abyssal hills segments, showing the consistency of their digitization with the
bathymetric data........................................................................................................................................403
Figure H.3: Cross-correlations for six pairs of profiles (normalized). On the right side: three pairs of
eastern profiles P20E-P19E, P20E-P17E and P20E-P6E sorted in an increasing distance between the
two profiles of the pair. On the left side: the equivalent Western profiles pairs. The bathymetry is shown
for normalized values. Lag 40 refers to a null offset; the left side (between 0 and 40) is for negative
offsets (the second profile in yellow is lagged to the left with the first profile in blue fixed) while the
right side (between 40 and 80) is for positive offsets (the second profile is lagged to the right). ............405
Figure H.4: Plots showing the negative correlation between (1) the distance between 2 profiles and (2)
the correlation coefficients between these profiles for the best cross-correlation lag fit – for Eastern and
Western profiles. The Spearman’s and Pearson’s correlation coefficients of the plots are shown
(respectively by r and ρ) as well as the p-values for the test of the null hypothesis. ................................406
Figure H.5: Results of the hierarchical clustering: dendrograms and spatial clustering. (a)
Dendrograms obtained for the Eastern and the Western profiles via hierarchical clustering. The
horizontal yellow line stands for the threshold for which the clusters are defined in the map below. This
threshold is arbitrarily set for a total Euclidean distance of 4000. The sample indices stand for the
different profiles. (b) Spatial representation of the different clusters. Five clusters are defined for the
Western side (W-a to W-e), whereas four (E-a to E-d) can be drawn for the East. Names of clusters and
of the nodes (e1, w1, etc.) between clusters are indicated on both A and B for an easier reading. Figure
made with GeoMapApp (www.geomapapp.org)......................................................................................410
Figure H.6: Spacing between consecutive abyssal hills plotted against distance from the EPR for the
pairs of profiles P4W-P4aW and P4bE-P4cE (see Figure H.1 for location).............................................411

xliii
Figure H.7: Results of Dynamic Time Warping (DTW) for the pair of profiles P4dW-P4eW and link
with the bathymetry. (a) to (c) Abyssal hills spacing plotted against distance from the EPR for profiles
P4dW and P4eW; DTW path between the two signals; Detrended DTW path. (d) Spatial description of
the spreading rates variations. The white lines show the abyssal hills crest that are curved in regard of
the difference of spreading rate between the two profiles. The letters A to E refer to intervals of different
relative spreading rates (based on the comparison of the two profiles). Figure made with GeoMapApp
(www.geomapapp.org). ............................................................................................................................413

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ABSTRACT
Earthquake by earthquake, plate-boundary fault systems have built mountains, opened basins, and
moved entire blocks of the crust, shaping and reshaping the Earth’s surface over millions of years, creating
today’s landscapes, climates, and ecosystems. Mechanisms behind earthquake timing and recurrence
remain largely unknown, and the interactions between faults that together accommodate overall movements
of tectonic plates are still not well understood. Knowledge of past and current fault behavior is a crucial
guide to refining techniques and assumptions used in seismic hazard assessment, which is fundamental to
evaluating the effects of future earthquakes on people and the built environment. This thesis builds a bridge
between slip histories of single faults and the behavior of entire plate-boundary fault systems.
This thesis comprises eight chapters, each of them tackling an aspect of active strike-slip fault behavior.
Chapter 1 introduces the basic concepts used in the thesis, as well as the main conundrums that I try to
resolve. Chapters 2 and 3 tackle the structural complexity surrounding a fault as an explanation for strain
accumulation and release variability, through the study of several active strike-slip plate boundaries around
the world. In these chapters, I use a metric that I developed which quantifies the relative structural
complexity surrounding a fault, which enables comparison of slip-rate variability with complexity of the
fault network, as well as comparison between geodetic slip-deficit rates with geologic rates averaged over
small-displacement scales and large-displacement scales. Chapter 4 examines the right-lateral slip behavior
of the Kekerengu fault at Bluff Station in South Island New Zealand, during the past four to five
earthquakes, including the Mw = 7.8 2016 Kaikōura earthquake. The next three studies present the slip
histories of three dextral strike-slip faults: the Kekerengu fault and the Wairarapa fault in New Zealand,
and the Elsinore fault in southern California. In these studies, I describe the geomorphology of each site
based on field investigations and interpretation of lidar data; I define geomorphic features that have been
offset by the fault of interest, and constrain the age of these offsets with geochronological methods such as
luminescence and radiocarbon dating, in order to obtain a slip-rate history of the fault. The last chapter

xlv
delves into numerical simulations that I use to explore the frictional properties of the Alpine fault in New
Zealand to explain its earthquake recurrence pattern, which has been characterized by one of the world’s
longest paleoseismic records. The observations and results of these studies have fundamental implications
for earthquake fault behavior, lithospheric mechanics, discrepancies between geodetic and geologic slip
rates, and probabilistic seismic hazard assessment.

1
CHAPTER 1 Introduction
Documenting the behavior of active faults at various spatial and temporal scales is fundamental to
improving geologists’ understanding of active tectonics. Deciphering the mechanisms responsible for
earthquake timing and recurrence, temporal fault slip patterns, as well as elastic strain accumulation and
release behavior along an active fault can be done through paleoseismic investigations of active faults, sliprate studies, and earthquake simulation modeling. In this thesis, I use a combination of these methods to
document strike-slip fault behavior at multiple spatial scales ranging from a single fault section to an entire
plate-boundary system, and temporal scales ranging from an individual earthquake to several tens of
earthquake cycles spanning multiple millennia.
1.1. Earthquake cycle conundrums
The core focus of this thesis is on active fault systems. Faults are fractures, discontinuities in a volume
of rock along which displacement has accumulated, often spanning multiple earthquakes. Therefore, the
understanding of earthquakes, which cause fault displacements, is a key component of this thesis.
Earthquakes are the result of the sudden elastic rebound of stored elastic energy, an idea that was first
proposed by Reid (1910) with the elastic rebound theory. The brittle upper crust, where the majority of
earthquakes occur, is an elastic body that accommodates stress by deforming continuously at the scale of
individual atomic bounds, and that warps elastically whilst faults therein remain locked (Figure 1.1b). Once
the yield strength of a section of crustal material is exceeded, a break occurs along the fault (Figure 1.1c),
which releases the stored elastic energy. An offset appears along the fault which, in the context of the elastic
rebound model, matches the amount of slip deficit that accumulated in the crust by elastic deformation over
the interseismic period (i.e., the period between two successive earthquakes). The study of such
displacements, as they are recorded in the landscape (Figure 1.1c), is one of the topics I explore in this
thesis.

2
Figure 1.1: Schematic explanation of the elastic rebound theory of Reid (1910), showing (a) an undeformed
configuration of a fault being crossed by a fence (as in the original examination of the displacement of the
ground surface that accompanied the 1906 San Francisco earthquake), and by a geomorphic marker (a
stream), (b) the accumulation of elastic strain on each side of the fault, (c) the release of that elastic strain
during an earthquake, with the generation of seismic waves resulting in ground shaking (hypocenter shown
as a star), and (d) the rebound to an undeformed shape with both the fence and the geomorphic marker
recording the earthquake displacement.
The elastic rebound theory model, illustrated in Figure 1.1, provides a simple and intuitive explanation
for the gradual accumulation and release of stress and strain during an earthquake cycle. This model has
enabled the initial formulation of simple relationships between the rate at which faults are loaded and
earthquake recurrence patterns that will result (Shimazaki and Nakata, 1980; Figure 1.2). These
relationships were attractive not only because of their simplicity but because they made intuitive sense
when assigning fixed failure-stress parameters to the time-predictable earthquake model (i.e., the amount
of time following an earthquake depends upon the size of the earthquake, or, equivalently, the final stress
varies in time while the initial stress remains constant), and basal failure-stress parameters to the slip-

3
predictable earthquake model (i.e., the longer the time-interval between two successive earthquakes, the
larger the displacement of the second earthquake), at a given fault segment. When both the time-predictable
and the slip-predictable models are combined, they predict quasi-periodic repetition of characteristic
earthquakes that show similar rupture extents and displacements (Schwartz and Coppersmith, 1984). In this
scenario, the recurrence time intervals between successive earthquakes have very low variability.
Figure 1.2: Recurrence models, adapted from Shimazaki and Nakata (1980). τ1 and τ2 are final and initial
stresses of faulting, respectively.
An earthquake recurrence pattern can be described by the coefficient of variation (CoV) of the
recurrence time intervals, which is the ratio between the standard deviation of these intervals to their mean
(σ/μ). The CoV is an indicator used by paleoseismologists to characterize the earthquake recurrence
behavior of a fault (Kagan and Jackson, 1991):
- 0 < CoV < 1 for a quasi-periodic behavior,
- CoV > 1 for a clustered behavior,

4
- CoV = 1 for a random behavior,
- CoV = 0 for a strictly periodic behavior.
Paleoseismic records enable the characterization of such earthquake recurrence patterns. They are
derived from geochronological analysis of stratigraphy deforming or being deposited in response to surface‐
rupturing earthquakes and/or ground shaking. However, paleoseismic records are inherently biased against
the preservation of evidence of relatively small earthquakes that do not rupture the surface, or the distinction
between events that occurred within a short amount of time that did not permit significant accumulation of
new sediments (McCalpin and Nelson, 2009; Ludwig et al., 2010; Biasi et al., 2015; Williams et al., 2019).
Nonetheless, paleoseismology is a crucial field of study that allows geologists to establish the timing of
past earthquakes. Several paleoseismic studies on the strike-slip, right-lateral, central San Andreas fault in
California, and the North Anatolian fault in Turkey advocate for slip-predictable models, based on
paleoearthquake ages and available cumulative-slip footprint observed in the geomorphology (Akçiz et al.,
2010; Kondo et al., 2010). However, these studies span no more than five earthquakes, which prevents any
robust assessment of whether the faults in question behave like one model or another. Other studies have
shown that the time-predictable and slip-predictable models are unable to explain earthquake recurrence
patterns in many cases. Unsurprisingly, paleoearthquake records that do not conform to time-predictable
and slip-predictable models occur within structurally complex fault networks such as the Pacific-North
America plate boundary in southern California (Weldon et al., 2004), or subduction zones (Konca et al.,
2008; Rubinstein et al., 2012). On the southwestern section of the Alpine fault, at Hokuri Creek, Berryman
et al. (2012b) have shown that earthquakes occur quasi-regularly, and likely rupture with a consistent,
"characteristic" displacement. In CHAPTER 8 of this thesis, I show that even in this case, it is impossible
to reconcile the earthquake recurrence behavior of the Alpine fault with the time- and slip-predictable
models (Gauriau et al., 2023). In addition, in the example of the Mw ~ 6 earthquake sequence at Parkfield
(which started in 1857), on the central San Andreas fault, none of the slip- and time-predictable models
manage to predict the seismic events (e.g., Murray and Segall, 2002). Explanations for this behavior include

5
(1) local variations in pore fluid pressure that can modify the amount of stress required for nucleation of an
earthquake, (2) stress perturbations that modulate the fault state, which in turn affect the time to the next
event, and (3) long-term post-seismic effects such as viscoelastic relaxation of the lower crust (Murray and
Segall, 2002; Murray and Langbein, 2006). The fact that these intuitive models of slip-predictability and
time-predictability (Figure 1.2) cannot be used to predict earthquake occurrence in relatively simple and
well-understood settings such as the southwestern Alpine fault or the central San Andreas reveals that
important aspects of earthquake physics are not captured by these models, and/or that the assumptions that
govern those models are potentially oversimplified.
Another related problem that arises from the elastic rebound theory is that the model implies that
earthquakes have complete stress drops (i.e., the stress across the fault after an earthquake is reset to its
original value). As noted earlier, the elastic rebound theory predicts that the coseismic displacement will
equal the slip deficit that accumulated during the interseismic phase of elastic strain deformation (Figure
1.1), and therefore that the stress drop will be complete, which in reality is rarely the case (e.g., Hanks,
1977; Fischer and Hainzl, 2017). Even for one of the largest historically recorded earthquakes, the 2011
Mw = 9 Tohoku earthquake, it was found that although the stress drop was nearly complete, some residual
stress was not released (Hasegawa et al., 2011). Pertaining to this conundrum, the nearly complete stress
drop of the 2015 Mw = 5.2 Borrego Springs earthquake was characterized as “anomalous” by Ross et al.
(2017).
Additionally, not all strain is necessarily accommodated by slip on the fault plane during an earthquake,
and in some cases, off-fault deformation can occur in larger amounts than on-fault deformation. Off-fault
deformation has been observed on immature faults over time scales spanning thousands to millions of years
(e.g., Rockwell et al., 2002; Mitchell and Faulkner, 2009; Shelef and Oskin, 2010; Dolan and Haravitch,
2014; Herbert et al., 2014; Zinke et al., 2015), and during the time scale of an individual earthquake (e.g.,
Gold and Cowgill, 2011; Zinke et al., 2014; Milliner et al., 2015; Zinke et al., 2019; Antoine et al., 2022).

6
As a fault becomes more structurally “mature” with increasing cumulative displacement over multiple
earthquake cycles (Wesnousky, 1988; Stirling et al., 1996), surface slip localizes onto the fault, which
reduces the off-fault damage (Dolan and Haravitch, 2014), while the shear zone narrows and strain tends
to localize (Perrin et al., 2016; Hatem et al., 2017). Interestingly, relatively mature faults may tend to
produce earthquakes with lower stress drop than relatively immature faults that are characterized by smaller
cumulative displacements (e.g., Kanamori and Anderson, 1975; Stirling et al., 1996; Hecker et al., 2010).
Beyond single-earthquake scale behavior, records of strain accommodation on active faults further
bring into question another component of the elastic rebound theory, which implies a constant rate of strain
accommodation. Recent evidence, based on slip-rate studies, has shown that some faults do indeed have
relatively constant slip rates over thousands to tens of thousands of years (Van Der Woerd et al., 2002;
Noriega et al., 2006; Kozacı et al., 2009; Salisbury et al., 2018; Grant-Ludwig et al., 2019), yet other faults
exhibit periods of acceleration and deceleration of strain release, spanning several earthquake cycles (e.g.,
Wallace, 1987; Weldon et al., 2004; Dolan et al., 2016; Zinke et al., 2017, 2019, 2021; Wechsler et al.,
2018; Hatem et al., 2020; Fougere et al., in revision). I explore the potential mechanisms that dictate this
behavior in CHAPTER 2, with a specific focus on strike-slip plate-boundary fault systems’ structural
complexity being a driver for relatively variable slip rates (Gauriau and Dolan, 2021). The variability of
strain accommodation may require mechanisms that store energy within a fault zone, or a fault system,
throughout the earthquake cycle. Alternatively, the accumulation of elastic strain energy on some faults me
not be constant through time. I discuss this latter scenario in CHAPTER 3 (Gauriau and Dolan, 2024).
This component of my research highlights the importance of assessing patterns of elastic strain
accumulation and release along active faults. Investigating earthquake cycles across different temporal and
spatial scales is key to understanding the slip behavior of active faults, and to integrate that knowledge into
larger-scale studies encompassing several faults within a fault system.

7
1.2. Earthquake-scale fault behavior
To improve the understanding of the variability of the earthquake cycle, I studied the Hokuri Creek
paleoseismic record on the southwestern section of the Alpine fault in South Island, New Zealand. This site
is thought to exhibit a pattern of exceptionally simple earthquake recurrence behavior (Berryman et al.,
2012b). The choice for using this record is twofold: (1) this is one of the longest available paleoseismic
records, which enables me to look at a large enough range of possible recurrence time intervals between
events, and (2) it exhibits very low variability in its recurrence time intervals. In CHAPTER 8, I investigate
the frictional fault parameters that best describe the quasi-periodic behavior of the Alpine fault at the Hokuri
Creek site, using rate-and-state friction earthquake models. The Hokuri Creek paleoseismic record is a good
candidate for exploring the potential variability of the earthquake cycle, since it shows relatively little
variability. Yet, I show in CHAPTER 8 that such small variability appears to be best represented by
deterministic chaos.
Understanding the earthquake cycle requires not only the investigation of earthquake recurrence
patterns, but also the study of possible coseismic displacements at the single-earthquake scale. This is the
focus of CHAPTER 4, wherein I investigate the slip behavior of the Kekerengu fault at the Bluff Station
site in South Island, New Zealand. Using cumulative displacements of river terrace risers along with
paleoseismic ages of late-Holocene earthquakes, recorded in a nearby paleoseismic trench (Morris et al.,
2022), I compare the possible slip histories for earthquakes that occurred prior to the 2016 Mw = 7.8
Kaikōura earthquake, which generated about 10 meters of slip at this site. This study explores the
possibilities that Kaikōura earthquake was exceptional or typical in terms of coseismic displacements on
the Kekerengu fault.
Both studies detailed in CHAPTER 4 and CHAPTER 8 shed further light on the intrinsic variability of
behavior in earthquake recurrence and slip-per-event patterns.

8
1.3. Millennial-scale fault behavior
To better understand fault behavior, I explore longer-term fault slip behavior. This is traditionally done
using slip-rate records, for which two datasets are needed: (1) a displacement measurement of an offset
feature (e.g., a river channel, a landslide deposit, a terrace riser), and (2) the age of the offset feature (e.g.,
age of incision, age of occurrence of landslide, or age of abandonment of a river terrace adjacent to a terrace
riser). If a slip-rate study site includes several geomorphic features that display increasing amounts of
cumulative displacement that can be dated, then different time/slip ranges can be combined to create an
incremental slip-rate record. In such cases, slip-rate records might highlight changes of rate over different
increments of displacement and time. In the following section, I describe the terminology and the methods
used to develop an incremental fault slip-rate record. CHAPTER 5, CHAPTER 6, and CHAPTER 7 focus
on the development of such records for the following right-lateral strike-slip faults: the Kekerengu and
Wairarapa faults in New Zealand, and the Elsinore fault in southern California.
1.3.1. Determining fault offsets
Offset geomorphic features recorded in the landscape after one or more earthquakes on a strike-slip
fault can include river channels, landslides, river terraces, bedrock landforms, or alluvial fans. The sites I
study for developing slip-rate records for the Kekerengu fault, the Wairarapa fault, and the Elsinore fault
are all crossed by a river. The geomorphic features associated with these rivers are therefore primarily
channels and river terraces, which can record progressive displacement along the strike-slip fault of interest.
The terminology of river terraces is borrowed from stairs. A terrace tread is a nearly horizontal surface
that constitutes evidence of a former level of the river floodplain. A terrace riser is the steeply sloping edge
of the terrace, formerly a river bank, that separates two successive terrace treads (Figure 1.3, Figure 1.4).
Using displaced terrace risers to measure fault displacements and determine slip rates for strike-slip faults
requires some knowledge of how those alluvial landforms have interacted with the river and the fault scarp

9
itself, and when these landforms began to record fault slip. The age of a terrace riser represents the time
when lateral fluvial erosion along the paleo-river bank ceased. This corresponds to a time interval between
(1) the abandonment age of the terrace tread located above the riser (named upper-terrace tread, relative to
the riser of interest) and (2) the abandonment age of the terrace tread located below the riser (named lowerterrace tread). The relationship between the timing and size of terrace riser displacements is challenging to
resolve (Suggate, 1960; Lensen, 1964; Avouac, 1993; Cowgill, 2007; Harkins and Kirby, 2008), but can be
established with the understanding of a river’s capacity to trim fluvial terraces. The lateral erosion of a riser
will cease either close in time to the abandonment of the lower bounding terrace tread, or it will cease close
in time to the initial incision of the upper-bounding terrace tread of that riser. The first model is typically
referred to as a lower-terrace reconstruction, whereas the second model is called an upper-terrace
reconstruction. The use of one model over another can be justified by the observation of the river dynamics
at a specific study site, as suggested by Cowgill (2007). Specifically, a lower-terrace reconstruction is
justified by the presence of coarse-grained, pebble-boulder sized sediments on the bedload gravels of the
terrace. This indicates deposition by high-energy stream flow with enough erosive power to trim risers. In
this situation, the risers are completely eroded after any potential displacement and before lower-tread
abandonment, and therefore, the width of the active channel increases during progressive fault displacement
(Figure 1.3). On the other hand, an upper-terrace reconstruction assumes that the stream flow is too weak
to laterally trim a riser after it has been offset. In this case, the lower tread is abandoned diachronously,
with different riser slopes aside from the fault and a decrease in age from the back to the front of the terrace
tread (Figure 1.3).

10
Figure 1.3: Block diagrams showing fluvial terrace terminology and models of lower-terrace and upperterrace reconstructions for linking riser offsets with terrace abandonment ages for the determination of
strike-slip fault slip rates. Modified after Cowgill (2007).
Offset measurements are obtained by backslipping one side of the fault along the fault trace relative to
the other side, until the displaced geomorphic feature is restored to a sedimentologically plausible
configuration. Several sedimentologically plausible configurations are considered in the reporting of
confidence intervals, with the minimum and maximum acceptable offsets spanning a 95% confidence
interval throughout this thesis. The maximum and minimum estimates represent the structural limits on
possible streamflow geometries, based on acceptable curvature of the water flow that originally created the
geomorphic feature. Riser restoration to determine fault offsets can be done visually, using lidar-derived
maps (such as hillshade, topographic, slope, or aspect maps), or using an automated code like LaDiCaOz
that determines the offset with the prior definition of the fault trace and the offset feature by the user (Zielke
and Arrowsmith, 2012). The use of such software is limited to the pure lateral component of offsets, whereas
a thorough analysis of each offset using observation from lidar-derived maps, and Geographical
Information System (GIS) tools allows one to determine potential vertical components of each offset as

11
well (Figure 1.4). This is demonstrated in CHAPTER 6 in a preliminary study of the Waiohine River site,
on the Wairarapa fault in North Island, New Zealand, at a site where that fault experiences oblique slip,
with both dextral and reverse motion.
Figure 1.4: Schematic block diagram showing basic geometric features of fault-displaced terrace risers.
Diagram depicts dextral offsets with a vertical component, and showcases the geomorphological terms used
for river terraces (tread, riser, channel, active floodplain). The setting illustrated represents the Waiohine
River site, described in CHAPTER 6.
1.3.2. Dating fault offsets
The determination of a fault slip rate also requires the dating of the offset features described above. In
this thesis, I use two geochronological methods to help determine the ages of the fault offsets: InfraRed
Stimulated Luminescence (IRSL) dating, and radiocarbon (
14C) dating.
1.3.2.1. Luminescence dating
Luminescence dating methods date the most recent exposure to sunlight of a mineral grain. In my thesis,
this method is used to date when a grain of river sediment was deposited and buried before the river
abandoned its floodplain to incise down to a new floodplain. In other words, this method dates the age of
abandonment of fluvial terraces that are displaced laterally by an active fault.

12
The basis of luminescence dating is the trapping and release of energy by electrons contained in the
different electron shells of a mineral crystal. In the case of InfraRed Stimulated Luminescence (IRSL), the
minerals used for dating are potassium feldspars (Rhodes, 2015). When the mineral’s electrons are exposed
to radiation, usually coming from the natural decay of surrounding radioisotopes in the sedimentary
deposits, they tend to move from a lower energy level (valance band) to a higher energy level (conduction
band). Trapped electrons can drop back to their valence band upon an infrared light stimulation (in the case
of IRSL luminescence). With this, the electrons release photons (luminescence) whose energy is equivalent
to the energy state change. This amount of energy is a function of the total radiation dose the sample
received over time, and is therefore a function of the amount of time during which that energy built up. The
amount of time it takes for a sediment grain to build up that energy is determined from the ratio of past
radiation exposure (paleodose, or equivalent dose, measured in Grays) to the rate at which the sample was
irradiated (dose rate, in Grays per time unit). The dose rate is typically measured both in situ, after the
collection of a sample, with a gamma-spectrometer placed in the sample hole, as well as by subsequent
analysis of sampled sediments with inductively coupled plasma mass-spectrometer (ICP-MS). The
equivalent dose is obtained via a growth curve drawn through the points representing different levels of
irradiation measured in the laboratory. The curve obtained can be matched against the observed
luminescence to estimate the equivalent dose. The full IRSL protocol that is used for the studies presented
in CHAPTER 4, CHAPTER 5, CHAPTER 6, and CHAPTER 7 for the determination of fault slip rates, is
described in Rhodes (2015).
1.3.2.2. Radiocarbon dating
The other geochronological method I use is radiocarbon dating. It is based on the radioactive decay of
carbon-14 (14C), formed in the atmosphere by cosmic radiation interactions with nitrogen (N). 14C is present
in the atmosphere in the form of CO2, which becomes fixed in plants during photosynthesis. Plants are
therefore made of carbon that matches the isotopic composition of carbon in the atmosphere (including 12C,

13
13C, and 14C), at the time it was assimilated. Once a plant cell dies, it no longer absorbs carbon from the
atmosphere. At this point, a radioactive decay clock starts to record the amount of 14C that has decayed into
the daughter 14N over time. Two methods can be used to measure the 14C activity from a sample of interest.
The first method to determine a 14C age is to observe the rate of decay obtained from a sample in the
laboratory, via β-counting, which corresponds to counting the β emissions from a sample over a period of
time. That rate of decay of emissions is a function of the number of 14C half-lives passed, itself a function
of time (one 14C half-life being 5735 years). The second method directly measures the amounts of 14C, 13C,
and 12C according to their respective isotopic weights, using an accelerator mass spectrometer. The relative
amounts of each isotope are then used to calculate the radiocarbon age.
Radiocarbon ages are presented as years before present (BP), with “present” referring to 1950 C.E.
Additionally, a raw 14C age needs to be calibrated to correct for variable amounts of atmospheric 14C over
time, due to changes in the cosmic-ray fluxes responsible for 14C formation. Tree rings provide a valuable
organic record that describes the variations in 14C production rate through time. This record has led to the
development of calibration curves used by calibration software (Bronk Ramsey, 2001, 2017; Stuiver, 2005),
which corrects for atmospheric 14C fluxes in time and translates radiocarbon ages into calendar year dates.
During the course of my PhD, I collected charcoal samples at only one location in New Zealand, for
the slip-rate study of the Kekerengu fault (CHAPTER 4, CHAPTER 5). The age of a charcoal sample will
always be older than the deposit in which it lies, since it may be derived from an organism that lived for
several decades or centuries, before it was burnt and preserved in the sedimentary record.
1.3.3. Modeling incremental slip rates
For the studies presented in CHAPTER 5, CHAPTER 6, and CHAPTER 7, I plan to use the
methodology of Zinke et al. (2017, 2019) for the modeling of incremental fault slip rates. This methodology,
which utilizes a software package called RISeR, has been applied effectively in several other studies (e.g.,

14
Zinke et al., 2019; Hatem et al., 2020; Zinke et al., 2021). This method calculates the incremental slip rates
represented by each offset-age pair using a Markov chain Monte Carlo sampling method. This method
considers the uncertainties in the offset measurements and age determinations, by representing them by
their probability density functions (PDFs). RISeR can generate several types of input PDFs, which can be
asymmetrical (with triangular PDFs, for asymmetrical offset measurements), or symmetrical (with
Gaussian distributions, for the luminescence ages), or can be given as input any PDFs already stored in a
text file. In addition, some constraints about the allowable range of slip-rate values that can be considered
by the code are also taken into account. Specifically, the code accommodates the assumption that the faults
slipped uni-directionally, with no backward displacement during the slip history (as originally proposed by
Gold and Cowgill, 2011). Unlike sampling methods used by Gold and Cowgill, (2011) and Gold et al.
(2017), this RISeR code samples each displacement and age measurement in a way that is representative of
the PDF for that measurement. It allows for propagation of uncertainties for both incremental and individual
slip-rate calculations. Slip rates and uncertainties are then reported with 68 % or 95 % confidence intervals.
1.4. Comparison of slip-rate behavior with structural complexity
Incremental fault slip-rate records are particularly scarce, because such records rely on the availability
of sites whose geomorphology has recorded several ranges of cumulative fault displacements. I compiled
tens of such records from active strike-slip faults from the most studied strike-slip plate boundaries in the
world and have established a comparison between these published incremental fault slip rates with the
proximity and number of other active faults in the surrounding plate-boundary system. This study is detailed
in CHAPTER 2. I examine the slip-rate behaviors of active strike-slip faults located in the San Andreas,
Alpine-Marlborough, North Anatolian, and Dead Sea fault systems and define a metric, called the
coefficient of complexity (CoCo) that quantifies the density and displacement rates of faults surrounding a
master fault. I show that CoCo correlates with the constancy or irregularity of the master fault’s incremental
slip rate.

15
1.5. Comparison of geologic slip rates with geodetic slip-deficit rates
In CHAPTER 3 I use the method introduced in CHAPTER 2 to relate the relative structural complexity
of the surrounding fault network to potential discrepancies between geologic slip rates (elastic strain release
rates) and geodetic slip-deficit rates (elastic strain accumulation rates). My analysis shows that the relatively
constant slip rates on faults embedded within structurally simple strike-slip tectonic networks (low-CoCo
faults) generally match rates of elastic strain accumulation of the faults’ shear zones, as measured by
geodetic slip-deficit rates. In marked contrast, geodetic slip-deficit rates for faults embedded within
structurally complex fault systems (high-CoCo faults) are less consistent with geologic rates, whether
averaged over short or large displacement scales, indicating significant variations in strain-accumulation
rates on high-CoCo faults. These results suggest patterns of geodetic-to-geologic rate ratios that may be
indicative of the near-future behavior of the fault of interest.
Resolving the earthquake cycle in space and time requires understanding of what might be happening
below the surface of a fault trace, at depths where the fault is no longer brittle. CHAPTER 3 addresses the
potential variability in flow rates of ductile shear zones, and the interactions between the ductile roots and
the brittle upper-crustalsections of a faults. The ductile shear zone of a fault is located below itsseismogenic
zone, typically below depths of ~12-15 km, and accumulates strain and displacement by viscous flow (e.g.,
Fossen and Cavalcante, 2017). To date, there have been limited studies on the variability of strain
accumulation over multiple earthquake cycles within fault shear zones. To that extent, CHAPTER 3 details
the use of geodesy as a proxy for the shear zone behavior.

16
Figure 1.5: Schematic diagram showing the three-dimensional architecture of a fault system with brittle
upper crustal faults underlain by ductile shear zones below a brittle-ductile transition zone. The lower
graphs are inspired by Chester, 1995; Cole et al., 2007; and Barbot and Fialko, 2010. The frictional rate
behavior is defined by the values of frictional parameter a-b (Ruina, 1983).
The study of the mid-to-lower crust ideally may yield a physical understanding of why and how faults
can effectively speed up or slow down. The potential mechanisms responsible for strength variations in the

17
ductile shear zone are addressed in detail by Cawood and Dolan (in revision). They suggest that the strength
variations are due to several physical mechanisms that affect the rocks in the strongest portion of the fault,
an area that corresponds to the brittle-ductile transition (Figure 1.5). These mechanisms must be reversible
or can be counteracted so that a fault can switch behavior in terms of its strength, or resistance to shear.
These strengthening mechanisms include processes such as hydrothermal cementation, shear folding, and
strain hardening, whereas the weakening mechanisms can include the injection of fluids into the shear zone,
grain size reduction, and heating. All of these processes are candidates for explaining periodic strengthening
and weakening of shear zones, as well as complementary mechanics between neighboring faults (Cawood
and Dolan, in revision).
1.6. Motivation for this research
The goal of studying behavior of active faults at different temporal and spatial scales is to update and
improve the primary inputs of probabilistic seismic hazard assessments (PSHA). PSHAs are used to develop
models of seismic hazard (e.g., Field et al., 2017; Petersen et al., 2024) that informs safety guidelines for
high-risk facilities (such as nuclear power plants) and help build code requirements and determine
earthquake insurance rates (e.g., Cao et al., 1999; Gkimprixis et al., 2021). The common goal of such studies
is to ensure that human structures can withstand a given level of ground shaking resulting from an
earthquake.
Earthquake shaking hazards are in part calculated by projecting earthquake rates based on earthquake
history and fault slip rates. In other words, PSHAs use fault slip rates and earthquake recurrence time
intervals obtained from geomorphic and paleoseismic studies, like the ones described in this thesis, as basic
inputs into these models. A concern about the use of fault slip rates in such models is the integration of
potential slip-rate variabilities throughout multiple earthquake cycles, and earthquake recurrence time
variability.

18
The work I present here improves the understanding of fault mechanics and fault system behaviors, at
time scales ranging from the single earthquake to multiple earthquake cycles over tens of thousands years.
This thesis does not intend to perform any changes in PSHA, but rather aims to highlight the results of my
research that provide areas of improvement and reflection for the next-generation PSHA tools.
In CHAPTER 2 and CHAPTER 3, I emphasize the need to consider faults embedded in complex fault
system as faults that exhibit variable behavior, and suggest that the comparison between geodetic slipdeficit rates and geologic slip rates may bear key information that could be further used in the framework
of probabilistic seismic hazard analyses. In CHAPTER 4, CHAPTER 5, CHAPTER 6, and CHAPTER 7, I
study examples of such faults, embedded in the complex fault networks of the Marlborough fault system
and the North Island dextral fault belt in New Zealand and the southern San Andreas fault system in
southern California. The slip-rate histories of such faults raise the question of the slip-rate input to use in
seismic hazard models. Finally, in CHAPTER 8, I use earthquake simulations to model the earthquake
recurrence patterns of a long paleoseismic record that exhibits quasi-periodic behavior, and show that the
best-fitting models are the ones defined by deterministic chaos, reviving the debate on seismic forecasts
based on earthquake recurrence times.

19
CHAPTER 2 Relative structural complexity of plate-boundary fault systems
controls incremental slip-rate behavior of major strike-slip faults
This chapter is based on the following published article:
Gauriau, J., & Dolan, J. F. (2021). Relative Structural Complexity of Plate‐Boundary Fault Systems
Controls Incremental Slip‐Rate Behavior of Major Strike‐Slip Faults. Geochemistry, Geophysics,
Geosystems, 22(11), e2021GC009938.
2.1. Abstract
A comparison of published incremental fault slip rates from four major strike-slip faults with the
proximity and number of other active faults in the surrounding plate boundary systems shows that the
behavior of the primary fault is correlated with the structural complexity of its tectonic setting. To do this,
we characterize the relative structural complexity of the fault network surrounding a fault location of
interest by defining the Coefficient of Complexity (CoCo), which quantifies the density and displacement
rates of the faults in the plate-boundary network at specified radii around the site of interest. We show that
the relative constancy of incremental slip rates measured along primary faults of the Alpine, San Andreas,
North Anatolian, and Dead Sea fault systems reflects the proximity, number, and activity of their close
neighbors. Specifically, faults that extend through more structurally complex plate boundary fault systems
exhibit more irregular slip behavior than faults that pass through simpler settings. We suggest that these
behaviors are likely a response to more complex stress interactions within more structurally complicated
regional fault systems, as well as possible temporal changes in fault strength and/or kinematic interactions
amongst mechanically complementary faults within a system that collectively accommodates overall
relative plate motion. Our results provide a potential means for better evaluating the future behavior of large
plate-boundary faults in the absence of well-documented incremental slip-rate behavior, and may help
improve the use of geological slip-rate data in probabilistic seismic hazard assessments.

20
2.2. Introduction
Understanding the manner in which major plate-boundary-scale faults accommodate slip is of critical
importance for a wide range of issues in the Earth sciences, including the geodynamics of relative plate
motions, the mechanics of storage of elastic strain energy on faults and the release of that energy during
earthquakes, the steadiness and predictability of fault slip through time, and the use of geological slip-rate
data in seismic hazard assessment. Previous analyses of major plate-boundary strike-slip faults have
revealed a wide range of behaviors, from relatively constant slip rates spanning a range of time intervals on
some faults (Noriega et al., 2006; Kozacı et al., 2009, 2011; Gold and Cowgill, 2011; Berryman et al.,
2012b; Dolan et al., 2016; Salisbury et al., 2018) to highly non-steady slip rates on other faults (Wallace,
1987; Friedrich et al., 2003; Weldon et al., 2004; Ninis et al., 2013; Dolan et al., 2016; Zinke et al., 2017,
2019; Hatem et al., 2020). Although studies of fault slip rates through time have placed important
constraints on the behaviors of major faults around the world, the controls on these behaviors remain poorly
understood. This diversity of fault behaviors raises a host of important questions. Most basically, why do
some faults slip at a relatively constant rate while others exhibit wide variations in rates spanning, in many
cases, multiple earthquake cycles? Even more importantly, what controls such behaviors? And do they
occur in predictable fashion, such that the constancy of fault slip rate can be predicted in a general sense at
millennial time scales?
One explanation that has been proposed to explain the variety of fault incremental slip-rate behavior is
that the relative structural complexity of the region surrounding a fault controls the constancy or irregularity
of slip through time (Hartleb et al., 2006; Dolan et al., 2007; Kozacı et al., 2011; Berryman et al., 2012b;
Dolan et al., 2016; Chen et al., 2020). For example, how might a single strike-slip fault embedded within a
structurally simple section of a plate boundary behave in terms of incremental slip rate through time (e.g.,
Figure 2.1a) relative to a similar fault embedded within a more structurally complex plate boundary
comprising multiple fast-slipping faults (Figure 2.1b)? To explore this issue further, we examine the

21
tectonic context and relative structural complexity of the fault networks surrounding four large plateboundary-scale strike-slip fault systems – the Alpine fault system in New Zealand, the San Andreas fault
system in California, the North Anatolian fault system in Turkey and the Dead Sea fault system in the
Middle East – each of which exhibits along-strike variations in the relative structural complexity of its
surrounding plate-boundary fault network. We introduce a new parameter, called the Coefficient of
Complexity (CoCo), which is designed to quantify the relative tectonic complexity of these different fault
networks. Defining the CoCo for different sites in these four examples facilitates comparison with the
incremental strike-slip-rate behavior of the primary fault in each of these plate boundaries. We discuss our
results in light of their implications for the system-level behavior of regional fault networks, the
geodynamics of relative plate motions, and the development of next-generation PSHA strategies.

22
Figure 2.1: Schematic 3D diagrams of two idealized plate boundaries that exhibit markedly different faultsystem complexity. (a) Structurally simple plate-boundary fault network, dominated by a single fastslipping strike-slip fault surrounded by few other active faults. (b) Structurally complex plate-boundary
fault network characterized by multiple fast-slipping strike-slip faults, including reverse faults and
conjugate strike-slip faults. The gray star denotes a hypothetical incremental fault slip-rate site on the fault
under consideration in our CoCo analysis. (c) Example of a structurally complex plate-boundary fault
network, the Marlborough fault system in South Island, New Zealand.

23
We call this metric the Coefficient of Complexity (CoCo), whose value at a given site (labeled as a star
on Figure 2.1) is defined as follows:
CoCo site (r) =
r²
v L
Nf 

 (1)
Where Nf is the number of faults within the circle of specified radius r, v is the velocity or average slip rate
(in mm/yr) of the fault section of length L (in km) within the circle. This sum is scaled by the πr² area (in
km²) of the circle of radius r. The specific unit of this coefficient is thus equal to mm/yr/km. This unit thus
describes a density of velocity averaged over a surface. More precisely, the unit refers to the average
velocity of a given 1-km fault within the observation circle of radius r. In this initial analysis, we use the
following values for the radius r to gauge their effect on the CoCo metric: 50, 60, 70, 80, 90, 100, 120, 150
and 200 km. These incremental values serve as comparisons between different surfaces that may encounter
regions with different active-fault densities. We chose a 200-km radius as a maximum threshold based on
the largest thus-far suggested potential interaction distance for faults within any of the regions we examine.
Specifically, 200 km is the distance between the paleoseismic trench site of McAuliffe et al. (2013) on the
Panamint Valley fault in the Eastern California Shear Zone (ECSZ) and the Mojave section of the San
Andreas fault. McAuliffe et al. (2013) suggested that even at this large distance, the behavior of the
Panamint Valley fault may influence the occurrence of large-magnitude (M≥7) earthquakes on both the San
Andreas and Garlock faults. A 200 km measurement radius will also include the maximum width of the
elastic strain accumulation fields measured by geodesy, which for the four strike-slip primary faults we
analyze will be likely be ≤ ~100-150 km on either side of the fault trace, assuming a typical locking depth
of about 15km.
Because of (i) the large uncertainty intervals that accompany some of the available slip-rate values;
(ii) the different ranges that might be found from one study to another; and (iii) the variability of slip-rate
behaviors of some faults, the CoCo value does not use exact slip-rate values. Rather, we use a set of ranges

24
of values that encompass the variability in the available slip-rate records. Specifically, instead of assigning
one precise slip-rate value, we assign the slip rates of the faults surrounding the primary faults into bins of
slip-rate ranges, in mm/yr: 0; ]0 – 0.2[; [0.2 – 1.0[; [1.0 – 3.0[; [3.0 – 5.0[; [5.0 – 7.0[; [7.0 – 10[; [10 – 15[;
[15 – 20[; and ≥20 (where open brackets refer to the exclusion of the value in the interval, and closed
brackets include that value). For each slip-rate range, a median value for the pertinent slip-rate bin is then
used for the calculation of CoCo (see Appendix A). As examples of this method, Figure 2.2 to Figure 2.5
show 100-km-radius circles around two different sites, illustrating which faults are included in the
calculation of the CoCo for this particular radius. Because it is the relative structural complexity of the plate
boundary surrounding the fault of interest that we try to quantify, the primary fault itself is not counted in
the calculation. In addition, if the slip-rate record at a specific site indicates that the fault has not been
slipping at a constant rate over the time range of study, we use the slip rate averaged over the longest time
interval in described in each study.
For the four plate-boundary strike-slip systems we consider (Alpine, San Andreas, North Anatolian and
Dead Sea faults – Figure 2.2), we used slip-rate and fault trace location information gleaned from fault
databases where these were available. Where downloadable fault databases were not available, we compiled
fault trace and slip rate data from published studies of individual faults (see Appendix A for more details
about the fault databases). All of these data were loaded into a QGIS environment. In the following sections,
we describe the available data for each of the four faults, document how we calculated CoCo values for
each study site, and detail the relationship between the CoCo values and the geometry and state of activity
of the surrounding fault networks. We then describe available incremental slip-rate data from the primary
fault under consideration in each plate boundary, with a focus on the constancy or irregularity revealed by
available incremental slip-rate data. The temporal and displacement scales of the incremental slip-rate
records we use for comparison with the CoCo results range from 102
to 105 years and meters to hundreds
of meters (Table A.6).

25
Figure 2.2: Active fault maps of the four studied strike-slip boundaries showing incremental slip-rate study
sites as white diamonds. (a) Topographic and tectonic map of South Island, New Zealand and the Alpine
fault system, with location of Hokuri Creek and the Marlborough fault system study sites, indicated in inset
(b). (c) Topographic and tectonic map of California and the San Andreas fault (SAF) system, with location
of Van Matre-Wallace Creek and Wrightwood study sites on the SAF, and the Quincy site on the San
Jacinto fault. (d) Topographic and tectonic map of Turkey and the North Anatolian fault system, with
location of Demir Tepe, Düzce, and Ganos Güzelköy sites. (e) Topographic and tectonic map of the Middle
East and the Dead Sea fault system, showing locations of the northern Wadi Araba and Beteiha incremental
slip rate sites.
2.3. Data and Results
2.3.1. The Alpine-Marlborough fault system, New Zealand
The Alpine fault system is one of the longest and fastest-slipping continental strike-slip faults in the
world (Berryman et al., 1992; Norris et al., 1990; Norris & Cooper, 2001; Sutherland et al., 2006). This
~900-km-long, right-lateral strike-slip and oblique-reverse-dextral fault is a structurally mature fault, with
470 km of cumulative displacement along the fault system (e.g., Wellman, 1953, 1984; Sutherland, 1999;
Hall et al., 2004). The Alpine fault system accommodates the majority of Pacific-Australia relative plate

26
motion in South Island, New Zealand, between the west-dipping Hikurangi subduction system to the
northeast and the northeast-dipping Puysegur subduction zone beneath southwestern South Island (e.g.,
DeMets et al., 1994; Beavan et al., 1999; Figure 2.1a). Lateral slip rates along the southwestern section of
the Alpine fault have been measured at 20-30 mm/yr, indicating that the Alpine fault accommodates up to
~75% of the strike-parallel plate motion (Norris & Cooper, 2001; Sutherland & Norris, 1995). Indeed, the
Alpine fault is the only mature, fast-slipping tectonic structure found in southwestern South Island. Other
nearby active faults exhibit relatively slow slip rates (~≤3 mm/yr) and do not extend for distances >90 km,
indicating that the Alpine fault in southwestern South Island is, in our parlance, a tectonically isolated fault.
In contrast, in northern South Island, Alpine fault slip is partitioned northeastward onto the four main
fast-slipping dextral strike-slip faults that constitute the Marlborough fault system (MFS). From north to
south these are the Wairau, Awatere, Clarence, and Hope faults (Figure 2.1b). With other nearby faults, the
Marlborough faults form a much more complex fault network than that surrounding the southern portion of
the Alpine fault.
2.3.1.1. The Hokuri Creek site and the Marlborough fault system: slip rate constancy and
irregularity
The incremental slip-rate behaviors of the Alpine fault and the Marlborough fault are quite
different, with the structurally isolated southwestern part of the Alpine fault slipping at a steady rate of
23±2 mm/yr (Berryman et al., 1992; Sutherland et al., 2006), in contrast to the four faults within the
tectonically complex MFS, all of which exhibit highly variable incremental slip rates throughout Holocenelatest Pleistocene time (Zinke et al., 2017, 2019, 2021; Nicol and Van Dissen, 2018; Hatem et al., 2020).
We use the paleoseismologic record from the Hokuri Creek study site (Berryman et al., 2012b) to
characterize the steady slip rate of the southwestern portion of the Alpine fault (Figure 2.1a; Figure A.1e).
At this site, Berryman et al. (2012b) documented an ~8,000-year-long record of the timing of the past 24
paleo-earthquakes. These data demonstrate relatively regular earthquake recurrence, with a low coefficient

27
of variation (CoV) of 0.33. The combination of an assumed characteristic slip per event, an inference
supported by the similar vertical components of slip from event to event, with this very low CoV implies a
monotonic slip rate (Berryman et al., 2012b).
To examine the behavior of this fault system in a more structurally complicated section of the plate
boundary, we calculated the coefficients of complexity (CoCo) for sites along each of the four main faults
of the MFS. We focus our study of the MFS faults on four sites for which detailed incremental slip-rate
data have been documented: the Hossack Station site on the Hope fault (Hatem et al., 2020), the Saxton
River site on the Awatere fault (Zinke et al., 2017), the Tophouse Road site on the Clarence fault (Zinke et
al., 2019), and the combined Branch River and Dunbeath sites on the Wairau fault. These latter two study
sites are 6 km apart, and we therefore use a single point halfway between them (Zinke et al., 2021); Figure
2.1b). The slip-rate records developed at these sites demonstrate that the four main MFS faults have all
experienced highly variable incremental slip-rates during Holocene-latest Pleistocene, with each
incremental rate spanning an interval of at least three, and commonly more earthquakes (Figure A.1).
Specifically, the Wairau fault’s incremental slip rates has varied by a factor of up to ~4x, from <4 mm/yr
to >15 mm/yr, with a traditional slip rate averaged over the last 11.9 ky of ~4.5 mm/yr (Zinke et al., 2021).
Similarly, the incremental slip-rate record of the Awatere fault exhibits nearly order-of-magnitude temporal
variations in rate at the Saxton River site, with incremental rates ranging from < 2 mm/yr to > 15 mm/yr
(Zinke et al., 2017) and a slip rate averaged since ca. 13 ka of ~5.5 mm/yr. The Tophouse Road site on the
Clarence fault reveals a similar variation in incremental rates, with Holocene-latest Pleistocene incremental
slip rates ranging from ~2 to ~10 mm/yr (Zinke et al., 2019). Finally, the incremental slip rate of the Hope
fault exhibits a factor of ~4 variation, with slip-rate increments ranging between ~8 and > 32 mm/yr, and a
slip rate averaged since ca. 14 ka of 15 mm/yr (Hatem et al., 2020).

28
2.3.1.2. Coefficients of Complexity of Hokuri Creek site and sites on MFS faults
We calculated CoCo values for circular areas surrounding the Hokuri Creek, Dunbeath-Branch River,
Saxton River, Tophouse Road, and Hossack Station sites (Figure 2.3). For simplicity’s sake, only the 100-
km-radius circles for Hokuri Creek and Hossack Station sites are shown on figure 3a. These circles show
which faults, color-coded by their respective slip rates, are included within this specific area.
The Hokuri Creek site on the relatively structurally isolated southwestern Alpine fault reveals CoCo
values that are markedly lower than those determined for the four MFS faults in the more structurally
complicated plate boundary in northeastern South Island. Specifically, the coefficient of complexity
CoCoHokuri Creek shows a narrow range of values that decrease from 3.4×10-2
to 1.7×10-2 mm/yr/km with
increasing radius of observation from r=50 km to r=120 km, stabilizing at ~1.9×10-2 mm/yr/km for the two
remaining larger radii (Figure 2.3a). These low CoCo values reflect the relative tectonic isolation of the
Alpine fault in central South Island. Conversely, the four main MFS faults exhibit much higher CoCo
values, extending all the way up to 33.4×10-2 mm/yr/km for the Clarence fault site for r=50 km. The overall
trend of CoCo values for the MFS faults shows a decrease for those sites with increasing area of observation.
This is because larger circle areas (i.e., larger r) include the central portion of South Island, which exhibits
generally lower levels of seismic activity. A finer observation of the trend reveals differences in behavior
from one fault to another within the MFS itself. The Tophouse Road site, located on the Clarence fault,
displays CoCo values decreasing with radius from 33.4×10-2
to 10.0×10-2 mm/yr/km. Similarly, the CoCo
values obtained for Saxton River on the Awatere fault, the next closest neighbor to the Hope fault, range
between 12.2×10-2
and 33.0×10-2 mm/yr/km and also follow an overall decreasing trend with radius. Those
two sites display the highest CoCo values among the four MFS sites up to a 120-km-radius circle of
observation. This is because the Clarence and Awatere fault are located close to the Hope fault, and several
of the CoCo observation areas include long sections of this fast-slipping fault, which accommodates the
largest portion of the relative plate motion within the MFS (Hatem et al., 2020). The Branch River-

29
Dunbeath site on the Wairau fault, which is the farthest fault from the Hope fault, exhibits higher CoCo
values at distances that incorporate the Hope fault within the observation area (starting from r=90 km;
Figure 2.2b). The CoCo values for this site range between 6.0×10-2
and 19.5×10-2 mm/yr/km. Finally, the
Hossack Station site on the Hope fault exhibits lower CoCo values than any other MFS site for any radius
larger than 70 km, because the Hope fault itself is not counted in the calculation of the CoCo. Specifically,
CoCo values for the Hope fault range from 8.0×10-2
for r=200 km to 15.7×10-2 mm/yr/km for r=50 km. The
Hikurangi megathrust trace offshore eastern North Island and its related high rate of displacement in this
area (≥ 25 mm/yr, Litchfield et al., 2014) becomes incorporated into the CoCo calculation only for r=200
km, for all the four faults. However, the length of the Hikurangi trace that is included in those circles
remains very short, which does not significantly affect the CoCo values obtained at this radius.
Figure 2.3: CoCo results for the Alpine-Marlborough fault system. (a) Active fault map of South Island,
New Zealand, with faults color-coded by slip rate (Litchfield et al., 2014). Circles of 100 km radius are
shown around the Hokuri Creek and Hossack Station sites to illustrate which portions of the surrounding
faults are included in the CoCo calculation for r=100 km. (b) Histogram of the CoCo values obtained at
Hokuri Creek on the southwestern Alpine fault (in blue), Hossack Station (Hope fault), Branch RiverDunbeath (Wairau fault), Saxton River (Awatere fault), Tophouse Road (Clarence fault) for different radii
of observation (r=50, 60, 70, 80, 90, 100, 120, 150, 200 km).
To summarize, the CoCo values obtained at Hokuri Creek and the four MFS slip-rate sites allow us to
quantitatively compare the degree of structural complexity of the plate-boundary fault networks
surrounding the southeastern section of the Alpine fault and the faults in the Marlborough fault system.

30
These results indicate that the Hokuri Creek site is embedded within a structurally simpler tectonic network
than any of the four MFS sites. Additionally, among these four sites, we observe slightly different levels of
complexity due to the relative proximity to the fastest-slipping fault of the MFS (i.e., the Hope fault).
2.3.2. The San Andreas fault system, California
The San Andreas fault (SAF) is a 1000-km-long, right-lateral transform fault that accommodates up to
70% of the total ~49 mm/yr of Pacific-North America relative plate motion in California (Sieh, 1978; Sieh
and Jahns, 1984; DeMets et al., 1994; Bennett et al., 1996). The 300-km-long central section of the SAF is
notably single-stranded, with several well-constrained slip rates of ~ 35 mm/yr(Sieh, 1978; Sieh and Jahns,
1984; Noriega et al., 2006; Salisbury et al., 2018; Grant-Ludwig et al., 2019). This central section is also
the most structurally isolated part of the SAF, with relatively few fast-slipping faults in the surrounding
fault network between the Pacific Plate to the west and the northern part of the eastern California shear
zone (ECSZ) to the east (Figure 2.2c). In contrast to the relative structural simplicity of the central SAF,
the northern and southern sections of the SAF system are notably multi-stranded and are embedded within
more structurally complex active tectonic settings. Specifically, in northern California, the SAF system
consists of three main strands, from west to east, the SAF proper, the Hayward-Rodgers Creek-Maacama,
and the Calaveras-Green Valley-Bartlett Springs faults. The plate-boundary fault network surrounding the
southern SAF is even more complex, consisting of three fast-slipping dextral faults (SAF, San Jacinto, and
Elsinore) that interact with six other large, sub-parallel strike-slip faults to the west, as well as numerous
reverse and sinistral-reverse faults in the Transverse Ranges. Adding to this network complexity are the
mainly right-lateral faults of the ECSZ, which splay northward off the southern SAF, and the fast-slipping,
left-lateral, east-west-striking Garlock fault, which intersects the SAF at its western end near the Big Bend.

31
2.3.2.1. Study sites: Van Matre-Wallace Creek, Wrightwood, and Quincy
We focus our CoCo analysis of the SAF system study on three sites: one from the structurally isolated
central section of the SAF, one from the southern SAF, and one located on the San Jacinto fault (SJF). The
latter two are located in a region characterized by much more structural complexity in the surrounding
plate-boundary fault network. To illustrate the effect on incremental slip of the simple tectonic setting of
the central SAF, we combined data from the closely spaced Van Matre Ranch and Wallace Creek study
sites of Sieh and Jahns (1984), Noriega et al. (200), and Grant-Ludwig et al. (2019) (Figure A.2a). In our
analysis, we chose a point in the middle of the 15-km-long trace of the SAF between these two sites.
Combining the data from these studies, Salisbury et al. (2018) calculated an average slip rate of 33.9±2.9
mm/yr over the last c. 4,000 years, with relatively little variation in rate amongst the three incremental time
intervals measured over this span. Additionally, Sieh and Jahns (1984) calculated a longer-term slip rate of
35.8 +5.4/-4.1 mm/yr averaged over the past 13,250 years that is similar to the aforementioned shorter-term
slip rates.
Conversely, the Wrightwood site, located on the Mojave section of the southern SAF in an area of
maximum plate-boundary structural complexity, exhibits markedly non-constant incremental slip rate over
the past 1,500 years (Weldon et al., 2004). Although paleoseismologic data demonstrate that the SAF at
Wrightwood exhibits quasi-periodic earthquake recurrence, the slip displacement per event is highly
variable, leading to a variable slip rate during the past 15 surface ruptures along this part of the SAF (Figure
A.2b). In particular, the slip rate averaged between 600 and 900 A.D. was ~89 mm/yr, more than three
times faster than the ~24 mm/yr average rate since c. 900 C.E. (Weldon et al., 2004).
The Quincy site, located in the Claremont section of the SJF, is embedded within a structurally
complex region, due in part to its proximity to the fast-slipping SAF, ~30-35 km to the east. The Quincy
site exhibits slip-rate behavior that we would not have expected for this part of the plate-boundary system,
as discussed below. Although the record spans only a short time range of 1,800 years and ~25 m of fault

32
slip, and thus may not encompass a long enough time range to record a reliable estimate of variability (i.e.,
some of the incremental rates are based on ≤ 3 earthquakes), the available data suggest a relatively constant
slip rate ranging between ~12 and ~16 mm/yr. Onderdonk et al. (2015a) reported a slip-rate record over the
1,800-year-long record ranging from 12.8 to 18.3 mm/yr, but in this entire study, we only consider slip rates
spanning three or more events, accounting for the slightly different maximum-possible rate we use (Figure
A.2c).
2.3.2.2. Coefficients of Complexity of Van Matre-Wallace Creek, Wrightwood and Quincy
As shown in Figure 4, we calculated the CoCo values for circular areas surrounding the Van MatreWallace Creek, Wrightwood and Quincy sites. For simplicity’s sake, we represent only the 100-km-radius
circles on Figure 4a for the Van Matre-Wallace Creek and Wrightwood sites, showing which faults, colorcoded by their respective slip rates, are included within this specific area. We observe that the CoCo value
of the region surrounding the Van Matre-Wallace Creek site is, for any radius of observation, much lower
than the values obtained for the Wrightwood and Quincy sites (Figure 2.4b).
Figure 2.4: CoCo results for the San Andreas fault system. (a) Active fault map of California with faults
color-coded by slip rate (see Appendix A.3 for references). Circles of 100 km radius are shown around the
Wrightwood (red) and Van Matre-Wallace Creek (blue) sites to illustrate which surrounding faults are
included in the CoCo calculation for an area of r=100 km. (b) Histogram of the CoCo values obtained at
the Quincy, Wrightwood, and Van Matre-Wallace Creek sites for different areas of observation (r=50, 60,
70, 80, 90, 100, 120, 150, 200 km).

33
For the SAF sites (Van Matre-Wallace Creek and Wrightwood), the CoCo values increase from a 50
km to a 120 km radius, and then decrease up to a radius of 200 km. The ratio CoCoWrightwood/CoCoVan MatreWallace Creek within the 50-km-radius circle is by far the highest at ~1400, and levels off to between 7 and 2
beyond r=70 km. The most striking contrast in tectonic complexity for both SAF sites is thus for the
observation area of 50 km radius. Indeed, the very low CoCo value (~0.3×10-2 mm/yr/km) for the Van
Matre-Wallace Creek site within this small area of observation is easily explainable by (i) the absence of
fast-slipping active faults on the western side of this portion of the San Andreas at this distance, (ii), slow
slip rates along the active blind thrust fault system along the western edge of the San Joaquin Valley east
of the SAF, and (iii), the absence of active faults across most of the width of the San Joaquin Valley.
Increasing the radius of observation around the Van Matre-Wallace Creek site encompasses additional
active faults of the southern Coast Ranges and western Transverse Ranges, including major contractional
structures in the Ventura and the Los Angeles basins, as well as the western part of the fast-slipping Garlock
fault. This trend of increasing CoCo with larger radius of observation extends to r=120 km, at which point
the CoCoVan Matre-Wallace Creek reaches its highest value of 5.6×10-2 mm/yr/km. At larger radii (r=150 and 200
km), greater areas of relatively low density of active faulting are included, such as the central San Joaquin
Valley and the westernmost part of the Mojave Desert. Similarly, CoCoWrightwood is highest for r=120 km,
reaching 18.8×10-2 mm/yr/km, because at this radius the measurement includes most of the active faults of
the southern Transverse Ranges and of the ECSZ, as well as long sections of the relatively fast-slipping
Elsinore and San Jacinto faults. Larger areas of observation (>120km radii) around Wrightwood do
encompass parts of the Great Valley fault system, the southernmost part of the Coast Ranges, as well as
larger portions of fast-slipping strike-slip faults including the Garlock, Elsinore, and San Jacinto faults, but
also include less tectonically active areas, such as the westernmost Mojave Desert and the southern San
Joaquin Valley, which reduces the CoCo values.
For the SJF’s Quincy site, we observe very high CoCo values decreasing from 36.6×10-2 mm/yr/km to
13.9×10-2 mm/yr/km with increasing radius of observation. These very high values, up to four times higher

34
than the ones obtained for Wrightwood, are mainly due to the proximity of Quincy site to the fast-slipping
SAF located ~30 km to the east, for any CoCo measurement radius.
These results show that the Van Matre-Wallace Creek site is embedded within a much less structurally
complex tectonic network than the Wrightwood site. The CoCo values for the Quincy site are actually
higher than those calculated at the Wrightwood site because large portions of the fast-slipping SAF are
integrated in the calculation of CoCoQuincy
.
2.3.3. The North Anatolian fault, Turkey
Unlike the two settings described above, the North Anatolian fault (NAF) in Turkey has relatively few
well-constrained incremental slip-rate studies that span long intervals of Holocene-latest Pleistocene time.
This 1500-km-long, right-lateral strike-slip fault, which extends from the Karliova triple junction in eastern
Turkey westward across northern Turkey and into the Aegean Sea, forms the boundary between the
Eurasian plate to the north and the Anatolian plate to the south (Barka, 1992). The NAF is broadly arcuate
in shape, subparallel to and about 80-90 km south of the Black Sea coast. This fault system consists, over
much of its length, of a single, isolated, structurally mature fault that extends from near its eastern end
westward for ~870 km to the Mudurnu Valley at 31°W (Figure 2.2d). Rate data from the central NAF
indicate that the fault accommodates almost all of the ~25 mm/yr of relative plate motion in this region
(Hubert‐Ferrari et al., 2002; Reilinger et al., 2006; Kozacı et al., 2007, 2009; DeVries et al., 2017). Although
other faults occur in this section of the plate boundary, they exhibit relatively slow slip rates. West of the
Mudurnu Valley, the NAF splays into multiple, sub-parallel strands, with the northernmost strand being the
fastest-slipping, main NAF strand in northwestern Turkey. The subsidiary NAF strands to the south slip at
much slower rates (Armijo et al., 1999). In addition to the North Anatolian fault strands, the area to the
south of the NAF system in western Turkey contains numerous active normal faults associated with
extension of western Anatolia and the Aegean region, adding to the structural complexity of this part of the
plate boundary (e.g., Hancock and Barka, 1987; Topal et al., 2016; Kent et al., 2017). We consider three

35
sites along the NAF: one along the relatively structurally isolated west-central part of the fault, one near the
area where the NAF splays westward into two main fault strands, and one from the main, northern NAF
strand within the more structurally complex plate boundary in northwestern Turkey (Figure 2.2d).
2.3.3.1. Relative steadiness of slip rates on the North Anatolian Fault
Slip-rate studies along the central part of the NAF at a range of Holocene-late Pleistocene time scales
suggest that the average rate is ~18-21 mm/yr (Hubert‐Ferrari et al., 2002; Kozacı et al., 2007, 2009). These
rates are generally similar to, but slightly slower than, decadal geodetic slip-deficit rates of 20-25 mm/yr
(McClusky et al., 2000; Reilinger et al., 2006; DeVries et al., 2017). The similarity in geodetic slip-deficit
and geologic fault slip rates indicates that almost all (80-95%) of the relative plate motion in north-central
Turkey is accommodated along the NAF itself, demonstrating that this section of the NAF is one of the
most structurally isolated major strike-slip faults in the world. Specifically, although there are faults other
than the NAF in this part of the plate boundary, they collectively accommodate only a few mm/yr of relative
plate motion. Paleoseismic and historical records indicate that the central and eastern NAF typically
ruptures in infrequent, but relatively regular large-magnitude earthquakes (Hartleb et al., 2003, 2006;
Okumura et al., 2003; Kondo et al., 2004, 2010; Kozacı et al., 2011). For example, detailed paleoseismic
records from the closely spaced Demir Tepe (Kondo et al., 2004, 2010a; Figure 2.2d) and Ardiçli trench
sites (Okumura et al., 2003) on the west-central part of the NAF reveal very low coefficients of variation
(CoV) of <0.2 for earthquake recurrence - indicating quasi-periodic earthquake behavior - and for slip per
event - revealing relatively characteristic displacement per event for this fault. The relatively regular
recurrence of earthquakes at these sites, combined with consistent offset values of 4-6 m in each event,
indicates a relatively constant incremental slip rate on the structurally isolated west-central NAF during late
Holocene time (Kondo et al., 2010).
About 60 km west of the Demir Tepe (Kondo et al., 2004, 2010) and Ardiçli (Okomura et al., 2003)
trench sites on the west-central NAF, the fault splays westward into two sub-parallel strands. The Düzce

36
segment is part of the northern strand westward from that point, where the NAF system becomes
progressively structurally relatively more complex. A slip-rate record developed for this segment by Pucci
et al. (2008) revealed relatively constant slip rates of ~15 mm/yr over three time intervals since 60 ka. West
of this point, the NAF system is everywhere multi-stranded, in marked contrast to the generally singlestranded, structurally isolated central part of the fault (Figure 2.2d).
In northwestern Turkey, near where the NAF extends westward into the Aegean Sea, the entire width
of the NAF system spans a north-south distance of ~100 km, with at least three significant sub-parallel
strands (Armijo et al., 2002; Flerit et al., 2003; Le Pichon et al., 2014; Şengör et al., 2005). In this part of
the plate boundary, the northern strand accommodates most of the relative plate motion. Specifically, along
the Ganos segment of the NAF’s northern strand northwest of the Sea of Marmara, Meghraoui et al. (2012)
documented relatively regular slip rates of the main, northern strand of the NAF near Güzelköy, with values
of ~17 mm/yr for the last 1,000 years and ~15 mm/yr for the last 2,500 years. At shorter time scales,
Rockwell et al. (2009) used paleoseismic and historical records of earthquake timing, combined with
detailed analysis of slip in the past two earthquakes, to infer relatively regular earthquake recurrence and a
typical fault displacement of ~4.5 m per event. Over longer time scales, Kurt et al. (2013) documented
progressive offset of the Çinarcik basin at 105
-year time scales along the NAF’s northern strand in the Sea
of Marmara to show that the slip rate of this strand has been relatively constant at ~18.5 mm/yr since 500
ka. These rates demonstrate that this strand is the fastest-slipping of the NAF strands in northwestern
Turkey. In contrast to the single-stranded and structurally isolated central NAF, however, the northern
strand of the NAF in northwestern Turkey is surrounded by a much more complex plate boundary, with
the central and southern strands to the south (Armijo et al., 1999; Armijo et al., 2002; Flerit et al., 2003), as
well as a network of generally EW normal faults in western Turkey and the adjacent Aegean region (Figure
2.2d). Although all of these faults have slow (< 1 mm/yr) to moderate (3-5 mm/yr) slip rates relative to the
much-faster slipping northern NAF strand, they collectively accommodate a much higher percentage of the
relative plate motion than the faults surrounding the structurally isolated central NAF.

37
2.3.3.2. Coefficients of complexity of Demir Tepe (west-central Gerede NAF segment), Düzce
(Düzce NAF segment) and Güzelköy (western NAF Ganos segment)
We calculate the CoCo values of three NAF sites: the Demir Tepe site located along the relatively
structurally isolated west-central portion of the fault, the Düzce site located on the eponymous segment,
and the Güzelköy site on the Ganos segment, which extends through the more structurally complex fault
network in northwestern Turkey (Figure 2.5). The Güzelköy site exhibits higher CoCo values, at all scales
of observation, ranging from 1.9×10-2
to 6.2×10-2 mm/yr/km, than those calculated at the Demir Tepe site,
where CoCo values are between 0.8×10-2
and 2.4×10-2 mm/yr/km. The values obtained for the Düzce site
are intermediate, and range from 1.9×10-2
to 3.4×10-2 mm/yr/km. Apart from the radius of observation r=50
km, the CoCoDüzce values are lower than the CoCoGüzelköy values (Figure 2.5b). It is worth noting, however,
that even though the CoCo values are higher at Güzelköy than at the two other sites, the Güzelköy values
are still low relative to the other plate boundary fault examples we described earlier in this paper. For
instance, the CoCoGüzelköy values are generally similar to those calculated for the structurally isolated central
section of the San Andreas fault at the Van Matre Ranch-Wallace Creek sites. There is no particular trend
observed for CoCoDemir Tepe values relative to the radii of observation, up to 150 km, and the same
observation holds true for CoCoGüzelköy, with values stabilizing around 6×10-2 mm/yr/km for radii between
70 and 150 km. For r=50 km to 70 km, CoCoGüzelköy increases due to the inclusion of larger portions of the
southern strand of the NAF, which extends sub-parallel to, and 20 to 100 km south of, the main northern
strand of the NAF in this region. Beyond a radius of 70 km, circles include other moderately fast-slipping
faults (≤4 mm/yr) of western Turkey, but also a number of faults in northwestern Turkey and southeastern
Bulgaria, which exhibit generally low levels of seismic activity. For the same reasons, there is an overall
slightly decreasing trend with increasing radius for CoCoDüzce, due to the incorporation, with larger radii, of
more active faults located in the western part of the plate boundary, in particular the southern strand of the
NAF.

38
Figure 2.5: CoCo results for the North Anatolian fault system. (a) Active fault map of Turkey with faults
color-coded by slip rate (see Table A.2 in Appendix A). Circles of 100 km radius are shown around the
Ganos, Güzelköy, and Demir Tepe sites to illustrate which surrounding faults are included in the CoCo
calculation for an area of r=100 km. (b) Histogram of the CoCo values obtained at Demir Tepe, Düzce, and
Ganos Güzelköy sites, for different radii of observation.
Overall, the CoCo values observed for the three NAF sites are quite low, especially if compared to the
SAF and Alpine fault system examples described above. This indicates that overall the NAF plate boundary
is a relatively simple fault system, even in its most complicated northwestern reaches. We can nevertheless
draw significant quantitative comparisons between the three sites. Specifically, the CoCo values show that
the NAF system gets slightly more complex westwards, with the lowest CoCo values in the central, isolated
portion of the NAF, intermediate CoCo values close to point at which the NAF splays westward into
multiple, sub-parallel strands, and the highest CoCo values along the westernmost section of the main,
northern strand of the western NAF.
2.3.4. The Dead Sea fault, Middle East
The Dead Sea fault (DSF) is the left-lateral strike-slip boundary that accommodates most of the
deformation between the Arabian plate to the east and the African-Sinai sub-plate to the west, as well as
their differential convergence relative to Eurasia (e.g., Freund et al., 1970; McKenzie et al., 1970; Garfunkel
et al., 1981; Marco and Klinger, 2014). The DSF extends northward for 1,000 km from the northern Red

39
Sea spreading center (Gulf of Aqaba) to the southernmost section of the East Anatolian fault in southeastern
Turkey (e.g., Garfunkel and Ben-Avraham, 1996; Ben-Avraham et al., 2008; Weber et al., 2009).
The Dead Sea transform plate boundary becomes progressively more structurally complex northward.
In the south, the fault is essentially a single strand that accommodates most of the relative plate motion,
whereas farther north, in southern Lebanon, it splays northward into two major and several minor subparallel left-lateral strands within a large restraining bend. Indeed, along the southern section of the plate
boundary, the DSF is the only major relatively fast-slipping fault accommodating relative plate motion,
making this stretch of the fault one of the most structurally isolated plate-boundary strike-slip faults
anywhere in the world (Galli, 1999; Marco and Klinger, 2014). As Figure 2.2e shows, this section of the
fault is surrounded by only a few faults, all of which are either inactive or slow-slipping, that generally do
not extend more for than ~20 km. Farther north, in the Jordan River Valley, the DSF plate boundary system
includes several slow-slipping (< 1 mm/yr), oblique-normal, NW-SE-striking faults to the west of the main
trace. North of the Jordan Valley, the DSF is embedded within the approximately 170-km-long Lebanese
restraining bend (e.g., Hancock and Atiya, 1979; Griffiths et al., 2000; Gomez et al., 2003), where it splays
northward into the main Yammouneh and Serghaya faults, as well as three other secondary faults, the
Rachaya, Hasbaya and Roum faults (e.g., Nemer and Meghraoui, 2006, 2020; Nemer et al., 2008). The
level of structural complexity in this region is increased by the presence of reverse faults of the Palmyra
fold belt to the east (Beydoun, 1999; Garfunkel et al., 1981; Quennell, 1984), and the offshore Mount
Lebanon thrust system to the west (e.g., Elias et al., 2007; Gomez et al., 2007; Figure 2.2e).
2.3.4.1. Slip rates on the Dead Sea fault: southern and central sections
Unlike the three plate-boundary fault systems discussed above, the Dead Sea transform is a rather
slow-moving plate boundary, with relative motion rate estimates ranging from 3 to 6 mm/yr(Klinger et al.,
2000; Niemi et al., 2001; Daëron et al., 2004a, 2007; Ferry et al., 2007, 2011). We base our CoCo analyses
on data from two sites on the DSF: one site located in the northern Araba Valley (Klinger et al., 2000;

40
Niemi et al., 2001), along the structurally isolated southern portion of the Dead Sea fault, and the other at
Beteiha (Wechsler et al., 2018), on the central DSF north of the Sea of Galilee, 40 km south of the junction
with the Roum, Rachayia, Serghaya and Yammouneh fault branches (Figure 2.6a).
Figure 2.6: CoCo results for the Dead Sea fault system. (a) Tectonic map of the Middle East with faults
color-coded by slip rate (see Table A.3 for fault database compilation). Circles of 100 km radius are shown
around the northern Wadi Araba (blue) and Beteiha (red) sites to illustrate which surrounding faults are
included in the CoCo calculation for an area of r=100 km. (b) Histogram of the CoCo values at these two
sites, for different radii of observation. Note that because the rate of relative plate motion along the Dead
Sea transform system is much slower than that for the other three plate boundaries we discuss, the CoCo
values along the DSF are not directly comparable to those from the other three plate boundaries. See text
for discussion.
The incremental slip rates documented by Klinger et al. (2000) and Niemi et al. (2001) in the northern
Araba Valley suggest a relatively constant slip rate for this structurally isolated section of the DSF. Klinger
et al. (2000) documented an average slip-rate estimate of 4 ± 2 mm/yr over the last 100 ky with four
individual slip-rate increments that range between ~1.8 mm/yr and 4.8 mm/yr. Similarly, Niemi et al. (2001)
estimated a slip rate of 4.7 ± 1.3 mm/yr since 15 ka. In contrast to the relatively constant incremental slip
rate along the southern DSF, analysis of the incremental slip rate of the central DSF at the Beteiha site
reveals a more irregular slip rate. Specifically, Wechsler et al. (2018) produced an incremental slip-rate
record spanning the last 3,500 years, exhibiting variability amongst individual incremental rate increments
ranging from ~3 mm/yr to >8 mm/yr over different time spans (Figure A.4).

41
2.3.4.2. Coefficients of complexity of the Northern Wadi Araba (southern section) and the Beteiha
site (central section)
Figure 2.6b illustrates the CoCo results obtained for the northern Wadi Araba and the Beteiha sites.
The slow relative motion of the Dead Sea transform boundary confers low CoCo values to the two sites, as
they remain below 1.4×10-2 mm/yr/km. These CoCo values are much lower than the CoCo values discussed
above for the other three plate boundary fault systems. This is a result of the very low relative plate-motion
rate along the DSF boundary, relative to the much-faster relative plate boundary rates for the three other
plate boundaries we discuss, all of which are characterized by relative plate boundary velocities of >15
mm/yr, which is more than twice or thrice the motion rate for the Dead Sea transform boundary.
Comparison of the two DSF sites reveals markedly contrasting results: CoCoBeteiha is between four and
eleven times higher than CoCoNorthern Wadi Araba, for all radii of observation. CoCoNorthern Wadi Araba decreases
with radius from 0.26×10-2
to 0.10×10-2 mm/yr/km. This is because the only active faults that surround the
southern section of the DSF are located very close to it, meaning that with larger radii of observation, wider
tectonically inactive areas are included in the CoCo calculation. Conversely, CoCoBeteiha does not follow
any particular trend. The two maxima in the histogram are for r=70 km and r=150 km, at ~1.3×10-2
mm/yr/km. The 70-km-radius circle includes significant lengths of the Roum, Rachaya, Serghaya, and
Carmel faults, while spanning a relatively small area. On the other hand, the 150-km-radius circle not only
encompasses the previously cited faults, but also includes the entire Damascus thrust fault, thought to be
slipping at > 2 mm/yr (Abou Romieh et al., 2012), as well as a large part of the Mount Lebanon offshore
thrust system, with slip-rate estimates ranging between 0.5 and 1.5 mm/yr.
These results numerically illustrate the different structural complexities surrounding both the southern
portion of the Dead Sea fault and the central one. They quantitatively corroborate that the Wadi Araba
Valley is embedded in a much less structurally complex tectonic network than the Beteiha site.

42
2.4. Comparison of CoCo and slip-rate variability
In Figure 2.7, we compile all of the observations of both the relative steadiness of incremental fault slip
rates and the relative structural complexity of the fault network surrounding each site for all sites from the
four plate-boundary fault systems that we have examined. All CoCo values are summarized in Table A.4.
In order to compare study sites from different plate boundary settings that exhibit different relative plate
motion rates, we “standardized” the CoCo values by dividing them by the respective total plate motion rate
at the site of interest (Table 2.1). The resulting unit of this standardized coefficient is the inverse of a length
(km-1
), which, although rather an arcane unit, effectively allows direct comparisons of the densities of fault
activity in the plate boundary fault network surrounding each measurement site standardized for the relative
plate-motion rate at each site.

43
Table 2.1: Plate motion rates used to standardize CoCo values, and extreme slip rate values inferred from available slip-rate records at all sites from
which the slip-rate variability is inferred (slip rate (SR) variability equals the ratio between the highest incremental slip rate of the record and the
lowest one). See Table A.5 and Table A.6 for uncertainties and comments, and Figure A.1 to Figure A.4 for visualization of published incremental
slip-rate records.
Fault
system
Study site References for slip rate record
Plate motion rate
(mm/yr)
References for
plate motion rate
AlpineMFS
Saxton River Zinke et al. (2017) 39
DeMets et al.
(1994)
Branch River-Dunbeath Zinke et al. (2021) 39
Tophouse road Zinke et al. (2019) 39
Hossack station Hatem et al. (2020) 39
Hokuri Creek Berryman et al. (2012) 39
SAF
Wrightwood Weldon et al. (2004) 49
DeMets et al.
(1994)
Quincy Onderdonk et al. (2015) 49
Van Matre-Wallace
Creek
Sieh & Jahns (1984); Noriega et
al. (2006); Grant-Ludwig et al.
(2019); Salisbury et al. (2018)
39 for r=100km; 150 km
42 for r = 200 km
NAF
Güzelköy Meghraoui et al. (2011) 27
DeVries et al.
(2017)
Düzce Pucci et al. (2008) 21
Demir Tepe Kondo et al. (2010) 21
DSF
Wadi Araba Valley 1 Niemi et al. (2001) 7
DeMets et al.
(1994)
Wadi Araba Valley 2 Klinger et al. (2000) 7
Beteiha Wechsler et al. (2018) 7

44
Table 2. 1 - continued
Fault system Study site
Fastest incremental
slip rate of the record
(mm/yr)
Slowest incremental
slip rate of the record
(mm/yr)
Slip rate variability
(fastest SR/slowest SR)
Alpine-MFS
Saxton River 16.80 1.40 12.00
Branch River-Dunbeath 20.90 1.40 14.93
Tophouse road 9.60 2.00 4.80
Hossack station 32.70 8.20 3.99
Hokuri Creek 26.96 21.01 1.28
SAF
Wrightwood 87.96 13.06 6.74
Quincy 16.01 12.00 1.33
Van Matre-Wallace
Creek
34.00 29.30 1.16
NAF
Güzelköy 17.00 7.10 2.39
Düzce 15.34 13.02 1.18
Demir Tepe 17.00 16.90 1.01
DSF
Wadi Araba Valley 1 4.72 2.83 1.67
Wadi Araba Valley 2 4.80 1.77 2.71
Beteiha 8.86 3.10 2.86

45
In addition, for each of the available published incremental slip-rate records, we calculated a slip-rate
variability metric by dividing the fastest incremental slip rate of the record by the slowest. The resulting
variability metric provides a measure of the relative steadiness or unsteadiness of incremental slip rate along
each of the fault we examined. If the slip-rate variability metric is close to 1, the fault is slipping at nearly
the same rate all the time (at least during the time intervals spanned by the available incremental slip-rate
records). Conversely, the slip-rate variability metric becomes much higher than 1 for faults that exhibit
highly variable incremental slip rates through time.
Our results show that sites that have been characterized by irregular slip-rate behavior generally
correspond to areas of higher structural complexity, whereas sites that exhibit steady slip rate through time
occur where the surrounding fault network is less complex (Table 2.1). As detailed above, the CoCo values
for different sites are quite sensitive to the detailed distribution of faults within each plate boundary,
especially at small CoCo measurement radii (e.g., 50 km), suggesting that larger measurement radii provide
a better measure of the relative structural complexity around a site of interest. We tried to find the optimal
measurement radius or range of measurement radii to explore the correlation between standardized CoCo
values and slip-rate variability, and therefore used the three largest CoCo measurement radii (100, 150, and
200 km) to test this correlation. For simplicity’s sake, in Figure 2.7 we show only the plot of the
standardized CoCo values against the slip-rate variability for only r=100 km (plots for r=150 km and 200
km are shown in Figure A.6). The respective 1-σ uncertainties are displayed in Table A.5, and a similar
summary plot displaying the different durations of slip-rate records and the different numbers of increments
of slip rate in each incremental slip-rate record is available in Figure A.5.
To determine whether the standardized CoCo values and the slip-rate variability are correlated, we
performed the non-parametric test of Spearman’s rank correlation, under the null hypothesis H0 that there
is no correlation between the two parameters. We obtained low P-values for the test of H0 of 0.015, 0.016
and 0.029, for the three measurement radii 100, 150 and 200 km, respectively. In addition, we obtained
Spearman’s coefficients of ~0.6 for those three radii. The related regression line for r=100 km is displayed

46
in the summary plot of Figure 2.7. Those results enable us to reject H0 and thus to infer that the two
parameters are positively correlated.
There is significant scatter exhibited in Figure 2.7, which is perhaps to be expected for a distillation of
the behavior of complex natural fault systems from four different plate boundaries with different structural
geometries, local kinematics, and stress-evolution histories. We suspect that as more incremental slip-rate
records of the sort compiled in Figure 2.7 become available, the statistics of the regression will improve,
yielding increasingly useful predictive power for this type of analysis in the absence of detailed incremental
slip-rate data for a fault being considered in any PSHA, as discussed below.
Figure 2.7: CoCo values “standardized” by respective plate rate (i.e., CoCo value divided by relative platemotion rate; see Table A.5) plotted against slip-rate variability (i.e., highest slip rate of the respective record
divided by slowest slip rate of the record) for all sites, for a radius of 100 km. For the sake of visualization,
the slip-rate variability is shown on a logarithmic scale. The actual unit of the ratio CoCo/respective plate
rate is km-1
, which is a rather arcane unit that we chose not to display on the graph. North Anatolian fault

47
sites are indicated as: DT: Demir Tepe; Dü: Düzce; Gü: Güzelköy. Dead Sea fault sites are indicated as
AV1: Araba Valley (Niemi et al., 2001); AV2: Araba Valley (Klinger et al., 2000); B: Beteiha. San Andreas
fault system sites are shown as: VM: Van Matre-Wallace Creek; Q: Quincy; W: Wrightwood. The AlpineMFS sites are indicated as: HC: Hokuri Creek; HS: Hossack Station; TR: Tophouse Road; SR: Saxton
River; BR: Branch River-Dunbeath. Dashed arrows indicate how the slip-rate variability would evolve if
the current, open interseismic interval since the last earthquake were to extend into the future. The
regression line for the whole dataset is shown, suggesting a logarithmic relationship between the
standardized CoCo and the slip-rate variability.
In addition, we note that some of these records are for relatively brief periods of time that span rather
small cumulative displacements and few earthquakes (Table A.6). For example, for the Demir Tepe site,
the available slip-rate record only spans four earthquakes, which account for a small cumulative
displacement of only 19.9 ± 2.2 m. The record is nonetheless notable for the constancy of slip rate during
this four-earthquake interval, and we use a minimum slip-rate variability of 1 for this site (Table 2.1). The
Quincy site is the main outlier that emerges from our analysis. As described in section 2.3.3, the CoCo
values obtained for this site are expectedly high yet are related to a relatively constant slip rate, which is
characterized by a relatively low slip-rate variability of < 2. Given the high level of plate boundary structural
complexity surrounding the Quincy site, we would expect a more irregular slip-rate behavior for this section
of the SJF. We suggest that the rather short time range of the record (~1.8 ky) may not account for the full
variability range of the slip rate at that site, and that the averaged incremental slip-rate values that are used
from the available record (Onderdonk et al. 2015; Figure A.2c) may not encompass the full slip-rate
variability. In addition, the fastest rate we calculate at Quincy is based on the preferred three-event, 9.5 m
displacement occurring in the past ca. 600 years (i.e., we ignore the alternative, potential two-event
displacement suggested for this site as being based on too few earthquakes), which may miss variability in
the record.
Moreover, in several of the incremental records, the youngest slip rate, including the currently open
(ongoing) interseismic interval, is either the fastest or slowest incremental rate at that site, and thus
constrains the slip-rate variability values plotted in Figure 2.7. For sites at which the youngest slip rate is
the fastest (Güzelköy, Wadi Araba Valley, Quincy), the slip-rate variability will decrease as the current

48
open interval lengthens and extends farther into the future before the next earthquake. This would have the
effect of decreasing the slip-rate variability metric at that site. Conversely, for sites where the youngest
incremental rate is the slowest (Tophouse Road, Hossack Station, Beteiha), as the open interval extends
farther into the future, the slip-rate variability will increase, yielding a more variable incremental slip-rate
metric. These patterns are denoted on Figure 2.7 by dashed arrows indicating the future trend of the sliprate variability at each of these sites, and are summarized in Table A.6.
Finally, we note that while only two incremental slip rates measured over different multi-earthquake
intervals are necessary to provide an estimate of the slip-rate variability, this will result in a minimum
estimate of variability. Additional incremental rate measurements will potentially increase the degree of
slip-rate variability, and are thus preferred.
Despite these caveats, the regression through the currently available incremental slip-rate data reveals
a strong pattern of increasing slip-rate variability with increasing structural complexity of the surrounding
plate-boundary fault network.
2.5. Discussion
Our comparison of the calculated CoCo values with available incremental fault slip-rate records at
multiple sites along the four major strike-slip faults we analyzed reveals a clear correspondence between
the steadiness or irregularity of slip rate on the fault and the relative structural complexity of its surrounding
fault network. Specifically, as shown in Figure 2.7, sites that exhibit lower CoCo values along stretches of
the primary fault that extend through relatively simpler parts of the plate-boundary fault network exhibit
steadier incremental fault slip rates. Conversely, the temporal slip behavior of the primary fault at sites that
exhibit higher CoCo values within more structurally complicated parts of the plate boundary is much more
irregular. These results indicate that the relative structural complexity of the plate-boundary fault network
surrounding a fault exerts a basic control on the steadiness of fault displacement. Several studies have
previously suggested that steadier fault slip occurs within structurally simple plate boundary fault networks,

49
(e.g., Dolan et al., 2007; Kozacı et al., 2011; Berryman et al., 2012b), and here we show that the converse
is also true, that complex areas are characterized by irregular fault slip rates, as has been suggested by
modeling efforts (e.g., Chen et al., 2020), and by analysis of regional normal fault networks (e.g., Wallace,
1987; Cowie et al., 2017; Wedmore et al., 2017).
These observations raise the obvious question of why fault systems would exhibit this relationship
between structural complexity and steadiness of slip on individual faults. Most basically, in a mechanically
integrated fault network in the elastic upper crust, we suggest that any non-steady perturbation to the stress
evolution of the primary fault, either through Coulomb Failure Function (CFF) interactions related to
earthquakes generated by other faults (e.g., Harris and Simpson, 1992, 1996; Stein et al., 1992, 1997;
McAuliffe et al., 2013), or changes in the rate of elastic strain accumulation on the primary fault (e.g.,
(Peltzer et al., 2001; Dolan et al., 2007, 2016; Evans et al., 2017; Evans, 2018), might be expected to result
in more irregular behavior. In regions where there are few other faults, or only slow-slipping faults (as
illustrated in Figure 2.1a), in the surrounding fault network, such perturbations to steady behavior will likely
be fewer than in settings in which the primary fault is surrounded by numerous, rapidly slipping faults
(illustrated in Figure 2.1b). In the latter settings, each of the numerous and/or fast-slipping faults in the
surrounding fault network will produce its own earthquakes, with attendant complications of the evolution
of the stress field on the primary fault.
Beyond the scale of individual earthquakes, several authors have suggested that system-level
interactions amongst the faults in a plate boundary can exert important controls on either the rate of elastic
strain accumulation and/or the resistance to shear on individual faults. For example, (Dolan et al., 2007)
suggested that the Pacific-North America plate boundary in southern California comprises two
mechanically complementary sub-systems that alternate periods of faster and slower overall slip. Such
kinematic interactions require accelerations and decelerations of faults or fault sub-systems, suggesting that
either regional CFF stress changes that suppress or enhance the likelihood of slip on entire fault networks
vary with time, depending on which sub-system is active, and/or that fault strength (i.e., resistance to shear)

50
in either the upper crustal brittle part of the fault zone, or its underlying ductile shear zone root, must vary
with time (Dolan et al., 2007, 2016; Dolan and Meade, 2017; Hatem and Dolan, 2018). In normal fault
intraplate settings, Wallace (1987) originally noted that the behavior of the various faults in the Basin and
Range province of the western United States exhibited long-term changes in slip rate across the entire
system, which he attributed to migration of fault activity from the main range boundary to sub-parallel
faults. Similarly, Cowie et al. (2017) and Wedmore et al. (2017) have suggested that the behavior of normal
faults within the central Apennines system is governed by coseismic Coulomb stress changes amongst the
various faults that compose the system. Specifically, Wedmore et al. (2017) show that interseismic time
intervals on 97 Apennine faults fluctuate widely over the course of 30 earthquakes across the region. In
addition, they demonstrate that fault length plays an important role in the effects of fault interaction, and
that, in particular, shorter faults are generally more sensitive to coseismic Coulomb changes, possibly due
to less localized ductile deformation down-dip of smaller faults. Whatever the exact mechanisms that cause
these behaviors, our analysis demonstrates that the CoCo methodology provides a useful metric with which
to characterize the steadiness, or unsteadiness, of incremental fault slip that can be directly related to the
structural complexity of the system.
In addition to potentially helping to provide a deeper understanding of the controls on how fault systems
accommodate relative plate motions in time and space, calculation of the CoCo metric could be useful in
probabilistic seismic hazard analysis (PSHA). Specifically, we suggest that the CoCo metric could be used
to determine the expected variability of incremental slip rate on a target fault in a predictive sense. As
discussed in detail in the following, this information could be useful in two ways. First, by providing better
estimates of potential error limits of faults under consideration that are constrained by only a single slip rate
estimate over a single time and displacement range, and second, by pointing out regions of high CoCo
values (i.e., structural complexity) within which faults will likely exhibit highly variable slip rates that need
to be considered in PSHA inputs.

51
One of the most basic inputs into any PSHA is the slip rate of the fault (e.g., Stirling et al., 2012; Field
et al., 2015, 2017). Although almost all such probabilistic seismic hazard analyses rely on this datum, a
vexing question remains: What is the “correct” slip rate to use in the probabilistic analysis? This question
is particularly important when trying to determine the hazard posed by faults that could potentially exhibit
wide variations in incremental slip rate. Specifically, for faults with highly variable slip rates, if the specific
incremental slip rate chosen to use as an input into the analysis is not representative of the current behavior
of the fault, the resulting estimate of probabilistic hazard will not provide a useful prediction of the nearfuture hazard. For example, if the available slip rate for a fault is averaged over some relatively brief time
interval, and the slip rate of the fault has been relatively fast over that time span, the use of that rate in the
PSHA would result in an overestimate of the probabilistic hazard. Conversely, if the slip rate used in the
analysis is averaged over a period that happened to coincide with a period of anomalously slow slip on the
fault, the use of that single average rate would potentially result in an underestimate of the hazard.
As originally suggested by Van Dissen et al. (2020), there are three possible strategies for incorporating
incremental slip-rate data into PSHA: (a) use the long-term average slip rate, ignoring any incremental rate
changes; (b) use the full error range associated with all available incremental slip rates; and (c) use the most
recent incremental slip rate as the most appropriate rate. Each of these has potential advantages in improving
the accuracy of PSHA, as well as potential drawbacks.
The first option, (a), is the method generally used currently in PSHA. Although this approach has the
advantage of being straightforward and simple to implement, and indeed is the only option in the many
cases where only a single fault slip rate averaged over a single time interval through the present is available,
it risks missing significant changes in shorter-term rate that may be more indicative of the near-future
behavior of the fault. Since the CoCo analysis indicates that such changes in incremental rate are more
likely along faults embedded within more structurally complex systems, the use of a single slip rate
averaged over a single time interval will be particularly problematic in such settings, and should warrant
use of larger error limits on the slip rate used as an input into the PSHA for that fault. Conversely, our CoCo

52
analysis provides confidence that along structurally isolated, fast-slipping strike-slip faults, the specific
incremental slip rate used in the PSHA is less likely to significantly affect the probabilistic hazard, since
along such faults slip rates will be relatively constant through time, regardless of the interval over which
they are averaged (except for intervals averaged over only a few earthquakes, which might depart
significantly form the overall average rate).
The second approach would be to use the full range of incremental rates as the PSHA slip-rate input
error range. Although this extremely conservative approach would have the benefit of covering the entire
range of slip-rate variation observed along the fault, it would, in cases of faults that exhibit wide variations
in incremental slip rate, result in an inordinately large possible range of slip rates that will, in turn, result in
commensurately imprecise hazard assessments. The CoCo results indicate that such situations are likely to
be particularly common for faults that extend through structurally complex fault systems. For example, in
the case of the Wairau fault, which exhibits a long-term (13 ka) average rate of 4.9 ± 0.6 mm/yr, the
incremental slip-rate record (with each incremental rate interval spanning at least a millennium), displays
a ca. 4-ky-long period of slow slip (0.45 mm/yr in Figure A.1c) and a much faster period of slip rate of 15.2
+23.10/-6.20 mm/yr (Zinke et al., 2021), resulting in a possible slip range of ~0 to 38.3 mm/yr (considering a
1-σ uncertainty). While it is unlikely to be “wrong”, in the sense that it will likely include the near-future
slip rate of the fault, this large error range would not provide much resolving power on the likely future
behavior of the fault.
A third approach would be to use the youngest multiple-event incremental slip rate, with the reasoning
being that the recent behavior of the fault is most likely to predict the near-future behavior of the fault. A
recent example of the potential utility of this approach is illustrated by the 2019 Mw = 7.1 Ridgecrest
earthquake, which occurred with the eastern California shear zone (ECSZ) in an area that had previously
been suggested to be in a mode of anomalously rapid elastic strain accumulation and release (Peltzer et al.,
2001; Dolan et al., 2007, 2016; Evans et al., 2017; Hatem and Dolan, 2018).

53
Could such estimates be further constrained, perhaps by controlling for the probability that the youngest
incremental rate could be about to (or has already) switch(ed) into a faster mode of strain release? We
suggest the following possible approach. In cases where a sufficiently detailed incremental slip-rate record
is available (e.g., the Wrightwood record on the San Andreas fault (Weldon et al., 2004), or the Saxton
River from the Awatere fault in New Zealand (Zinke et al., 2017)), the difference between consecutive
incremental rates may be indicative of the propensity of a particular fault to switch either abruptly, or more
gradually, between fast and slow modes of strain release. For example, as shown in the hypothetical
incremental slip-rate records in Figure 2.8, the difference between two consecutive incremental slip rates
reflects the ability and propensity of a fault to rapidly change its behavior. Figure 2.8a shows a hypothetical,
well-populated incremental slip-rate record that displays consecutive incremental rates that do not change
abruptly from one measurement to the next: the largest variation in adjacent incremental rates is only a
factor of four times (3.3 to 0.75 mm/yr) in this scenario. In contrast, Figure 2.8b shows more abrupt changes
from one increment to the next, with the largest variation in consecutive rates being a factor 27 times (16.0
to 0.6 mm/yr). Hence, any forecasting method applied to incremental slip-rate record may strongly depend
on the level of abruptness between two slip-rate increments. Documentation of the relative abruptness of
change in consecutive rates depends, however, on the resolution of the different slip-rate increments that
compose the slip-rate record (Figure 2.8a versus Figure 2.8c). For example, Figure 2.8a and Figure 2.8c
show hypothetical representations of the same slip-rate record with different levels of detail in incremental
rates. The hypothetical record shown in Figure 2.8c is the same as that shown in Figure 2.8a, but populated
with fewer data. In Figure 2.8c, the incremental rates vary by a factor of 14 times relative to a total variation
of only four times for the same record shown in Figure 2.8a. Nevertheless, even sites with relatively few
incremental slip-rate intervals may provide some indication of the likelihood that a fault has a high
propensity of a rapidly changing slip rate that could be useful as an additional input into any PSHA.

54
Figure 2.8: Three different scenarios illustrating potential abruptness of transitions in variable incremental
slip rates and the impact of incremental slip-rate records based on relatively few data. (a) Hypothetical,
well-populated incremental slip-rate record in which adjacent incremental rates do not change abruptly
from one measurement to the next. The largest variation in adjacent incremental slip rates in this scenario
is only a factor of four times (0.75 to 3.3 mm/yr). (b) Hypothetical incremental slip-rate record in which
adjacent incremental rates do change abruptly from one increment to the next. The largest variation in
adjacent rates in this scenario is 27 times (0.6 to 16.0 mnm/yr). (c) Same overall incremental slip-rate record
as shown in (a) but populated with fewer data. The resulting incremental rates vary by a factor of 14 times
relative to variations in incremental rate in (a) of only four times.

55
What about settings where the fault under consideration does not have a documented record of
incremental slip rate? Could the CoCo analysis be useful even in such instances? The comparison of CoCo
results with available incremental slip-rate records shown in Figure 2.7 suggests that this might be possible.
Specifically, as detailed above, a regression through all of the currently available data suggests that we can
propose a range of values for the expected variability of slip rate on a fault even in the absence of a detailed
incremental slip-rate record on that fault, if at least an estimate value of the slip rate of the fault in question
is available. This expected range could, in turn, be used as an input into the PSHA, allowing more accurate
estimation of the error ranges that should be considered on whatever average slip-rate data are available for
the considered fault. Despite the preliminary nature of the compilation shown in Figure 2.7, we suggest that
as more incremental slip-rate data become available for more faults, the reliability and robustness of the
regression will improve, providing the beginnings of potential analyses that can constrain in a predictive
sense the likely variability of fault slip through time, even in the absence of detailed incremental slip-rate
data for that particular fault.
All of the options discussed above for the use of incremental slip-rate data could be used as separate
branches in a PSHA logic-tree approach, with each weighted according to the specifics of the available data
and the previous likelihood of abruptly varying slip rate. Although the number of faults for which detailed
incremental slip-rate records are available is still relatively small, this number is rapidly increasing, and the
use of these data in both studies of fault system behavior and PSHA will only increase as more such records
become available.
Finally, as noted above, the CoCo metric provides a useful tool to document regions of high network
complexity. In turn, since faults in such regions are likely to exhibit highly variable slip rates, this highlights
areas where detailed incremental fault slip-rate data will be necessary to fully characterize the past and
likely future behavior of these faults.

56
2.6. Conclusions
Comparison of the CoCo metric, which we developed to quantify the structural complexity surrounding
a site located on a fault, with incremental slip rates at multiple sites along the major faults within four wellknown strike-slip plate boundaries (Alpine, San Andreas, North Anatolian, and Dead Sea fault systems),
indicates that portions of these faults that extend through relatively structurally simple plate-boundary fault
systems exhibit near-constant incremental slip rates. Conversely, faults that extend through structurally
complex plate-boundary fault networks exhibit much more irregular incremental fault slip rates. This study
thus reinforces the idea that system-level interactions amongst the faults in a plate boundary exert a
fundamental control on either the rate of elastic strain accumulation and/or the resistance to shear on
individual faults, with more complex fault systems related to more complicated and irregular incremental
fault slip-rate behavior. The relationship between the steadiness or irregularity of incremental fault slip rate
and the relative structural complexity of the surrounding fault network revealed by the CoCo analysis
suggests that use of the CoCo metric could contribute to next-generation PSHA as a potential additional
input that better accounts for slip-rate variability. For example, along faults that exhibit high CoCo values
indicative of structurally complex settings and irregular incremental slip-rate behaviors, the slip rates used
as basic inputs into PSHA will require careful consideration of the proximity, geometry, and slip rates of
faults in the fault system surrounding the fault under consideration to determine the optimal slip-rate range
to be used. In contrast, incremental slip rates on structurally isolated faults that exhibit low CoCo values
are less likely to vary from the average estimate, no matter the duration over which the slip rate is averaged
(as long as if it is longer than three earthquakes), suggesting that slip rates averaged over any time scales
longer than a few earthquakes will likely reflect the near-future slip rate of the fault in such settings.
Although our CoCo analysis is based on the relatively few incremental slip-rate records that are
currently available on the four major faults we considered, this analysis provides the beginnings of a useful
means of evaluating the slip-rate variability of a fault through time, even without available incremental sliprate data for that particular fault. As an increasing number of well-constrained incremental slip-rate records

57
become available, the relationship between fault system structural complexity, as defined by the CoCo
metric, and incremental slip-rate behavior will come into ever clearer focus, providing more robust
estimates of the future behavior of major faults. Finally, we note that although this initial CoCo analysis
was confined to strike-slip faults, the basic methodology should prove to be equally useful in any other
tectonic regime.

58
CHAPTER 3 Comparison of geodetic slip-deficit and geologic fault slip rates
reveals that variability of elastic strain accumulation and release rates on
strike-slip faults is controlled by the relative structural complexity of plateboundary fault systems
This chapter is based on the following published article:
Gauriau, J., & Dolan, J. F. (2024). Comparison of geodetic slip-deficit and geologic fault slip rates reveals
that variability of elastic strain accumulation and release rates on strike-slip faults is controlled by the
relative structural complexity of plate-boundary fault systems. Seismica, 3:1.
3.1. Abstract
Comparison of geodetic slip-deficit rates with geologic fault slip rates on major strike-slip faults reveals
marked differences in patterns of elastic strain accumulation on tectonically isolated faults relative to faults
that are embedded within more complex plate-boundary fault systems. Specifically, we show that faults
that extend through tectonically complex systems characterized by multiple, mechanically complementary
faults (that is, different faults that are all accommodating the same deformation field), which we refer to as
high-Coefficient of Complexity (or high-CoCo) faults, exhibit ratios between geodetic and geologic rates
that vary and that depend on the displacement scales over which the geologic slip rates are averaged. This
indicates that elastic strain accumulation rates on these faults change significantly through time, which in
turn suggests that the rates of ductile shear beneath the seismogenic portion of faults also vary through time.
This is consistent with models in which mechanically complementary faults trade off slip in time and space
in response to varying mechanical and stress conditions on the different component faults. In marked
contrast, structurally isolated (or low-CoCo) faults exhibit geologic slip rates that are similar to geodetic
slip-deficit rates, regardless of the displacement and time scales over which the slip rates are averaged. Such
faults experience relatively constant geologic fault slip rates as well as constant strain accumulation rate

59
(aside from brief, rapid post-seismic intervals). This suggests that low-CoCo faults “keep up” with the rate
imposed by the relative plate-boundary condition, since they are the only structures in their respective plateboundary zone that can effectively accommodate the imposed steady plate motion. We hypothesize that the
discrepancies between the small-displacement average geologic slip rates and geodetic slip-deficit rates
may provide a means of assessing a switch of modes for some high-CoCo faults, transitioning from a slow
mode to a faster mode, or vice versa. If so, the differences between geologic slip rates and geodetic slipdeficit rates on high-CoCo faults may indicate changes in a fault’s behavior that could be used to refine
next-generation probabilistic seismic hazard assessments.
3.2. Introduction
Unravelling the relationship between geologic fault slip rates and rates of strain accumulation as
measured by geodesy is critically important for developing a better understanding of the mechanics of faults
and the seismic hazards that they pose. Whereas some major faults exhibit constant behavior, with relatively
steady geologic slip rates spanning a range of time and displacement scales (e.g., Kozacı et al., 2009, 2011;
Berryman et al., 2012b; Salisbury et al., 2018; Grant-Ludwig et al., 2019), other faults exhibit highly
irregular slip rates through time, with centennial to millennial periods of relatively fast slip rate spanning
multiple earthquake cycles, separated by prolonged periods of slower or no slip rate (Benedetti et al., 2002;
Friedrich et al., 2003; Bull et al., 2006; Dolan et al., 2016, 2023; Zinke et al., 2017, 2019, 2021; Hatem et
al., 2020).
Elastic strain accumulation rates inferred from analysis of geodetic data reflect the shearing velocity of
the seismogenic faults’ underlying ductile roots, and have been suggested to be relatively constant beyond
the single-earthquake scale (i.e., once fast post-seismic and slower interseismic rates have been averaged
out). Indeed, comparisons of geodetic slip-deficit and geologic rates have been used to infer near-constant
interseismic rates. For example, in one of the largest such compilations to date, Meade et al. (2013)
compared geologic fault slip rates and geodetic slip-deficit rates for 15 major continental strike-slip faults

60
around the world. Their results suggest that, as an ensemble, these faults exhibit a near 1:1 relationship
(with a slope of 0.94 ± 0.09) between geologic and geodetic rates. Slight differences between the datasets
could be attributable to short-lived periods of higher-than-average strain accumulation during the postseismic period. The geologic rates used as inputs into the analysis of Meade et al. (2013) span a huge range
of displacement and time scales, from as small as ~13 m to as large as ~600 m, and as short as 2 ky to as
long as 160 ky. We recently presented results that demonstrate that, for faults that lie within complex plateboundary fault networks, geologic slip rates vary depending on the displacement scale over which the slip
rate is estimated; on the other hand, structurally isolated faults that accommodate most of the relative motion
within simple plate boundaries exhibit steadier slip rates (Gauriau and Dolan, 2021). These observations
lead us to explore the possibility that differences between geodetic slip-deficit rates and geologic slip rates
might also be sensitive to the relative complexity of the surrounding fault network. If they are, this would
require that geodetic-geologic rate comparisons consider time and displacement scales over which
incremental slip rates are averaged, as well as the relative structural complexity of the surrounding fault
system, especially in structurally complex plate boundaries (e.g., northern and southern California,
Marlborough fault system in New Zealand), that are characterized by multiple, mechanically
complementary faults.
In this paper, we explore the potential constancy, or lack thereof, of the elastic strain accumulation rate
patterns on active strike-slip faults. Specifically, we aim to investigate the relative constancy and potential
variability of elastic strain accumulation rates on faults characterized by temporally constant geologic slip
rates, on the one hand, and faults that exhibit temporally variable geologic slip rates, on the other.
Comparing elastic strain accumulation rates derived from geodesy with geologic slip rates has been done
in several studies (Kozacı et al., 2009; Meade et al., 2013; Tong et al., 2014; Dolan and Meade, 2017; Evans
et al., 2017) but never in light of the relative complexity of the plate-boundary fault systems being
considered.

61
3.3. Studied faults and terminology
In this study, we use the recently developed Coefficient of Complexity (CoCo) method (Gauriau and
Dolan, 2021), which quantifies the relative structural complexity of the fault network surrounding a fault
of interest by integrating the density and displacement rates of the faults in the plate-boundary network at
a specific radius (here, 100 km) around the site of interest. The method is illustrated in Figure 3.1. We use
CoCo values calculated for 18 major strike-slip faults for which both geologic incremental slip-rate records
and geodetic slip-deficit rates are available (Figure 3.2, Table 3.1). In total, we work with 24 different fault
sites where these two kinds of data are available and approximately collocated. The comparison of the
CoCo values for all sites is then enabled by the standardization of the CoCo values by the respective platemotion rate, totaled for the observation area of 100 km radius. This allows direct comparisons of the
intensity of fault activity in different plate-boundary fault networks that move at different relative plate
motion rates.

62
Figure 3.1: Schematic explanation of the rationale of the Coefficient of Complexity (CoCo) analysis for a
hypothetical fault network. The calculation of CoCo for a given radius is shown on top. The radius over
which CoCo is calculated is 100 km. Within a structurally complex fault system (numerous, and relatively
fast-slipping faults), shown to the left, the CoCo value will be higher than within a structurally simple fault
system (few or zero neighboring faults), shown to the right. The quantification of complexity, done with
the CoCo analysis, correlates with the relative steadiness of geologic slip-rate record, as shown in our recent
study (Gauriau and Dolan, 2021).

63
Table 3.1: Summary of data from the different fault sections used in this study, including small-displacement (SD), large-displacement (LD)
averaged geologic slip rates (in mm/yr) with corresponding time and displacement ranges over which they are averaged, and geodetic slip-deficit
rates (in mm/yr). The rate values are reported as they were in their original source publications, unless specified otherwise.
Fault Section/Site #
SD
slip
rate
Time
range of
SD slip
rate (ky)
Displacement
of SD slip
rate (m)
References
for SD slip
rate
LD slip
rate
Time
range
of LD
slip
rate
(ky)
Displacement
of LD slip rate
(m)
References for
LD slip rate
Geodetic
rate
References
for geodetic
rate
Plate
rate
References
Garlock Central 1
14
+2.2
/-
1.8
1.9 26
+3.5
/-2.5
Dolan et al.
(2016)
8.8 ± 1.0
8.0 ±
0.9
70 ± 7
Fougere et al.
(2023)
2.61 ±
3.00
Evans (2018)
49
Dolan et al. (2016);
McGill and Sieh
(1993); Evans
(2017)
San
Andreas
Mojave 2
28.8
+1.5
/-
0.8
1.007
+0.028
/-0.050
~ 29
(Weldon et al.,
2004; Dolan et
al., 2016)
30.9
+2.9
/-2.5
1.49 ±
0.13
46
Weldon et al.
(2004)
15.12 ±
2.78
49
Weldon et al.
(2004); Dolan et al.
(2016); Evans
(2017)
Carrizo Plain 3
31.6
+9.0
/-
6.6
0.38 ±
0.06
12 ± 1
Salisbury et al.
(2018)
36 ± 1 ~ 3.5 128 ± 1
Grant-Ludwig et
al. (2019)
35.65 ±
5.11
39
Grant-Ludwig et al.
(2019); Salisbury et
al. (2018); Sieh and
Jahns (1984);
Noriega et al. (2006)
San Jacinto Claremont 4
12.8
-
18.3
2.05 ±
0.12
25 - 30
Onderdonk et
al. (2015)
13.18 ±
4.61
49
Onderdonk et al.
(2015), Evans
(2018)
Owens
Valley
5
0.5-
2.1
§
Haddon et al.
(2016) and
references
therein
2.8-4.5 55-80 235 ± 15
Kirby et al.
(2008)
2.71 ±
1.38
12
Kirby et al. (2008),
Haddon et al.
(2016); Evans
(2017)
Calico Central 6 1.6 ± 0.2
650 ±
100
900 ± 200
Oskin et al.
(2007)
7.42 ±
3.44
49
Oskin et al. (2004,
2008); Evans (2017)

64
Table 3. 1 – continued
Fault Section/Site #
SD slip
rate
Time
range of
SD slip
rate (ky)
Displacement
of SD slip
rate (m)
References
for SD slip
rate
LD slip
rate
Time range of
LD slip rate
(ky)
Displacement
of LD slip rate
(m)
References
for LD slip
rate
Geodetic
rate
References
for
geodetic
rate
Plate
rate
References
Hope Conway 7 8.2
+5.4
/-3.0 ca. 1.1 12 ± 2
Hatem et al.
(2020)
15.2
+2.2
/-2.4 ca. 13.8 210 ± 15
Hatem et al.
(2020)
5.8
+1.8
/-1.1
Johnson et
al. (2024)
39
Hatem et al.
(2020);
Johnson et
al. (2024)
Wairau
Branch River
Dunbeath
8 4.5 ± 1.0 * 3.3 ± 0.4 15 ± 2.6
Zinke et al.
(2021)
4.9 ± 0.4 11.9
+1.0
/-0.8 58.5 ± 2
Zinke et al.
(2021)
2.8
+2.4
/-0.8
Zinke et al.
(2021);
Johnson et
al. (2024)
Clarence
Tophouse
Road
9 2.0 ± 0.4 4.5
+0.8
/-0.7 9.0 ± 1.0
Zinke et al.
(2019)
4.2 ± 0.5 11.2 ± 1.3 47.0 ± 3.0
Zinke et al.
(2019)
8.6
+1.4
/-1.1
Zinke et al.
(2019);
Johnson et
al. (2024)
Awatere Saxton River 10 4.2
+1.2
/-1.0 1.8 ± 0.3 9.5 ± 1.0
Zinke et al.
(2017)
5.6
+0.8
/-0.6 12.9
+1.2
/-1.0 72.5 ± 7.5
Zinke et al.
(2017)
1.9
+2.2
/-0.8
Zinke et al.
(2017);
Johnson et
al. (2024)
Alpine Southern 11 29.6
+4.5
/-2.5 270 8000
Barth et al.
(2014)
29.1
+1.1
/-
3.2
Berryman et
al. (2012);
Page et al.
(2018);
Wallace et
al. (2012)

65
Table 3. 1 – continued
Fault Section/Site #
SD slip
rate
Time
range of
SD slip
rate (ky)
Displacement
of SD slip
rate (m)
References
for SD slip
rate
LD slip
rate
Time range of LD
slip rate (ky)
Displacement
of LD slip
rate (m)
References
for LD slip
rate
Geodetic
rate
References
for
geodetic
rate
Plate
rate
References
Dead Sea
Wadi Araba
Valley
12 3.8 - 6.1 2 - 4.2 13.2 ± 1.0
Klinger et
al. (2000)
4 ± 2 140 ± 31 300-900
Klinger et
al. (2000)
5.0 ± 0.2
Gomez et
al. (2020)
7
Klinger et al.
(2000); Niemi
et al. (2001);
Hamiel et al.,
(2018)
Beteiha 13 3.5 ± 0.2
$ 1.472 5.2 ± 0.3
Wechsler et
al. (2018)
4.8 ± 0.3
Wechsler et al.
(2018);
Masson et al.
(2015)
Yammouneh 14 3.5 - 7.5 6 - 10 40 ± 5
Daëron et al.
(2004)
2.7-7.3 12 - 27 80 ± 8
Daëron et
al. (2004)
2.5 ± 0.5
Daëron et al.
(2004); Gomez
et al. (2003,
2007)
Queen
Charlotte
15 52.9 ± 3.2 17 ± 0.7 900 ± 40
Brothers et
al. (2020)
46.3 ± 0.6
Elliott and
Freymuller
(2021)
55
Brothers et al.
(2020); Elliott
and
Freymueller,
(2020)
Denali
Central 16 12.1 ± 1.7 12.0 ± 1.3 / 11.9 ± 1.3
# 144 ± 14
Matmon et
al. (2006)
7.0 ± 0.3 17
Matmon et al.
(2006); Elliott
and Freymuller
(2021); Bender
et al. (2023)
Western 17 10.4 ± 3.0 2.4 ± 0.3 25
+5
/-7
Matmon et
al. (2006)
9.4 ± 1.6 16.8 ± 1.8 158 ± 14
Matmon et
al. (2006)
7.75 ± 0.3 17
Matmon et al.
(2006); Elliott
and Freymuller
(2021); Bender
et al. (2023)

66
Table 3. 1 – continued
Fault Section/Site #
SD slip
rate
Time range
of SD slip
rate (ky)
Displacement
of SD slip
rate (m)
References
for SD slip
rate
LD slip
rate
Time range
of LD slip
rate (ky)
Displacement
of LD slip
rate (m)
References
for LD slip
rate
Geodetic
rate
References
for
geodetic
rate
Plate
rate
References
Altyn
Tagh
Central 18 9.4 ± 0.9
¤
5.889 – 5.658 54 ± 5 Cowgill (2007) 9.4 ± 2.3 16.6 ± 3.9 156 ± 10
Cowgill et
al. (2009)
9 ± 4
Bendick et
al. (2000)
11.2
Cowgill (2007);
Cowgill et al.
(2009); Bendick
et al. (2000);
(Shen et al.
(2001; Zhang et
al., (2007); He
et al., (2013)
Kunlun
Central
Western
19 10.7 ± 2.2 2.885 ± 0.285 31 ± 2
Haibing et al.
(2005)
10.6 ± 1.8 5.96 ± 0.450 63 ± 5
Haibing et
al. (2005)
11.3 ± 3.5
Zhao et al.
(2022)
12
Van Der Woerd
et al. (2002);
Haibing et al.
(2005) Kirby et
al. (2007)
Haiyuan Laohushan 20 3.7 ± 0.6 9 - 11 32 - 42 Liu et al. (2022) 4.8 ± 0.2 15 - 17 73 - 79
Liu et al.
(2022)
5.6
+1.3
/-1.1
Daout et al.
(2016)
6.5
Li et al. (2009);
Liu et al.
(2018); Shao et
al. (2020)
North
Anatolian
Demir Tepe
Eksik
21 16.8 ± 0.1 * 0.988 15.3 ± 0.1
Kondo et al.
(2010)
20.5 ± 5.5 2 - 2.5 46 ± 10
Kozacı et
al., (2007)
20.5
DeVries et
al. (2017)
21
Hubert-Ferrari
et al. (2002);
Kozacı et al.,
(2007)
Tahtaköprü 22 18.6
+3.5
/-3.3 ~ 3 55 ± 10
Kozacı et
al. (2009)
21.2 - 21.5 21
Kozacı et al.
(2009)
Northern /
Ganos
23 15 ± 6 2.5 ± 0.5 35.4 ± 1.5
Meghraoui et
al. (2012)
18.5
+10.9
/-5.9 490 ± 100 >~ 8000
Kurt et al.
(2013)
28.6 27
Meghraoui et al.
(2012); Kurt et
al. (2013)
East
Anatolian
Pazarcık,
Tevekkelli
24 5.6 ± 0.3 17.8 101 ± 5
Yönlü and
Karabacak
(2023)
10.3 ± 0.6
Aktug et al.
(2016)
10
Reilinger et al.
(2006);
Güvercin et al.
(2022)
* rate calculated between MRE and given offset marker
§ based on several studies cited in Haddon et al. (2016), with offsets ranging from 3 m (1 earthquake) to 19 m, and respective ages ranging from 600 years ago and 25 ka.
$
averaged over the past four historical earthquakes
#
first age relates to boulder samples, second age refers to sediment samples (
10Be technique)
¤ using their upper-terrace reconstruction (Cowgill et al., 2009), as for the small-displacement slip rate

67
We divide the available geologic slip-rate data into two groups: large-displacement slip rates and smalldisplacement slip rates (usually referred to as “long-term” and “short-term” slip rates, respectively), which
are averaged over large (> ~50 m) and small (< ~50 m) displacements, respectively (Table 1). The reasons
for this are twofold: (a) This allows us to discuss fast- and slow-slipping faults with comparable parameters
and hence by considering similar numbers of earthquakes on faults that have widely different recurrence
intervals, and (b) displacement, not time, may be what matters most in terms of the mechanisms governing
fault behavior in complex plate-boundary fault systems (Dolan et al., 2007, 2023; Cawood and Dolan, in
revision). In addition, we use the terms “geodetic slip-deficit rates” to refer to any rate that was obtained
on the basis of space geodetic measurements of surface ground displacement over multi-annual to decadal
time scales, such as Global Positioning System (GPS) or Interferometric Synthetic Aperture Radar (InSAR),
and which has been modeled to characterize the most recent rate of elastic strain accumulation for the
studied strike-slip faults.
3.4. Consideration of elapsed time since most recent event relative to sampling geodetic
slip-deficit rates
In order to evaluate potential differences in behavior of faults embedded within structurally simple fault
systems (i.e., low-CoCo faults) versus faults embedded within structurally complex fault networks (i.e.,
high-CoCo faults), we compare geodetic slip-deficit rates with geologic fault slip rates that are averaged
over both small displacements and large displacements. We first introduce a few key considerations that
allow us to carry out this comparison between geodetic and geologic data.
The interseismic geodetic data used in this paper may derive from different sampling times throughout
the earthquake cycle. Although we have no precise control over where exactly the examined faults lie in
their elastic strain cycles, we can in most instances document the elapsed time since their most recent event
(MRE), as well as an estimate of their mean earthquake recurrence interval. For a majority of the faults we

68
study, it has been at least 100 years since the MRE, as documented historically (e.g., the 1717 Alpine fault
earthquake, the 1857 Fort Tejon earthquake on the San Andreas fault, the 1872 Owens Valley earthquake)
or on the basis of paleoseismological evidence (e.g., the ca. 1800-1840 CE earthquake on the Conway
section of the Hope fault; Hatem et al., 2019). In a few instances, the MRE occurred more recently, such as
the series of earthquakes on the North Anatolian fault between 1939 and 1999 (Barka, 1992; Barka et al.,
2002), the 2002 Denali earthquake (Haeussler et al., 2004), or the Kahramanmaraş earthquake (e.g., Barbot
et al., 2023) that occurred in Februray 2023 on the East Anatolian fault (for which we use a geodetic rate
that was acquired before the earthquake).
Table B.1 summarizes the MRE dates and the available mean recurrence intervals for the fault locations
we study. In most of the examples, we are well into at least the middle part of the elastic strain accumulation
cycle, likely well past any rapid post-seismic deformation (with the possible exceptions of the 1992
Landers, 1999 Izmit, 1999 Düzce, 1999 Hector Mine, and 2002 Denali earthquakes).
3.5. Relative structural complexity of the surrounding fault network in interpretation of
geodetic slip-deficit rate and geologic slip-rate comparisons
In our original formulation of the CoCo metric (Gauriau and Dolan, 2021), we categorized faults as
either low- or high-CoCo. To determine the CoCo metric for each fault study site, we apply a system in
which we recognize that the degree of structural complexity surrounding a fault is a continuum, with no
hard boundary between high- and low-CoCo faults. Whereas many of the faults we study can be readily
categorized as either high-CoCo faults (e.g., the Hope fault or the Mojave section of the San Andreas fault)
or low-CoCo faults (e.g., the southern Alpine fault, the central San Andreas fault), some of the faults exhibit
intermediate CoCo values reflecting a surrounding plate-boundary zone that shows minor to moderate
complexity. The two faults that fall in this in-between area are the Central Denali fault (16), characterized
by a standardized CoCo value of 1.62·10-2 yr-1
and the Altyn Tagh fault (18), characterized by a

69
standardized CoCo value of 1.56·10-2 yr-1
. Based on these two values, we use a standardized CoCo value
of 1.6·10-2 yr-1
as the dividing line between what we will refer to hereafter as low- and high-CoCo faults.
With this boundary defined, we can explore the behaviors exhibited by these two categories of faults, as
shown in Figure 3.2b, c (see Figure 3.3 for standardized CoCo values of all faults).
3.6. Comparison of geologic slip rates and geodetically based slip-deficit rates on strikeslip faults
Figure 3.2 illustrates the comparison between geologic and geodetic slip-deficit rates for the 24
different sites on the studied strike-slip faults. It reveals marked differences in the consistency of the values
of the geodetic/geologic-rate pairs for high-CoCo faults relative to low-CoCo faults. Specifically,
comparison of geodetic slip-deficit rates with large-displacement and small-displacement average geologic
slip rates (displayed as squares and circles, respectively, in Figure 3.2) reveals that these rates are similar
for faults characterized by low CoCo values (displayed in blue in Figure 3.2), whereas they differ for the
faults characterized by high CoCo values (displayed in red in Figure 3.2). This observation is a corollary to
the main conclusion of our previous study (Gauriau & Dolan, 2021), in which we showed that low-CoCo
faults slip at relatively constant rates through time whereas high-CoCo faults exhibit long-term slip rates
that are potentially different from the slip rates averaged over small displacements. In other words, the
displacement over which the slip rate is averaged does not matter for low-CoCo faults, since any geologic
slip rate will give the same value. In contrast, geologic slip rates for high-CoCo faults that are averaged
over one particular displacement range may differ from the slip rate averaged over a different displacement
range.

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Figure 3.2: Geodetic slip-deficit rate and geologic slip-rate comparisons for major strike-slip faults. The geologic rates are shown as either averaged
over a large displacement, or over a small displacement. The data points are color-coded according to their respective values of the Coefficient of
Complexity (CoCo), standardized by the plate rate contained within a 100 km radius, as defined in Gauriau and Dolan (2021). The strike-slip faults
considered in this study are: (1) Garlock, (2) San Andreas, Mojave segment, (3) San Andreas, Carrizo Plain segment, (4) San Jacinto, Claremont
segment, (5) Owens Valley, (6) Calico, (7) Hope, (8) Wairau, (9) Clarence, (10) Awatere, (11) Alpine, (12) Dead Sea, Wadi Araba Valley, (13)
Dead Sea, Beteiha, (14) Yammouneh, (15) Queen Charlotte, (16) Denali, central section, (17) Denali, western section, (18) Altyn Tagh, (19) Kunlun,
(20) Haiyuan, (21) North Anatolian, Demir Tepe, (22) North Anatolian, Tahtaköprü, (23) Northern North Anatolian, (24) East Anatolian, Pazarcık
(references listed in Table 1). (a) shows all the compiled faults in the same diagram. (b) shows all faults characterized by CoCo values that are less
than 0.0016 yr
-1
(referred to as low-CoCo faults). (c) shows all faults characterized by CoCo values that are more than 0.0016 yr
-1
(referred to as
high-CoCo faults).

71
Figure 3.2a shows a comparison of geologic slip rates and geodetic slip-deficit rates. Figure 3.2b shows
that low-CoCo strike-slip fault sites plot on (or near) the 1:1 line, reflecting the similarity of their shortterm geodetic strain accumulation rates and both their small-displacement and large-displacement geologic
strain-release rates. This can be further illustrated statistically, since the coefficient of determination
obtained from an ordinary least squares regression for the low-CoCo faults is 0.983 for geologic rates
averaged over large displacements, and 0.978 for geologic rates averaged over small displacements (Figure
B.1). Assuming a linear relationship between geologic slip rates and geodetic slip-deficit rates going
through the origin, we find scaling lines with best-fit slopes and respective 1σ confidence of 0.945 ± 0.028
and 1.103 ± 0.050 for the low-CoCo faults using the large-displacement and small-displacement average
geologic rates, respectively (see Figure B.1a, b). These results show that for these low-CoCo faults, geodetic
rates provide a reliable proxy for the geologic slip rate of the fault of interest.

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Figure 3.3: Variations of geodetic to geologic slip-rate ratios against CoCo values standardized by plate
rate over a 100 km radius. (a) Ratios of geodetic slip-deficit rate to geologic rate plotted against CoCo. The
geologic rate values are averaged over large or small displacement (as in Figure 3.2). The numbering of the
fault sites is referred to in Figure 3.2 and Table 3.1. The dashed arrows refer to a ratio of geodetic/geologic
rate that would reach infinity, with a geological rate close or equal to 0 mm/yr, if the fault has not slipped
for a long time since the MRE (see text for details). (b) Diagram showing the dispersion of the ratio
(geodetic to geologic rates) values varying with the CoCo values. The higher the CoCo value, the more
scattered the data (i.e., the farther from the 1:1 ratio line they tend to plot). The measure of the dispersion
is detailed in Appendix B. Although we cannot calculate an exact CoCo value for the Queen Charlotte fault
(15), because of our inability to include all active faults within a 100 km radius of the slip-rate site, we
assign it a CoCo value of zero, since this fault accommodates >95 % of the total Pacific/North America
plate-motion rate (NUVEL-1A; DeMets and Dixon, 1999).

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That geodetic slip-deficit rates are a reliable proxy for geologic slip rate is not the case for high-CoCo
faults (Figure 3.2c). Specifically, there is wide dispersion amongst the geodetic slip-deficit and both largeand small-displacement geologic slip rates (Figure 3.3). This observation requires that geodetic slip-deficit
rates cannot be used as a proxy for geologic rates for high-CoCo faults, whether the rate is averaged over
small displacements or large displacements. For these high-CoCo faults, the coefficient of determination
obtained from an ordinary least squares regression between geologic rates and geodetic slip-deficit rates is
0.396 for geologic rates averaged over large displacements, and 0.350 for geologic rates averaged over
small displacements (Figure B.1c, d). Scaling lines between geologic rates and geodetic rates for these
faults, assuming a linear relationship going through the origin (as in Meade et al., 2013) are characterized
by the best-fit slopes of 0.751 ± 0.162, using the small-displacement geologic rates, and 0.696 ± 0.140,
using the large-displacement geologic rates (Figure B.1c, d). These linear regressions seem to imply a
global trend where geologic slip rates are faster than geodetic slip-deficit rates, but we suggest that these
best-fit slope values are not meaningful, and are rather artifacts of the current limited state of available data.
Reinforcing this idea is the observation that the dispersion of the data, shown by the standard deviations of
the best-fit slopes, demonstrates that there is no good correlation between geodetic slip-deficit and geologic
slip rates for high-CoCo faults. Figure 3.3 further illustrates this result, by displaying the ratio between the
geodetic slip-deficit rates and the geologic rates averaged over large or small displacements. Figure 3b plots
a measure of distance from the data points to the 1:1 ratio line with varying CoCo values, and emphasizes
the dispersion of the data for higher-CoCo faults (see details of the dispersion calculation in Appendix B);
the relatively sharp increase in dispersion at standardized CoCo ~0.0015-0.002 yr-1
likely reflects the
presence of major secondary faults that can accommodate significant portion of relative plate motions.

74
3.7. Fault loading rates…
3.7.1. …are constant on low-CoCo faults
Our analysis reveals that low-CoCo faults are characterized by geodetic rate/geologic rate ratios very
close to one, regardless of the displacement scale over which the geologic slip rate is measured (Figure 3.2,
Figure 3.3). Geologic slip rates estimated from offset landforms at widely different displacements are the
same for these faults, showing that the elastic strain release remains constant over the time intervals over
which these displacements have accumulated. Furthermore, the current elastic strain accumulation rate (as
constrained by the geodetic slip-deficit rate) is equal to strain release rates (as constrained by geologic slip
rates) at all measured displacement scales. This indicates that for these faults, the elastic strain accumulation
rate provided by the geodetic slip-deficit rate remains constant during the interseismic period (Figure 3.4),
following the short-duration periods of fast post-seismic deformation at the beginning of each cycle, as
originally noted by Meade et al. (2013).
3.7.2. …vary on high-CoCo faults
In contrast, high-CoCo faults, embedded within more complex structural settings, display no consistent
relationship between geodetic slip-deficit and geologic slip rates. As noted above, these results reinforce
the point that geodetic slip-deficit rates cannot be used as reliable proxies for geologic slip rates on highCoCo faults. Moreover, although the mismatch between geodetic slip-deficit rates and small-displacement
geologic slip rates could conceivably be due to short-term variations in fault slip rate, the mismatch between
geodetic slip-deficit rates and large-displacement geologic slip rates, which are averaged over >50 to
hundreds of meters of slip (see Table 3.1) and numerous individual earthquakes, and will thus average over
any shorter-term/smaller-displacement accelerations or decelerations of fault slip, indicates that elastic
strain accumulation rates on the high-CoCo faults must vary through time. Specifically, at these largedisplacement scales, the fault slip rate spanning numerous earthquakes will provide a robust estimate of the

75
average rate of strain release on that fault through time. Insofar as the elastic strain accumulation rate must
equal the elastic strain release rate (i.e., fault slip) over long time intervals, the mismatch that we document
between geodetic slip-deficit rates and geologic slip rates averaged over large displacements requires that
elastic strain accumulation rates as measured by geodetic slip-deficit rates must vary through time.
Further examination of the results displayed in Figure 3 helps us distinguish several types of behaviors
amongst the high-CoCo faults. Those behaviors can be defined depending on whether the geodetic slipdeficit rate is equal to, slower than, or faster than either the large-displacement average geologic rate, or the
small-displacement average geologic rate (Figure 3.4).

76
Figure 3.4: Observed modes of fault behavior, with time shown as the horizontal dimension of the block,
and with relative slip rate displayed with a color gradient. In (a), we show that whatever the time over which
its behavior is averaged, a low-CoCo fault’s slip rate is constant and thus equals its elastic strain
accumulation rate, as shown in the left hand-side, hence the same color at each point in time and in the
brittle and ductile parts of the fault. Note that we are not considering single-earthquake time scales. In
contrast, high-CoCo faults (b and c) exhibit several types of behaviors, as discussed in the text. In (b), we
illustrate a fault that has a short-term (small-displacement) geologic slip rate that is slower than its longterm (large-displacement) rate. For this fault, the current elastic strain accumulation (ductile shear of the
ductile roots) is slower than the short-term geologic slip rate, and therefore might be entering what we refer
to as a slow mode. In (c), we show another example of a fault whose long-term geologic slip rate is faster
than its short-term geologic slip rate. This fault is entering a fast mode since its elastic strain accumulation
is much faster than its short-term geologic slip rate.

77
These differences between geodetic and geologic rates reveal the following fundamental point: Faults
for which the current loading rate does not equal the average large-displacement geologic slip rate overly a
ductile shear zone that must be creeping at either a slower or a faster rate than the long-term average slip
rate. If, furthermore, the geodetic rate differs from the small-displacement rate, the rate of elastic strain
accumulation consequently has to vary over the same periods of accelerations and decelerations that are
averaged over in these small-displacement geologic rate values.
We suggest that using the mismatches between geodetic slip-deficit and small-displacement geologic
rates can help us infer the current behavior of the faults that may be most representative of the near-future
likelihood of major earthquake recurrence. Mismatches between elastic strain accumulation rates and smalldisplacement geological rates reveal three different modes for the high-CoCo faults. These are: faults that
are storing elastic strain energy more slowly than their small-displacement geologic slip rates; faults that
exhibit a current rate of elastic strain accumulation that is faster than the small-displacement geologic slip
rate; and faults in which the geodetic slip-deficit rate approximately equals the youngest average geologic
slip rate. In the following, we describe the details of the behavior of faults that fall within these three
categories and discuss a model that attempts to explain the observations in terms of faults switching from
one mode to another.
In the first case, geodetic slip-deficit rates are slower than the small-displacement (short-term) geologic
slip rates measured on these faults. The Garlock (numbered 1 in Figure 3.2Figure 3.3), the Mojave segment
of the San Andreas (2), Wairau (8), Hope (9), Awatere (10), and Yammouneh (14) faults are all
characterized by geodetic rate values that are slower than their respective geologic slip rates (both largeand small-displacement). For example, the central Garlock fault experienced a cluster of four large
earthquakes between 0.5 and 2.0 ka (Dawson et al., 2003), resulting in a small-displacement (26 m) slip
rate averaged over these four events through to the present of 14 +2.2/-1.8 mm/yr (Dolan et al., 2016).
Modeling of geodetic data consistently yields very slow rates of elastic strain accumulation on the central

78
Garlock fault, with a best estimate of ~2.6 mm/yr (Evans, 2018), potentially including almost no elastic
strain accumulation. In contrast, the large-displacement (long-term) slip rate averaged over the most recent
70 m of slip on this section of the Garlock fault is 8.8±1.0 mm/yr (Fougere et al., 2023, in revision). While
this is slower than the small-displacement geologic rate, it is at least three times faster than the current rate
of elastic strain accumulation. This mismatch suggests that the Garlock fault has recently entered into a
“slow” mode of elastic strain accumulation, likely as a result of a decreased shearing rate on the underlying
ductile shear zone. But why is the youngest, small-displacement rate so fast? We suggest that the switch in
behavior of the Garlock fault from the 0.5 – 2 ka “fast” mode ended with the final earthquake in the cluster,
either because the fault (including the upper seismogenic part and the ductile shear zone roots) strengthened
during the fast period encompassing the four-event cluster and became more difficult to slip (Dolan et al.,
2007; Cawood and Dolan, in revision), and/or because the Garlock fault has exhausted what Dolan et al.
(2023) refer to as the “crustal strain capacitor” (similar to Mencin et al.'s (2016) “strain reservoir”), that is,
the shear strain stored in the crust surrounding this section of the Garlock fault. In this view, the current slip
rate (or, equivalently in this context, the “most recent geologic slip rate”) of the Garlock fault since the
most recent earthquake (MRE) ca. 500 years ago has been 0 mm/yr, reflecting the current very slow rate of
elastic strain accumulation on the Garlock fault.
Similarly, the geodetic slip-deficit rate on the Wairau fault in New Zealand (2.8+2.4/-0.8 mm/yr; Johnson
et al., 2024) is slower than the small-displacement rate of 4.5±1.0 mm/yr (Zinke et al., 2021), calculated for
the preceding fast period of slip between a geomorphic offset dated at ca. 5.4 ka and the ca. 2 ka MRE. This
contrast highlights a period of fast slip on the fault during this time interval. Yet, 2,000 years have elapsed
since the MRE on the Wairau fault (relative to an average Holocene recurrence interval of ca. 1,000 years
(Nicol and Dissen, 2018)), which we suggest indicates a “most recent geologic slip rate” since the MRE of
0 mm/yr. Thus, the averaging of the small-displacement rate over the past 5,400 years through to the present
may be masking a switch of the Wairau fault from a fast mode between 2 and 5 ka, to the current slow
mode that has prevailed since the MRE at 2 ka. In both the Wairau and Garlock faults examples, if we were

79
to use the inferred most recent geologic slip rate of 0 mm/yr as the best representation of the smalldisplacement slip rate, the geodetic/small-displacement rate ratios would soar, as the dashed arrows in
Figure 3a illustrate.
Another example is the Yammouneh fault (14), which has a geodetic slip-deficit rate (2.5±0.5 mm/yr;
Gomez et al., 2020) that is much slower than its small-displacement slip rate (5.5±2.0 mm/yr; (Daëron et
al., 2004a) (Figure 3.3). The Yammouneh fault might therefore also be experiencing a slow mode since the
MRE in 1202 C.E. (Daëron et al., 2007; Table B.1).
Although the small-displacement slip-rate of the Hope fault (8.2 +5.4/-3.0 mm/yr; Hatem et al., 2020) is
likely faster than the geodetic slip-deficit rate estimate (5.8 +1.8/-1.1 mm/yr; Johnson et al., 2024), their
respective 2σ uncertainties overlap (Table 3.1), which does not allow us to strongly affirm a potential switch
of mode for this fault. However, the difference between these estimates might suggest that the Hope fault
is currently in a slower mode, and may have exhausted its strain capacitor in the past five earthquakes,
which generated 20-30 m of fault slip over the past ~1,500 years (Hatem et al., 2019, 2020). The thusreduced shear stress stored in the crust surrounding the Hope fault might explain the lack of significant slip
on the Hope fault in the 2016 Kaikōura earthquake sequence (e.g., Hamling et al., 2017), despite its
proximity to the faults that initially ruptured in the sequence. Indeed, both Ulrich et al. (2019) and Nicol et
al. (2023) have suggested that the lack of significant 2016 coseismic slip on the Hope fault could be due to
the low stresses in play across the Hope fault prior to the Kaikōura earthquake.
A final example is the Mojave section of the San Andreas fault (SAFm), which is characterized by an
elastic strain accumulation rate (15.1±2.3 mm/yr; Evans, 2018) that is much slower than its smalldisplacement slip rate (~27-29 mm/yr; Weldon et al., 2004; Dolan et al., 2016) (Figure 3.3, Figure 3.4,
Table 3.1). The MRE occurred 167 years ago on the SAFm, whereas the mean recurrence interval for this
stretch of the fault is about 100 years (e.g., Scharer et al., 2017). The absence of any earthquakes since the
1857 MRE led to much speculation in earlier decades, when some scientists suggested that the SAFm was

80
“overdue” (e.g., Weldon and Sieh, 1985). These early ideas of earthquake recurrence patterns were based
on the assumption of steady elastic strain accumulation rates. If, instead, elastic strain accumulation rates
vary, as we show here, then the long elapsed time since the 1857 earthquake may at least partially be a
consequence of reduced loading rates in this section of the SAF, as reflected in the current geodetic rate.
All of this suggests that the SAFm (2) may have entered a “quieter mode”.
A partial, potential alternative explanation for this situation was provided in Hearn et al., (2013) and
Hearn (2022), who suggested that some of this slow elastic strain deformation rate on the SAFm might be
due to a so-called “ghost transient” related to long-term visco-elastic relaxation of the lithospheric mantle
and lower crust following the 1857 Fort Tejon earthquake. However, this would only explain 5 mm/yr of
the apparent ~14 mm/yr difference between the geodetic slip-deficit rate and the small-displacement slip
rate. In marked contrast to the SAFm, Hearn et al. (2013) also noted that there is no such “ghost transient”
associated with the Garlock fault, which ruptured most recently in 1450-1640 CE (Dawson et al., 2003).
Our analysis reveals another type of behavior, in which faults exhibit geodetic slip-deficit rates that are
faster than their geologic slip rates. We suggest that these faults may have switched from a slow mode to a
fast mode. This behavior characterizes the Clarence fault (9), the northern Dead Sea fault (nDSF - 13), the
northern strand of the North Anatolian fault system (nNAF - 23), and the Pazarcık segment of the East
Anatolian fault (EAF – 24) (Figure 3.3). The Clarence fault (9) has a geodetic slip-deficit rate (8.6 +1.5/-1.1
mm/yr; Johnson et al., 2024) that is faster than both its small-displacement and large-displacement geologic
rates, although its small-displacement slip rate (2.0 ± 0.4 mm/yr) is half as fast as its large-displacement
slip rate (4.2 ± 0.5 mm/yr; Zinke et al., 2019). Similarly, the nDSF stores elastic strain energy at a rate of
4.8 ± 0.3 mm/yr (Gomez et al., 2020) and is characterized by a slower small-displacement slip rate of 3.5
± 0.2 mm/yr (Wechsler et al., 2018). For the nNAF, considering the large uncertainties on the largedisplacement geologic slip rate (18.5 +10.9/-5.9 mm/yr, measured over a 500 My time scale; Kurt et al., 2013),
we cannot confidently infer that it is slower than the reported geodetic slip-deficit rate (28.6 mm/yr; DeVries

81
et al., 2017), but we can more confidently state that the small-displacement geologic rate (15 ± 6 mm/yr;
Meghraoui et al., 2012) is slower that the geodetic rate, as suggested by Dolan and Meade (2017). The EAF
(24) has a geodetic slip-deficit rate (10.3 ± 0.6 mm/yr; Aktug et al., 2016) that is nearly twice as fast as the
available large-displacement geologic slip rate (5.6 ± 0.3 mm/yr; Yönlü and Karabacak, 2023). Notably,
this section of the EAF ruptured in the 2023 Mw = 7.8 Kahramanmaraş earthquake.
The Calico fault (6) may also fall within this type of behavior, with a switch from a previous slow mode
to a current faster mode. Although the data currently available for the Calico fault do not allow us to infer
a small-displacement slip rate, the current loading rate (7.4 ± 3.4 mm/yr; Evans, 2018) is much faster than
its large-displacement slip rate (1.6 ± 0.2 mm/yr; Oskin et al., 2007) (Figure 3.3). Specifically, the Calico
fault has generated four surface-rupturing earthquakes within the past ~9,000 years (Ganev et al., 2010),
which coincide with periods of clustered moment release identified on other faults in the eastern California
shear zone (ECSZ) (Rockwell et al., 2000b). The MRE on the Calico fault occurred sometime between 0.6
and 2 ka, likely as part of an ongoing cluster of earthquakes that has been occurring over the past 1-1.5 ky
in the ECSZ (Rockwell et al., 2000b), including most recently the 1872 Owens Valley, 1992 Landers, 1999
Hector Mine, and 2019 Ridgecrest earthquakes. Geodetic data suggest that the Calico fault, and potentially
other nearby faults in the ECSZ, are likely experiencing a period of anomalously fast loading (Dolan et al.,
2007; Oskin et al., 2007), as originally suggested by Peltzer et al. (2001), and further discussed by Oskin et
al. (2008). Peltzer et al. (2001) showed that active dextral shear associated with the ECSZ extends across
the Garlock fault, which does not exhibit any accumulation of left-lateral shear strain energy, emphasizing
the idea that the Garlock fault has entered a slow mode (Evans, 2017; Evans et al., 2017). These
observations are consistent with kinematic models that suggest that the Garlock fault is currently storing
and releasing elastic strain energy at much slower-than-average rates, whereas the ECSZ subsystem is
storing and releasing energy at faster-than-average rates (Peltzer et al., 2001; Dolan et al., 2007, 2016;
Hatem and Dolan, 2018). Farther north in the ECSZ-Walker Lane system, the Owens Valley fault exhibits
a geodetic slip-deficit rate estimate (2.7 ± 1.4 mm/yr; Evans, 2018) that may be faster than its small-

82
displacement slip-rate (1.3 ± 0.8 mm/yr; Haddon et al., 2016), consistent with a period of faster-thanaverage elastic strain accumulation. It is worth noting however, that these rate estimates overlap at 95%
uncertainty (Table 3.1).
In addition to these behaviors, the San Jacinto fault (4) exhibits a small-displacement geologic slip rate
(15.6 ± 2.3 mm/yr; Onderdonk et al., 2015) that is similar to the current loading rate (13.2 ± 4.6 mm/yr;
Evans, 2018) within 2σ uncertainties. However, there is currently no well-constrained, large-displacement
(> 50 m) geologic slip rate available for the San Jacinto fault. Thus, the similarity of the geodetic and smalldisplacement geologic rates might suggest that the San Jacinto fault may have been captured in the middle
of either a fast period (i.e., cluster) or a slow period, but in the absence of a large-displacement slip rate, we
cannot say definitively which.
It is worth noting that the slip rate of high-CoCo faults does not seem to affect their behavior; both fastslipping and slow-slipping high-CoCo faults exhibit significant dispersion of geodetic/geologic ratios.
Dispersion analysis indicates that fast-slipping, high-CoCo faults exhibit larger dispersion of
geodetic/geologic ratios than for slower-slipping high-CoCo faults (see Appendix B), contrary to what
Cowie et al. (2012) obtained from their simulations of elastic interactions between growing faults. However,
we suspect that the dispersion values we determine are not particularly meaningful given the dearth of sliprate data from fast-slipping, high-CoCo faults.
One key element to highlight is the potential difficulty in capturing any switches from fast to slow mode
(or vice versa) with the available incremental fault slip-rate data, which in some instances may not be
detailed enough over the appropriate displacement intervals to capture these switches in mode. This
challenge will typically lie in the resolution at which the increments of the incremental slip-rate record are
obtained, and if the slip-rate data are not detailed enough over the appropriate time and displacement
intervals, the switches in mode may not be observable. Assuming, however, that the input data we use in
this study provide sufficient information to constrain the timing of these switches in mode, our results imply

83
that the elastic strain accumulation rate keeps up with or controls fast and slow fault slip periods, which
challenges the suggestion by Weldon et al. (2004) that the strain release rate varies while the strain
accumulation rate does not (i.e., their “strain-predictable behavior”).
3.8. Ductile shear zone behavior…
3.8.1. …on high-CoCo faults
The variations in strain accumulation rate described above likely record variations in the rate of shear
along the ductile shear zone roots of seismogenic faults. Here we discuss the mechanisms that might control
the behavior of ductile shear zones on high-CoCo faults.
The different behaviors exhibited by the high-CoCo faults can be explained by mechanisms that occur
at the plate-boundary scale, such as the shared accommodation of slip in complex plate-boundary structural
settings (Peltzer et al., 2001; Dolan et al., 2016), as well as by mechanisms at the scale of the fault zone,
with potential strengthening and weakening processes over the ductile shear zone and the coupling between
the brittle and the ductile parts of a fault (e.g., Peltzer et al., 2001; Dolan et al., 2007; Oskin et al., 2008;
Cawood and Dolan, submitted). In structurally complex, high-CoCo settings, mechanically complementary
faults within the system can share the load by trading off slip while maintaining a relatively constant overall
system-level rate that keeps pace with the relative plate-motion rate (Dolan et al., 2023). In these
structurally complex plate-boundary fault systems, when one fault slips much faster than its average rate
throughout multiple earthquakes, the other faults of the system slip more slowly or not at all as the overall
fault system works together to maintain constant average rate. Acceleration of the ductile shear zone rate
will create a positive feedback loop in which faster shear on the ductile shear zone roots will drive the
occurrence of more frequent, large earthquakes (i.e., an earthquake cluster) in the seismogenic part of the
fault, which will in turn accelerate underlying ductile shear rates through viscous coupling, increasing
driving stress, and potentially by addition of fluids into the nominally ductile uppermost parts of the ductile

84
shear zone roots (Ellis and Stöckhert, 2004; Dolan et al., 2007; Cowie et al., 2012; Mildon et al., 2022;
Cawood and Dolan, in revision). But eventually, either through exhaustion of the crustal strain capacitor of
stored elastic strain energy on the fault in question, and/or through increases in ductile shear zone strength
(i.e., resistance to shear), the fault will enter a slow mode of strain release as deformation shifts to a
mechanically complementary, weaker fault within the system (Dolan et al., 2023).
These accelerations and/or decelerations of the faults’ ductile shear roots of a complex fault network
might be explained by strength changes (e.g., strain hardening and weakening). (Dolan et al., 2007, 2016;
Dolan and Meade, 2017), for instance, suggested that ductile shear zone roots can harden during fast slip
periods, leading to lulls in ductile shear and hence earthquake lulls in the upper crust. In this model, the
ductile shear roots of faults are accumulating elastic strain energy more slowly than their long-term slip
rate, after having been “exhausted” during a period of rapid ductile shearing and fast fault slip in clusters
of earthquakes (Dolan et al., 2023). Other potential mechanisms occurring within ductile shear zones that
could give rise to a change in shearing rate and associated elastic strain accumulation rates of the overlying
fault include changes in fluid concentration (e.g., Mancktelow and Pennacchioni, 2004; Okazaki et al.,
2021), changes in grain size (e.g., Handy, 1989; Okudaira et al., 2017), macroscopic fault evolution (e.g.,
Handy et al., 2007) and fabric development (e.g., Carreras et al., 2005; Melosh et al., 2018) (see Cawood
and Dolan (submitted) for details on these mechanisms). All these mechanisms could drive the crustal
“strain capacitor” to either its exhaustion or its replenishment (Dolan et al., 2023; Cawood and Dolan, in
revision).
3.8.2. …on low-CoCo faults
In contrast, tectonically isolated, primary low-CoCo plate-boundary faults (e.g. central SAF, central
and eastern NAF, Alpine fault), are characterized by interseismic rates that correlate well with geologic slip
rates that are averaged over both small and large displacements (Figure 3.3). This suggests that such lowCoCo faults must “keep up” with the relative plate-motion rate over short time and small displacement

85
scales because there are no other mechanically complementary faults in such systems to share the load. In
other words, even though all of the potential strengthening and weakening mechanisms we discuss for highCoCo faults must be operating on low-CoCo faults as well, these processes will be overwhelmed by steady
increases in driving stress related to relative plate motion. All or most of the relative plate motion must be
accommodated on the primary fault in the absence of other major faults that could potentially share the
work required to move the plates past each other. Moreover, the similarity of geodetic slip-deficit rates and
small-displacement geologic slip rates on low-CoCo faults requires that the fault responds to steady
increases in driving stress at scales of no more than a few tens of meters of relative plate motion. This is
consistent with the long-held notion embodied in elastic rebound theory (Reid, 1910) that the crust can only
store a given amount of elastic strain energy before the weakest element of the system (i.e., the structurally
isolated primary fault) slips in an earthquake. In turn, this line of reasoning implies that the single, isolated
fault either has to be weak all the time - as soon as it stores no more than a few tens of meters of elastic
strain energy, it is ready to slip - or it cyclically becomes weak when stress is approaching the rupture limit.
A key question is whether this near-1:1 relationship between “energy in” (as manifest in geodetic slipdeficit rates) and “energy out” (i.e., fault slip rates) on low-CoCo faults extends to single-earthquake scales.
The few available earthquake-by-earthquake age plus displacement-per-event datasets that are available
from low-CoCo faults suggest that, at least generally, this may be the case. Specifically, the relatively
regular timing (CoV ~ 0.3) of surface ruptures on the Alpine fault at Hokuri Creek, coupled with similar
~7.5 m horizontal displacements in the two most recent earthquakes (Sutherland et al., 2006; Berryman et
al., 2012b; De Pascale and Langridge, 2012), and the similar displacements in the four most recent
earthquakes and relatively regular timing of earthquakes on the NAF at Demir Tepe (Kondo et al., 2010)
are consistent with the idea that this may extend to single earthquake scales. If this is generally true, then
low-CoCo faults may release much of, and perhaps almost all, of the shear stress accumulated since the
previous event during each rupture. It is worth noting, however, that even at the Hokuri Creek site on the
low-CoCo Alpine fault (Berryman et al., 2012b), which is characterized by quasi-periodic earthquake

86
recurrence, the 24-event record cannot be fit precisely with either time- or slip-predictable models
(Shimazaki and Nakata, 1980), and may best be explained by an underlying chaotic behavior (Gauriau et
al., 2023).
3.9. Fault’s near-future behavior, and further applications for PSHA
Our results may provide new insight into how slip rates can be better used as basic inputs into
probabilistic seismic hazard assessment (PSHA) methods. For low-CoCo faults, the outcome is
straightforward – both the slip rate averaged over large displacements and the slip rate averaged over small
displacements are similar to the geodetic slip-deficit rate. Therefore, any of these values can be used as an
input into a PSHA. Despite this relative constancy of both strain accumulation and release rates in the
behavior of a low-CoCo fault, any attempt towards formulating earthquake prediction focused on timing of
earthquake occurrence on a specific fault may be functionally impossible (Chen et al., 2020; Gauriau et al.,
2023). Therefore, a probabilistic methodology is required for any seismic hazard assessment.
For high-CoCo faults, the outcome is less straightforward, since such faults exhibit variable strain
accumulation and release rates through time. The question arises as to what slip rate value is the best to use
in PSHA? There are three possible strategies for incorporating incremental slip-rate data into PSHA, as
originally suggested by Van Dissen et al. (2020): (a) incorporating the large-displacement average slip rate
by neglecting any incremental rate changes, which in a long-term statistical sense can be viewed as
variations about the mean rate; (b) using the full error range associated with all available incremental slip
rates, or (c) favoring the most recent (smallest-displacement multiple-earthquake) incremental slip rate as
the most appropriate one.
Here we propose a potential solution to this conundrum by comparing the small-displacement and largedisplacement rates with the elastic strain accumulation rates. Geodetic slip-deficit rates have been suggested
as primary inputs into seismic hazard assessment (e.g., Bird and Kreemer, 2014; Hussain et al., 2018), but

87
never in light of comparison to available geologic slip-rate records. The examples listed in paragraph 3.8.2,
however, illustrate the current limitations on using small-displacement rates (suggestion c) as a proxy for
the most recent phase of fault behavior without considering the possibility that the fault may have switched
modes in the interval since displacement of the most-recent available small-displacement slip-rate data. We
suggest that a potential path forward is to use the comparison of the geodetic slip-deficit rates with smalldisplacement geologic rates of high-CoCo faults to forecast the near-future behavior that might be expected
on a given fault. While we suggested in our earlier paper (Gauriau and Dolan, 2021) that option (c), i.e.,
implementing the shorter-term slip rate into a PSHA, would lead to a more reliable forecast of the nearfuture behavior of the fault, the current analysis suggests that deviations of geodetic rates from the smalldisplacement geologic slip rates might better illustrate the future behavior of high-CoCo faults.
Specifically, we propose that a geodetic slip-deficit rate that is slower than the small-displacement slip
rate might indicate lower near-future hazard, because the fault is storing elastic strain energy more slowly
than average (Figure 3.5). This is exemplified by the cases of the Garlock fault, the SAFm, and the Hope
fault. Conversely, geodetic rates that are faster than the small-displacement rate on faults that have not
experienced a recent earthquake (i.e., those not experiencing a post-seismic strain transient) may indicate
higher near-future hazard, as illustrated by the nNAF, the Clarence fault, and the nDSF. In support of this
idea, the 2023 Mw = 7.8 Kahramanmaraş earthquake occurred on a section of the EAF that exhibited a
geodetic slip-deficit rate, prior to the earthquake, that was almost twice as fast as the long-term geologic
slip rate. In the case of the San Jacinto fault, and other faults with a geodetic rate that equals the smalldisplacement slip rate, we suggest that the near-future hazard can best be represented by the smalldisplacement slip rate and/or the geodetic rate (Figure 3.5).

88
Figure 3.5: Schematic illustration of modes of behavior defined in this chapter, according to the CoCo
values and the geodetic/geologic rate ratio, and their potential meaning in terms of near-future hazard.
One possible route towards using these observations in improved PSHA would be to evaluate geodetic
and geologic rate discrepancies using the smallest-displacement incremental slip rate for a fault to infer the
current mode of fault behavior.
3.10. Conclusions
Our comparison of geologic fault slip rates with geodetic slip-deficit rates from strike-slip plateboundary faults reveals markedly different strain accumulation and release behavior on structurally isolated
faults relative to those that extend through structurally complex regions. Our main take-away is that elastic

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strain accumulation rates on high-CoCo faults must vary through time, whereas they remain relatively
constant on low-CoCo faults. This can potentially be applied to faults exhibiting other kinematics, such as
extensional or compressional fault systems, where both fault interactions and slip-rate variability have also
been studied (e.g., Luo and Liu, 2010; Mildon et al., 2022).
High-CoCo faults have geodetic-to-geologic ratios that vary widely, demonstrating that rates of elastic
strain accumulation vary significantly through time at scales that are longer than individual earthquake
cycles. This is particularly clear from the differences observed between the short-term geodetic slip-deficit
rate data with long-term, large-displacement geologic slip rates, which will average over any shorter-term
and smaller-displacement accelerations and decelerations of fault slip that typify faults in such settings
(Gauriau and Dolan, 2021). Presumably, these changes reflect temporally variable rates of shear on the
ductile shear zone roots of brittle faults, which we infer are related to the more complicated history of strain
accumulation and release among regional fault interactions at displacement scales of a few tens of meters
and centennial to millennial time scales. Specifically, geodetic slip-deficit rates that neither match largedisplacement nor small-displacement average slip rates indicate that the elastic strain accumulation rate
must vary over time scales corresponding to the deceleration and acceleration periods over which smallestdisplacement geologic rates are averaged.
In contrast, low-CoCo faults are characterized by steady elastic strain accumulation and release rates,
which indicate that such faults need to “keep up with” the relative plate motion rate at short-time and smalldisplacement scales, overwhelming any potential strengthening and weakening mechanisms that might be
operating on such faults. Consequently, the geodetic slip-deficit rate observed on a low-CoCo fault can be
used as a proxy for its geologic rate, which itself can be assumed to be relatively constant.
Finally, we suggest that the discrepancies between short-term geologic slip rates and geodetic slipdeficit rates for high-CoCo faults might represent a switch of mode, revealing either an accelerating or a
decelerating phase. A geodetic slip-deficit rate that is faster than the most recent geologic incremental slip

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rate would imply a potential higher near-future seismic hazard, whereas a geodetic rate that is slower than
the smallest-displacement slip rate would signal a lower near-future seismic hazard. These discrepancies
could be used to refine PSHA models, not only in strike-slip fault systems, as highlighted in this study, but
potentially to any type of plate-boundary kinematics. The importance and current relative dearth of robust
incremental slip rate records highlights the need to develop more such records from more faults around the
world to enable better PSHA.

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CHAPTER 4 Co-seismic displacements on the Kekerengu fault during the past
three to five earthquakes at Bluff Station, New Zealand
This chapter is based on the following manuscript in preparation:
Gauriau, J., Dolan, J. F., Rhodes, E. J., Van Dissen, R. J., Little, T. A. (in prep). Co-seismic displacements
on the Kekerengu fault during the past four to five earthquakes at Bluff Station, New Zealand.
4.1. Abstract
The Kekerengu fault is one of fastest-slipping onshore faults in New Zealand and is known for having
ruptured and generated most of the seismic moment in the Mw = 7.8 2016 Kaikōura earthquake. We study
the Bluff Station site, which underwent ~10 m of right-lateral coseismic displacement during this
earthquake. We determine the potential range of displacements of the past three to five earthquakes at this
site, as well as two additional cumulative displacements. Field and lidar-based geomorphological mapping,
combined with InfraRed-stimulated luminescence and radiocarbon ages of offset features, and recently
published paleoearthquake ages of the Kekerengu fault reveal that the past slip-per-event behavior could
follow six possible scenarios. We will be able to reduce the number of possible scenarios after the complete
set of our luminescence ages is available. FAs of this writing, we observe plausible slip-per-event histories
that allow for past slip increments that may have been equal, larger, or smaller than the slip observed during
the Kaikōura earthquake, depending on the scenario. When underpinned by the luminescence ages, our
results will have key implications for earthquake slip behavior and seismic hazard analyses in New Zealand.
4.2. Introduction
Constraining patterns of slip on faults is key for understanding fault rupture mechanics and deciphering
fault system behaviors, and is one of the primary steps toward assessing the potential range of magnitudes
in future earthquakes. Records of slip per earthquake extending farther than one or two events are scarce,

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as these require both paleoearthquake ages and corresponding measures of displacement. Such records
document the behavior of a fault at a location and may indicate whether the slip per earthquake has been
variable or relatively constant. Regular slip patterns over the past four to five earthquakes have been
observed on the central North Anatolian fault at Bolu Gerede (Kondo et al., 2010) and the Carrizo Plain of
the San Andreas fault (Zielke et al., 2010). These observations suggest uniform-slip behavior, as initially
proposed by Sieh (1984), a result that also implies similar magnitude earthquakes over time along a given
fault section. In contrast, other sites exhibit slip per event that is more variable, as on the northern Dead Sea
fault (Wechsler et al., 2018) and the Imperial fault (Rockwell and Meltzner, 2008), a situation that makes
seismic hazard assessment more challenging.
Recent paleoseismic studies have yielded a chronology for the past six surface-rupturing earthquakes
on the Kekerengu fault in the South Island of New Zealand (Little et al., 2018; Morris et al., 2022), including
the Mw =7.8 2016 Kaikōura earthquake, one of the most complex earthquakes ever recorded and the largest
earthquake experienced in New Zealand in more than a century. It is unclear whether the large
displacements observed on the Kekerengu fault are exceptional or more typical of the behavior of the fault.
In this study, we evaluate the slip-per-event behavior of this fast-slipping fault by measuring progressive
displacements of fluvial landforms at the Bluff Station site on the Kekerengu fault based on geomorphic
reconstructions of offset landforms. Combined with geochronologic data for the offset landforms and the
published Kekerengu earthquake ages, these data allow us to measure the incremental slip resulting in two
cumulative displacements, including the Kaikōura earthquake coseismic displacement (Figure 4.1),
constrain the ages of these displacements, and attribute them to particular combinations of
paleoearthquakes. This analysis constrains the slip-per-event behavior of the Kekerengu fault and will allow
us to discuss its slip behavior throughout the past three to five earthquakes, including during the 2016
Kaikōura earthquake.

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4.3. The Kekerengu fault and the Bluff Station site
The Kekerengu fault is part of the Marlborough fault system (MFS), an array of sub-parallel dextral to
dextral-reverse faults that splay northeastward from the northern end of the Alpine fault (Wallace et al.,
2012a; Litchfield et al., 2014) to accommodate Pacific-Australia relative plate motion in the onshore region
of central New Zealand at the northeastern end of the South Island. The MFS accommodates the 38-40
mm/yr of plate motion that occurs chiefly on the west-dipping Hikurangi megathrust to the north, and
transfers it to the steeply east-dipping, reverse-dextral Alpine fault to the south (DeMets et al., 1994; Beavan
et al., 2002; Wallace et al., 2007, 2012a; Litchfield et al., 2014) (Figure 4.1a). The central MFS includes
four main strike-slip faults that accommodate most of the plate-boundary slip. From north to south, these
are the Wairau, Awatere, Clarence, and Hope faults. To the northeast of the Hope fault, the KekerenguJordan fault system is an 85-km-long, northwest-dipping reverse-dextral fault that carries much or all of the
slip that is transferred on to it from the southwest by the Hope Fault (Van Dissen and Yeats, 1991). The
Jordan thrust is the southern part of the Kekerengu-Jordan fault system, linking the fast-slipping Hope fault
in the south (Hatem et al., 2020) to the Kekerengu fault farther to the north (Figure 4.1c). The Kekerengu
fault extends northeastward farther offshore as the Needles fault across Cook Strait (e.g., Van Dissen, 1989;
Van Dissen and Yeats, 1991; Little et al., 2018). Farther north, in southern North Island, this slip is
transferred onto the Wairarapa and Wellington faults onshore as well as offshore faults (Litchfield et al.,
2014).
The Kekerengu fault ruptured most recently during the Mw=7.8 Kaikōura earthquake on November, 14
2016. The rupture nucleated south of the Hope fault, propagated through more than 20 faults, some of which
had never been mapped, and terminated offshore on the Needles fault ~180 km northeast of Cape Campbell
(Hamling et al., 2017; Litchfield et al., 2018; Figure 4.1). Several studies suggest that slip is also likely to
have occurred along the southernmost part of the Hikurangi subduction interface during and after that event
(e.g., Duputel and Rivera, 2017; Hollingsworth et al., 2017; Wallace et al., 2018). Approximately 27 km of

94
surface rupture occurred along the Kekerengu fault exhibiting the largest horizontal displacements
produced by the earthquake, with a maximum of ~12 m of net dextral-reverse displacement observed about
1 km to the northeast of the Bluff Station site (Kearse et al., 2018). The Bluff Station site, located on the
structurally simple, eastern stretch of the Kekerengu fault, experienced ~10 m of lateral displacement,
which is nearly twice as much as the mean net slip recorded on the total ~83 km-long rupture (Figure 4.1c)
(Kearse et al., 2018). These large displacements at the Bluff Station site resulted in one of the most wellknown images broadcast around the world after the earthquake, with the iconic picture of a house, built
directly atop the Kekerengu fault trace whose foundations were split in two by the rupture, with one of
these fragments carrying the entire superstructure of the house along with it (north of sample pit location
19-01, Figure 4.1d) (Van Dissen et al., 2019).

95
Figure 4.1: (a) Index map of the New Zealand plate boundary. The Marlborough fault system (MFS)
consists of active dextral-slip faults that transfer slip between the Hikurangi subduction zone (to the north)
and Alpine fault (to the south). (b) Major active faults of the MFS. For location, see inset (a). (c) Jordan
Thrust-Kekerengu fault system shown on hillshaded topography. For location, see part (b).The black box
is the location of several paleoseismic trenches from which Little et al. (2018) and Morris et al. (2022)
determined a paleoearthquake chronology for the fault. Red fault traces indicate the surface rupture of the
2016 Kaikōura earthquake in both (b) and (c). (d) Geomorphic surfaces (fluvial terrace treads) mapped on
hillshaded lidar (sourced from the LINZ Data Service and licensed for reuse under the CC BY 4.0 licence)

96
at Bluff Station. The labelling scheme for these (e.g., T3B) is explained in the text. White dots are
luminescence sample pits, labeled by pit number. White crosses locate co-seismic dextral displacement
measurements from Kearse et al. (2018) that we use to obtain the average co-seismic displacement at Bluff
Station.
The fluvial depositional history of the Bluff Station site began during the late Pleistocene when
voluminous alluvial gravels aggraded throughout the Kekerengu River valley. These gravels formed
extensive aggradational terraces, including those labeled in Figure 4.1d as T1 and T2 (in this chapter, we
label the oldest terrace with a subscript of “1”, and younger terraces with sequentially larger numbers).
Subsequent fluvial downcutting into these alluvial gravels yielded a younger flight of progressively lowerelevation, degradational terrace surfaces, each stabilized for periods of perhaps up to a few thousands of
years. They are labelled as T3 to T7 in Figure 4.1d, and their progressive offsets across the Kekerengu fault
are used herein to recover the incremental-slip story of the most recent earthquake displacements at Bluff
Station. Periods of high-energy streamflow, during which incising power of the Kekerengu River was
sufficient to laterally trim the adjacent risers is attested to by the ubiquitously cobble- to boulder-grain size
of the gravels that form the base of each terrace deposit (e.g., Bull & Knuepfer, 1987; Cowgill, 2007).
Locally interbedded with sand lenses, these gravels are typically capped by silt layers, which record postterrace abandonment periods of streamflow that probably bore too little energy to significantly modify
terrace risers. The latter are inferred to record deposition of overbank sediments or windblown loess on an
abandoned terrace surface after the river had cut its floodplain down to a lower elevation.
4.4. Offset measurements
We use lidar data to document offsets recorded by the two lower-terrace risers at Bluff Station, and an
average displacement recorded by modern markers for the 2016 Kaikōura earthquake. Minimum coseismic
slip estimates between 9 and 10 m were documented across the Kekerengu River immediately to the east
at the Bluff Station site, and a total slip estimate of 11 m was measured ~600 m east of the Kekerengu river
(Kearse et al., 2018). We use coseismic displacements measured from a hedge row and two farm tracks

97
(Kearse et al., 2018; see supporting Figure C.2) located on the stretch of the Kekerengu fault where we
measure the small displacements at Bluff Station. The location and measurements of these three co-seismic
displacements are indicated by white crosses in Figure 4.1d. We obtain an average coseismic displacement
of 9.7 ± 0.8 m (Figure 4.2b), which we use as the displacement during the 2016 earthquake at the Bluff
Station site.
To measure each offset, we backslip one side of the feature relative to the other along the surface trace
of the fault to visually determine a preferred (i.e., most likely, in statistical terms) displacement value. Our
reported uncertainties are 95% brackets and represent the maximum and minimum sedimentological and
structural limits on possible streamflow geometries, based on acceptable curvature of the water flow that
trimmed the terrace risers (Figure C.3, Figure C.4, and Figure C.5).
We document two offsets marked by the displacement of terrace risers T6/T7A and T3B/T6. The younger
offset we document is for the T6/T7A riser, characterized by an arcuate shape that yields a large uncertainty
in the dextral-slip estimate (about a fifth of the preferred offset value). Our restoration indicates a preferred
slip of 20 ± 4 m for this riser (Figure 4.2c). The correlation of surface T7A on both sides of the fault to the
east of terrace T6 was done by comparing the relative elevation of river gravels at sample locations 23-01
and 23-07 (Figure 4.1d, Figure 4.2a) (see Appendix C for detailed method).
The next older offset we document is the NW/SE-oriented T3B/T6 riser. South (downstream) of the
fault it is nearly straight, but with some eastward convexity, whereas north (upstream) of the fault it has
minor eastward concavity. The preserved length of the southern segment is short due to a road traversing
the riser. Our preferred restoration yields a dextral-slip estimate of 33 +3/-4 m for the T3B/T6 riser (Figure
4.2d).

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Figure 4.2: Incremental displacements of Kekerengu River alluvial terraces at Bluff Station. (a) Current
configuration of the Bluff Station site, after the 2016 Kaikōura earthquake. White dots are sample pits,
labeled by pit number. (b) Configuration prior to the 2016 Kaikōura earthquake, restored using Digital
Terrain Model acquired after the earthquake (collected by Zekkos, 2018; processed by GNS Science). We
combined the displacement measurements of three markers (a hedge row, shown by the white arrows, and
two farm tracks) documented by Kearse et al. (2018) and get an average right-lateral displacement of 9.7 ±
0.8 m. (c) Restoration of the T6/T7A riser at 20 ± 4 m. (d) Restoration of the T3B/T6 riser at 33 +3/-4 m.
4.5. Luminescence and radiocarbon dating
We dated terrace deposits at Bluff Station using infrared stimulated luminescence (IRSL) dating. We
excavated four sample pits into terraces T3B, T4, T6A (downstream of the fault) and T7A (upstream of the
fault). IRSL samples were also obtained from cleaned exposed riser cuts for terraces T6B and T7A (Figure
4.3). At each sampling location, we logged the stratigraphy relative to the ground surface, collected up to
four samples in that stratigraphic section, and dated those samples using the post-IR-IRSL225 single K-

99
feldspar grain procedure of Rhodes (2015). We assume that the abandonment age of a terrace coincides
with the age of the youngest alluvial sand or gravel bedload deposits in each the sampled section, since
these coarse-grained deposits record the final phase of deposition when the river had enough erosive power
to trim the channel margins. Most of the sampled terraces at Bluff Station are capped by thin layers of silts
(all sampling locations except pit 19-02; Figure 4.3) which, if sampled and dated, provide a minimum age
for the abandonment of the terrace tread.
Figure 4.3: IRSL samples collected at Bluff Station, plotted by elevation and relative position. Sediment
types were logged in each pit. The sample numbers indicated in bold are samples that have been dated.
At the time of this writing (April 2024), IRSL ages are still pending. When these ages are determined,
we will use them to feed a Bayesian statistical model in OxCal (Bronk Ramsey, 2001, 2017; Rhodes et al.,
2003; Zinke et al., 2017) to refine the terrace ages based on stratigraphic order within a given sequence of
samples at one location. In addition, we will determine the age of terrace risers T4/T6A and T6B/T7A
(Figure 4.3) based on the geomorphologic information that lower terraces are younger than higher ones.

100
In addition to the IRSL samples, we collected charcoal samples at location 23-02 for radiocarbon dating
(terrace T6B, Figure 4.3 and Figure C.4). All the charcoal samples came from a relatively small volume (<
3 cm3
) of the shallow part of a thick (> 1 m) sandy silt layer, located below a 20-cm-thick layer of terrace
gravels, suggesting that they may all have come from the same piece of burned wood, a possibility
supported by the very similar ages obtained for the five samples we analyzed. The analysis of the samples
was done at the University of California, Irvine, Keck accelerator mass spectrometer facility. We calibrated
the radiocarbon ages to calendric years in OxCal (Bronk Ramsey, 2001, 2017) using the most up-to-date
Southern Hemisphere calibration curve, SHCal20 (Hogg et al., 2020). The dates for each sample and an
average date for the five samples are summarized in Table 4.1.

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Field Sample
Number
UCIAMS lab
no.
Δ
14C
(‰)
#
14C Age (yrs
B.P.)
#
Calibrated Age (95.4%, yrs
B.P.) *
Calibrated Age (95.4%,
yrs C.E.)
Probability for each mode
range (%)
Mode 1 Mode 2 Mode 1 Mode 2 1 2
KE23-02-C14-01 277455
-199.3 ±
1.3 1785 ± 15 1703 - 1608 1598 - 1592 247 - 343 352 - 359 92.5 3.0
KE23-02-C14-07 277456
-201.7 ±
1.7 1810 ± 20 1730 - 1610 220 - 341 95.4
KE23-02-C14-08 277457
-199.0 ±
1.3 1785 ± 15 1703 - 1608 1598 - 1592 247 - 343 352 - 359 92.5 3.0
KE23-02-C14-09 277458
-200.3 ±
1.3 1795 ± 15 1705 - 1609 245 - 342 95.4
KE23-02-C14-14 277459
-201.6 ±
1.2 1810 ± 15 1726 - 1691 1677 - 1611 224 - 260 274 - 340 27.2 68.2
Combined age * 1705 - 1691 1682 - 1611 246 - 260 269 - 340 14.1 81.3
# Conventional radiocarbon age and Δ
14C are reported as defined by
Stuiver and Polach (1977)
* Calibrated with SHCal20 (Hogg et al., 2020). Calibrated ages are reported with respect
to A.D. 1950
Table 4.1: Radiocarbon ages of samples collected from T6B. All pieces of sample were charcoal.

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4.6. Matching terrace offsets with abandonment ages
Once the luminescence ages are determined, we will match the Bluff Station terrace ages with the
geomorphic offsets. We will use a lower-terrace reconstruction (Cowgill, 2007) which assumes that a
terrace riser age is best constrained by the age of the youngest bedload gravels on the lower-terrace tread.
This type of reconstruction is valid where the river has been sufficiently powerful to laterally trim faulted
risers, which is the case for the Kekerengu river. For instance, an offset channel levee that was observed
right after the 2016 Kaikōura earthquake on the modern Kekerengu river (Kearse et al., 2018; Figure C.2)
was completely smoothed out within about month of the earthquake. This example provides direct evidence
that the lower-terrace reconstruction model is valid at the Bluff Station site.
The 33+3/-4 m offset defined by displaced riser T3B/T6 is therefore best dated by the age of abandonment
of terrace T6A. In addition to the IRSL age that is pending, the 246-340 C.E. radiocarbon ages (Table 4.1)
of the charcoal samples collected below the terrace gravels at sampling location 23-02 provides a maximum
possible age for the abandonment of T6B. In this case, the charcoal samples likely come from a piece of
burnt wood, likely a tree fern (Figure C.5), which may have lived decades to centuries (e.g., Brock et al.,
2016) before burning and getting buried in the stratigraphy. If the tree fern burnt at a relatively old age, the
radiocarbon age could therefore overestimate the actual date when the resulting charcoal was deposited
within the bedload deposits. The comparison of the radiocarbon ages with the IRSL ages from the same
location will enable us to determine how much inheritance is related to that radiocarbon age.
The 20 ± 4 m offset defined by riser T6B/T7A is dated by the age of abandonment of terrace T7A.
Although we sampled the deposits of T7A upstream of the fault because of a very shallow water table, we
were not able to dig deeply enough to reach the river gravel bedload deposits and could therefore only
sample sands located ~35 cm above the bedload gravels, the depth of which was measured using a metal
probe inserted below the base of our sample pit. The age that will be obtained from the sampled silty sands
at sampling location 23-07 will thus provide a minimum age constraint for final trimming or incision of the

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T6B/T7A riser. To constrain the age of the T6/T7A riser offset, we will use the age of the shallowest gravels
of terrace T7A downstream of the fault, south of T6B. The age of these gravels represents a maximum age
for the abandonment of T6B, and thus the 20 ± 4 m offset.
With these age constraints on the recorded offsets within the lower terraces at Bluff Station, and our
knowledge of the magnitude and timing of slip during the Kaikōura earthquake at the site, we will be able
to constrain the ages of displacements between the 2016 Kaikōura earthquake and abandonment of the
T6/T7A riser, as well as the amount of slip that occurred between offset of the T3B/T6 riser and the onset
of offset of the T6/T7A riser. Finally, by using the Kekerengu paleoearthquake age chronology, we can
evaluate potential slip-per-event scenarios that may have produced those incremental slips.
4.7. Comparing terrace surface abandonment ages to paleoearthquake ages
We plan to compare the abandonment ages of the offset terraces with the paleoearthquake ages obtained
from three paleoseismic trenches that were studied in years 2016 (before the Kaikōura earthquake) and
2018 (after the Kaikōura earthquake) by Little et al. (2018) and Morris et al. (2022). The trenches were dug
approximately 2 km east of the Bluff Station site, along the same structurally simple and continuous stretch
of the northeastern section of the Kekerengu fault. We plan to use the most recently updated
paleoearthquake ages published by Morris et al. (2022). Using these paleoearthquake ages and our age
estimates for the 20 m and 33 m offsets, we will be able to constrain the possible scenarios of slip per event
for the Kekerengu fault at Bluff Station.
Morris et al. (2022) reported five paleoearthquake ruptures prior to the Kaikōura earthquake since 2 ka.
Their analysis yielded an earthquake mean recurrence interval for the Kekerengu fault of 375 ± 32 (1σ)
years, excluding their least constrained, oldest event (shown as E5 in Figure 4.4). They dated the
penultimate event (E1, Figure 4.4) at 1751-1839 C.E. (68.3% probability range). The antepenultimate event

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(E2) was dated at 1425-1510 C.E., and was preceded by a 400-year-long quiet period. The third event back
(E3) was dated at 729-915 C.E., and their fourth event back (E4) was dated at 354-682 C.E.
Using these paleoearthquake ages, we will be able to determine how many earthquakes cumulatively
generated 20 m of displacement and 33 m of displacement, since we will have constraints on how much
time it took for these displacements to accumulate along the Kekerengu fault. As explained in previous
section, the radiocarbon age from T6B provides a maximum age of 246-340 C.E. for the 33 +3/-4 m offset.
Combining this information with the paleoseismic record from Morris et al. (2022), we can say that the 33
+3/-4 m offset accrued throughout a maximum of five earthquakes, including the 2016 Kaikōura earthquake
(Figure 4.4). This would yield a minimum average single-event displacement of ~6.5 m.
Figure 4.4: Summary figure showing time constraints on paleoearthquake ages (Morris et al., 2020) and
terrace surface age. We display the radiocarbon age from sample location 23-02 on T6B in teal green (see
Table 4.1). Once determined, the IRSL ages will provide further constraints on plausible slip-per-event
scenarios, and specifically, the number of earthquakes possibly responsible for the 20 m and 33 m offsets.
The 20 ± 4 m offset provides an additional constraint on the slip-per-event history of the Kekerengu
fault at Bluff Station. Because the 20 ± 4 m offset must have accrued during at least one fewer earthquake
than the 33 +3/-4 m offset, it is therefore the result of a maximum of four earthquakes, including the Kaikōura

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earthquake. In addition, because we know that the Kaikōura earthquake (E0) displaced 9.7 ± 0.3 m at Bluff
Station, the 20 ± 4 m offset is necessarily the result of a minimum of two earthquakes (E0+E1). And, as a
consequence, the 33 +3/-4 m offset is the result of a minimum of three earthquakes (E0+E1+E2).
This leads to six plausible scenarios for these offsets to have accrued within the recorded
paleoearthquake. Pending final age dates from our IRSL analyses, all six of these scenarios are possible:
- Scenario A: the 33 +3/-4 m offset is the result of three earthquakes (E0+E1+E2), and the 20 ± 4 m offset
is the result of two earthquakes (E0+E1).
- Scenario B: the 33 +3/-4 m offset is the result of four earthquakes (E0+E1+E2+E3), and the 20 ± 4 m
offset is the result of two earthquakes (E0+E1).
- Scenario C: the 33 +3/-4 m offset is the result of four earthquakes (E0+E1+E2+E3), and the 20 ± 4 m
offset is the result of three earthquakes (E0+E1+E2).
- Scenario D: the 33 +3/-4 m offset is the result of five earthquakes (E0+E1+E2+E3+E4), and the 20 ± 4
m offset is the result of two earthquakes (E0+E1).
- Scenario E: the 33 +3/-4 m offset is the result of five earthquakes (E0+E1+E2+E3+E4), and the 20 ± 4
m offset is the result of three earthquakes (E0+E1+E2).
- Scenario F: the 33 +3/-4 m offset is the result of five earthquakes (E0+E1+E2+E3+E4), and the 20 ± 4
m offset is the result of four earthquakes (E0+E1+E2+E3).
Assuming that the paleoearthquake record is complete, these scenarios, illustrated in Figure 4.5, enable
us to assess the possible amounts of slip per event in the past three to five earthquakes on the Kekerengu
fault at the Bluff Station site.
Specifically, Scenario A suggests that the average slip per event is 11+2.1/-2.3 m, including the
displacement measured at Bluff Station in the Kaikōura earthquake, or 11.7+3.2/-3.5 m if we exclude the
Kaikōura earthquake. Scenarios B and C suggest an average slip per event of 8.2+1.6/-1.7 m for the past four

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earthquakes including the Kaikōura earthquake, or 7.8+2.1/-2.3 m without the Kaikōura earthquake. Scenarios
D, E, and F imply an average slip per event of 6.6+1.3/-1.4 m for the past five earthquakes, including the
Kaikōura earthquake, or 5.8+1.6/-1.7 m if the Kaikōura earthquake is not accounted.
Figure 4.5: Slip per event for each of the six possible scenarios. The boxes shown in blue refer to the
earthquakes for which we know the assumed cumulative displacement. The boxes in gray refer to the ones
for which the displacement is hypothetical. We define the probability distributions of these displacements
in Appendix C.
4.8. Elements of discussion and preliminary conclusions
4.8.1. Implications for Holocene slip rate of the Kekerengu fault
This study will provide two late Holocene slip-rate estimates for the Kekerengu fault, averaged over 20
± 4 m and 33 +3/-4 m, and incremental values averaged between 0 and 20 ± 4 m of slip, and between 20 ± 4
m and 33 +3/-4 m of slip. These incremental slip-rate values will enable a comparison with current estimates

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of the long-term slip rate of the Kekerengu fault of 20-26 mm/yr (Van Dissen et al., 2017), averaged over
30-50 ka. Specifically, these incremental slip-rate values will allow us to evaluate how much the slip rate
has varied through time. We might expect that the slip rate of the Kekerengu fault has been variable through
time, as is typical for faults embedded in a structurally complex system like the Marlborough fault system
(Gauriau and Dolan, 2021). Such faults commonly exhibit periods of slow slip releases, spersed with
periods of faster slip release (e.g., Dolan et al., 2016, 2023). The development of a complete incremental
slip-rate record for the Kekerengu fault is the subject of the next chapter.
Acquisition of a slip rate that is averaged over a small displacement (e.g., 20 ± 4 m or 33 +3/-4 m) will
facilitate comparison with current geodetic slip-deficit rate on the Kekerengu fault, of about 4.8-6.9 mm/yr
(Johnson et al., 2024). Such a comparison may allow us to recognize one of two possible situations relating
to contemporary elastic strain accumulation: (1) we are entering a slow mode, in which the geodetic rate is
slower than the small-displacement geologic rate; or (2) we are entering a fast mode, in which the geodetic
rate is faster than the small-displacement geologic rate – as potentially supported by the recent occurrence
of the Kaikōura earthquake (Gauriau and Dolan, 2024; see CHAPTER 3).
4.8.2. Variability of coseismic slip at a point
With our current knowledge of the radiocarbon age constraints for the 33 +3/-4 m offset, we have six
possible scenarios of the slip-per-event history at Bluff Station for the past three to five earthquakes (Figure
4.5), and the respective average slip per event.
To go beyond the strict analysis of average slip per event for each of the six scenarios, we ran 10,000
Markov chain Monte Carlo simulations using the RISeR code from Zinke et al. (2017, 2019a) in order to
study the possible range of single-event displacements for the earthquakes that compose each scenario.
These simulations take in as inputs the probability density functions (PDFs) of the displacements and of the
paleoearthquake ages. We detail the definition of each PDF in Appendix C. The Monte Carlo simulations

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then establish the plausible paths taken for each slip per event considering the input PDF of both the
displacement and the paleoearthquake age. The resulting ranges of single-event displacements for all six
scenarios are shown in Figure 4.6. The PDFs of the displacements used as inputs into the RISeR code allow
for very small single-event displacements (<0.5 m), which might not be recognized as single events in a
paleoseismic trench. However, the change of the PDFs of the displacements would only reduce the
variability of displacements, which is a point we discuss further, and that we show is limited to relatively
low coefficients of variation.
Figure 4.6: Distribution of single-event displacements for the six plausible scenarios presented in Figure
4.5. These distributions are the result of 10,000 Monte Carle simulations run using the RISeR code (Zinke
et al., 2017, 2019), with the definition of probability density functions (PDFs) of allowable cumulative
displacements and earthquake ages for each of the six scenarios. The definition of the PDFs for the
displacements and earthquake ages can be found in Appendix C.
Hecker et al. (2013) analyzed the statistics of repeated fault displacements at a point by compiling a
global paleoseismic data set of 505 slip observations from 171 sites around the world, with a third of these
sites coming from strike-slip faults. They expressed the variability of repeated single-event slip at a point
in terms of a coefficient of variation (CoV), the ratio of standard deviation to the mean of slip per event.

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For the strike-slip faults, they calculated a CoV of 0.50 ± 0.06 (1σ). From this result, they argued that slip
at a point is characteristic, and found that the characteristic-earthquake model produces CoV values
consistent with their results on the global paleoseismic data, if the range of earthquake magnitudes is small.
Our Monte Carlo simulations allow us to compare the CoVs of the single-event displacements in light of
the results from Hecker et al. (2013), and to discuss the influence of the Kaikōura earthquake displacement
in the statistical analysis of single-event displacements.
Figure 4.7 presents the CoVs of the single-event displacements for the six scenarios presented in the
previous section. One interesting outcome is that the exclusion of the Kaikōura earthquake displacement
from the analysis results in the shifting of the CoV distribution to higher values. In other words, when the
Kaikōura earthquake displacement is ignored, the CoVs are relatively higher than in the analysis that
includes the Kaikōura earthquake displacement. This occurs for Scenarios B to F (Figure 4.7b to f), which
are the scenarios that exhibit more variability in slip per event. If the variability in single-event slip becomes
reduced by the addition of the Kaikōura event, it suggests that the Kaikōura earthquake is likely not an
exceptional event on its own. Only Scenario A, which exhibits the lowest CoV values of all the scenarios,
becomes slightly skewed towards zero when the Kaikōura earthquake is not included in the analysis (Figure
4.7a).

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Figure 4.7: Distribution of coefficients of variation (CoV) for the six scenarios presented in Figure 4.5.
These distributions are the result of 10,000 Monte Carle simulations run using the RISeR code (Zinke et
al., 2017, 2019), with the definition of PDFs of allowable cumulative displacements and earthquake ages
for each of the six scenarios. The definition of the PDFs for the displacements and earthquake ages can be
found in Appendix C. The gray distributions display the CoVs of slip per event excluding the Kaikōura
earthquake 9.7±0.3 m coseismic displacement. The blue distributions display the CoVs of slip per event
including the Kaikōura earthquake coseismic slip.
Indeed, Scenario A suggests similarly large displacements, where the average of slip per event is
11.7+3.2/-3.5 m. In this scenario, the slip that occurred during the Kaikōura earthquake at Bluff Station is
similar to the other coseismic displacements, with a CoV of the single-event displacements centered at
~0.16 (Figure 4.7a).
Scenarios C and F suggest that a 13+8/-7 m displacement would have occurred within a single earthquake
(during earthquake E3 for Scenario C, and during E4 for Scenario F - Figure 4.6), which implies that other
earthquakes had much smaller displacement. Indeed, in Scenario C, 10.3 ± 4.3 m of displacement would
have had to occur during two earthquakes (E1 and E2), and in Scenario F, 10.3 ± 4.3 m of displacement
would have had to occur during three earthquakes (E1, E2 and E3). In these scenarios, the 13+8/-7 m
coseismic displacement could be seen as a “Kaikōura-like” event, with a very large displacement, whereas
the other earthquakes generated smaller displacements (Figure 4.6c, f). For Scenario C, the possible CoVs

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of all single-event coseismic displacements range between 0.2 and 0.8, with a median value of ~0.47 (Figure
4.7). Similarly, for Scenario F, the CoV values range between 0.4 and 0.9, with a median value of ~0.65
(Figure 4.7f). Variability of single-event displacement for Scenarios C and F is comparable, or slightly
higher than the Hecker et al. (2013) global average for strike-slip faults (Figure 4.7c, f).
In Scenarios B and D, the penultimate event E1 would have displaced 10 ± 4.3 m, and could be
interpreted as a “Kaikōura-like” event. The CoV values for Scenario B range between 0.1 and 0.6, with a
median value of 0.36, and the ones for Scenario D range between 0.2 and 0.8, with a median value of 0.52.
These results allow for a range of variability that overlaps with the low CoV results from Hecker et al.
(2013), but also allow for higher variability (Figure 4.7b, d).
Only Scenario E seems to describe a slip-per-event history that displays the Kaikōura earthquake as the
only large-displacement event, although the PDFs of the other coseismic displacements allow for slip as
large as 17 m (Figure 4.7e). The CoV values of slip per event for Scenario E range between 0.2 and 0.8,
with a median value of 0.48, which allow for a range of variability that overlaps with the low CoV results
from Hecker et al. (2013), but also allow for higher variability.
In addition, something that was noted by Little et al. (2018) and Morris et al. (2022) in the study of
their Kekerengu fault paleoseismic trenches, was that surface ruptures of E1, E2, E3 and E4 all have
minimal structural relief and surface expression. In contrast, at the trench sites, the 2016 rupture exhibited
much stronger surface deformation, with more vertical displacement (Morris et al., 2021). It will be
interesting to verify if the minimal surface expression of past events directly relates to the size of these
paleoearthquakes. An alternative explanation for the different patterns of surface expression is the overall
slip vector, which slightly compressional for the Kaikōura earthquake, in contrast to slightly extensional
for the past earthquakes.

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Having luminescence age constraints on the 20 ± 4 m offset will help reduce the number of plausible
scenarios and will allow us to discuss further the possibility of several “Kaikōura-like” events in the past,
and determine whether or not the coseismic slip that occurred during the Kaikōura earthquake is a typical
displacement at Bluff Station. The analysis of allowable CoV of slip per event and its implications for how
characteristically the Kekerengu fault has behaved, in light of the study by Hecker et al. (2013), will enable
us to further tackle this question.
4.8.3. Timing of earthquakes in coastal Kaikōura ranges
Comparing the timing of earthquakes that occurred in the coastal Kaikōura ranges, including the
Kekerengu fault, with the slip-per-event history of the Kekerengu obtained from this study will enable us
to discuss relationships between single-event displacements and potential multi-fault ruptures, involving
neighboring upper-plate faults, and/or the Hikurangi megathrust interface.
Additionally, we can explore whether the paleoseismic records from the Kekerengu fault (Morris et al.,
2022), the Papatea fault (Langridge et al., 2023), and the Hope fault (Hatem et al., 2019) indicate that the
structurally simple eastern segment of the Kekerengu fault may rupture by itself in isolated earthquakes.
Given the length of the northeastern Kekerengu fault section, together with its offshore portion, the Needles
fault (~65-km-long), empirical scaling relationships (Stirling et al., 2013) would predict that it can rupture
in isolated earthquakes of maximum magnitude Mw ~ 7.1. Alternatively, the Kekerengu fault could rupture
with other faults of the plate-boundary system. Figure 4.8 summarizes findings from paleoseismic studies
(Hatem et al., 2019; Morris et al., 2022; Langridge et al., 2023) and from coastal uplifts related to seismic
events recorded along the Kaikōura coast (Howell and Clark, 2022), or related to Hikurangi megathrust
events (Clark et al., 2015).

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Figure 4.8: Summary figure of paleoseismic records in coastal Kaikōura ranges. This figure compares
paleoearthquakes from the Hope fault (Hatem et al., 2019), the Papatea fault (Langridge et al., 2023), the
Kekerengu fault (Morris et al., 2022), and Kaikōura coast paleoarthquakes at Kaikōura peninsula, Waipapa

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Bay, Parikawa, Cape Campbell, and Mataora-Wairau lagoon (Howell and Clark, 2022, and references
therein).
The 1701-1842 C.E. (95% confidence interval) age range of the penultimate event (E1) on the
Kekerengu fault is similar to the 1757-1857 C.E. time range of the penultimate event found on the Papatea
fault (Langridge et al., 2023), and to the 1731–1840 C.E. age range found for the Conway segment of the
Hope fault (Hatem et al., 2019). This indicates that the Kekerengu, Papatea, and Hope faults either ruptured
together, or separately within a few-decade-long time window, prior to the beginning of European
settlement. This rupture or temporal cluster of earthquakes likely just preceded the Mw >8.1 1855 Wairarapa
earthquake, which ruptured an ∼160-kilometer-long section extending into Cook Strait (e.g., Little et al.,
2009). In addition, the coastal uplift records from Howell and Clark (2022) suggest that the Kaikōura
peninsula, along with the Parikawa beach and Cape Campbell sites may have been uplifted at approximately
the time of the Kekerengu earthquake E1 (Figure 4.8).
The antepenultimate event recorded on the eastern Kekerengu fault (E2) is constrained to have occurred
at 1422-1594 C.E. (95% confidence interval; Morris et al., 2022). This event is similar in age to the Hope
fault’s third event dated at 1495-1611 C.E. (Hatem et al., 2019) and to the Hikurangi megathrust earthquake
recorded by subsidence at Mataora-Wairau lagoon, dated at 1430-1480 C.E. (Howell and Clark, 2022;
Figure 4.8). Coseismic coastal uplift at Cape Campbell and Parikawa beach has also been recorded during
time intervals that are not distinguishable from the time range of event E2 on the Kekerengu fault.
Interestingly, the age range for the Kekerengu E2 rupture does not overlap at 95 % confidence interval with
the Papatea antepenultimate earthquake (Figure 4.8).
The records of coseismic coastal uplift along the Marlborough northeastern coast and of the Hikurangi
megathrust earthquake, as well as the paleoseismic record of the Papatea fault, do not extend beyond the
past millennium. There are therefore no additional time correlations we can potentially make between older
seismic events on the Kekerengu fault (E3, E4, E5) and recognized uplift events. Potentially, since the

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oldest event that occurred on the Papatea fault (shown as dashed arrow in Figure 4.8) is purely constrained
by a minimum age, we cannot rule out another event when the Papatea fault could have ruptured together
with the Kekerengu fault. Both events E3 and E4 on the Kekerengu fault could also have occurred
coincidentally with the oldest event on the Conway segment of the Hope fault (47-1240 C.E.; Hatem et al.,
2019).
From the amount of evidence collected for the past three earthquakes that ruptured the Kekerengu fault
(E0, E1, E2), the Kekerengu fault may rupture simultaneously with other faults like the Papatea fault or the
Hope fault, with coastal uplift involved (E0 and E1), or during Hikurangi megathrust earthquakes (E2).
However, the available data rule out the possibility that all Kekerengu earthquakes involved the same set
of faults that slipped in 2016 (e.g., lack of overlap in age ranges of Kekerengu earthquake and the
antepenultimate earthquake on the Papatea fault).
Little et al. (2018) and Hatem et al. (2019) discussed potential earthquake sequences involving the
Hope, Jordan-Kekerengu-Needles, and Wairarapa faults. They identified the sequence involving E1 on the
Kekerengu fault that began prior to the historic era, and ended with both the 1855 Wairarapa earthquake
(Grapes and Downes, 1997; Rodgers and Little, 2006), and the 1888 Mw = 7–7.3 Amuri earthquake that
ruptured the central Hope fault (e.g., Cowan, 1991; Khajavi et al., 2016). Similarly, earthquake E3 that
occurred on the eastern Kekerengu fault may have been involved in a “wall-to-wall” simultaneous rupture
of the entire Alpine-Hope-Jordan-Kekerengu-Needles-Wairarapa system (Hatem et al., 2019).
The Kekerengu fault ruptures either together with a regional network of faults, simultaneously (as during
the 2016 Kaikōura event), or during time intervals that are not resolved by radiocarbon dating of brief
clusters of large single-fault earthquakes. It seems less likely that it ruptures in isolation from other faults,
and the combinations of regional faults involved in multi-fault ruptures may differ from one earthquake
cycle to another, given the record of paleoseismic evidence shown in Figure 4.8.

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Determining the age constraints on the 20 ± 4 m will help reduce the number of allowable single-event
slip scenarios, to link the possible single-event slip records with the possible Kekerengu rupture styles,
including potential ruptures in isolation, ruptures with other regional faults, and ruptures involving the
megathrust interface.
4.9. Conclusions
In this study we have analyzed two cumulative displacements recorded in fill-cut terrace risers, along
with the right-lateral coseismic displacement that occurred during the 2016 Kaikōura earthquake at the
Bluff Station site on the eastern Kekerengu fault. Our measurements for the cumulative displacements are
20 ± 4 m and 33+4/-3 m, and the Kaikōura earthquake coseismic slip was 9.7 ± 0.3 m. Luminescence dating
(total of 17 samples) will help us constrain the ages of the two larger offsets. For now, radiocarbon dating
has allowed us to constrain the maximum-possible age of the 33 m offset. By combining this information
with paleoearthquake ages from paleoseismic trenches that were studied in the vicinity of Bluff Station, we
have established six possible scenarios of single-event slip in the past 1,700 years. These scenarios allow
us to discuss whether or not the Kaikōura earthquake was exceptional in terms of coseismic displacement.
Our analysis shows that the variability of single-event displacement for the six scenarios allow for low
variability of slip for the eastern Kekerengu fault, even when the Kaikōura earthquake displacement is
included in the analysis, implying that this structure may behave in a nearly characteristic way. Having
further constraints on the age of the 20 ± 4 m offset will help narrow the number of plausible slip-per-event
scenarios, and further consider the relationship between possible amount of coseismic slip with isolated
Kekerengu ruptures or multi-fault ruptures in the coastal Kaikōura ranges.
4.10. Acknowledgements
We thank Dannielle Fougere, Caje Weigandt and Peter Tuckett for their great help in the field for
digging and sampling. We thank John Southon and the people from the Keck accelerator mass spectrometer

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facility at UC Irvine for their help processing the radiocarbon samples. We thank the Murray family who
allowed us to sample on their property at Bluff Station. We also would like to acknowledge Matt Hill (GNS)
and thank him for providing us with high-resolution Digital Surface Models and Digital Terrain Models of
the area around the ripped off house at Bluff Station site.

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CHAPTER 5 Latest Pleistocene-Holocene incremental slip rates of the
Kekerengu fault, South Island, New Zealand
5.1. Abstract
In this chapter, I detail my studies of four different slip-rate sites along the Kekerengu fault in South
Island, New Zealand. I performed geomorphic mapping of offset river terraces at Bluff Station, Shag Bend,
Black Hut, and McLean Stream sites, by combining observations from field investigations and analysis of
lidar data. I document the locations where my collaborators and I have collected luminescence samples to
date the river terraces and constrain the age of the cumulative displacements that they record. Once
completed, these luminescence ages will enable me to develop an incremental slip-rate record for the
Kekerengu fault, with a focus on determining potential slip-rate variability through time and along strike.
Specifically, the incremental slip-rate record may enhance variations of slip rate through time, and
contrasting slip histories between the simple northeastern section of the Kekerengu fault, and its
southwestern double-stranded section. Finally, this work will add to the emerging record of slip-rate studies
in the Marlborough fault system, a complex plate-boundary system of dextral faults, facilitating comparison
of the incremental slip-rate record from all of the major faults in the mechanically integrated system.
5.2. Introduction
The Kekerengu fault is embedded within the structurally complex, strike-slip Marlborough fault
system. Research on the MFS has been one of the most productive for fault slip-rate studies, yielding four
incremental fault slip-rate records for each of the four main faults of the MFS (Zinke et al., 2017, 2019,
2021; Hatem et al., 2020). Such slip-rate records are crucial to the understanding of the behavior of fault
systems, as they reveal potential slip-rate variability through time, close interplay between faults or groups
of faults (Dolan et al., 2007, 2016, 2023) and provide constraints on the controls on the behavior of the fault

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themselves, which may individually reflect how relative plate motion is accommodated throughout an entire
plate boundary.
The goal of this study is to document the incremental fault slip of the Kekerengu fault during late
Pleistocene-Holocene time. The data originate from four different sites, which are, from northeast to
southwest: (1) the Bluff Station site, (2) the Shag Bend site, (3) the Black Hut site, and (4) the McLean
Stream site. I used high-resolution aerial light-detection and ranging (lidar) data (sourced from the LINZ
Data Service) and field study to measure fifteen offset geomorphic features. I will combine these results
with luminescence dating technique to determine the ages of the individual offsets, once the luminescence
ages are obtained. The incremental slip-rate records that will emerge from this study will allow me to
discuss strain-release patterns in light of the other MFS faults’ incremental slip-rate records, as well as the
collective behavior of the MFS faults.
5.3. Geologic overview
5.3.1. Tectonic setting
In New Zealand, relative motion between the Pacific and Australia plates is accommodated along the
west-dipping Hikurangi megathrust in the north, and the east-dipping reverse-dextral Alpine fault in the
south (Figure 5.1a). In between, the relative plate motion of 38-40 mm/yr (DeMets et al., 1994; Beavan et
al., 2002; Wallace et al., 2007) is transferred through the Marlborough fault system (MFS), a network of
sub-parallel dextral and oblique-reverse faults that splays northeastward off the Alpine fault (Wallace et al.,
2012a; Litchfield et al., 2014). The central MFS includes four main strike-slip faults that accommodate
most of the relative plate-boundary motion: the Wairau, Awatere, Clarence, and Hope faults (Figure 5.1b).
The Kekerengu fault is located on the easternmost edge of the MFS. Like the other MFS faults, it is a rightlateral strike-slip fault that extends northward offshore as the Needles fault across the Cook Strait, and
southward as the Jordan thrust. The Kekerengu section and the Jordan thrust form the 85-km-long reverse-

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dextral Kekerengu-Jordan fault system, whose rupture generated most of the seismic moment during the
2016 Mw = 7.8 Kaikōura earthquake. Recent paleoseismic studies, carried out on the eastern Kekerengu
fault, have documented five other earthquakes in the past ~2,000 years, suggesting a mean earthquake
recurrence interval of 375 years (Little et al., 2018; Morris et al., 2022).
If slip rate has been constant, a 9-13 km offset that restores the sharp turn of the Clarence River (Figure
5.1c) and other key geomorphic markers (Figure D.1; Duvall et al., 2020), combined with a ~20-26 mm/yr
long-term slip rate for the Kekerengu fault, as initially proposed by Van Dissen et al. (2017), would suggest
that the dextral fault slip possibly initiated at 350 to 650 thousand years ago. If, alternatively, these offset
rivers first incised at some point after the fault started slipping, this would imply a younger initiation age.

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Figure 5.1: (a) Map of active tectonics of New Zealand. The Marlborough fault system (MFS) transfers
slip between the Hikurangi subduction zone and the Alpine fault. Gray arrows show Pacific/Australia
relative convergence (e.g., Beavan et al., 2002) (b) Major active faults of the MFS. The grey squares are
the locations where the incremental slip-rate records for the Wairau, Awatere, Clarence and Hope faults
have been developed. The white squares are the sites studied in this paper. (c) The Jordan-Thrust-Kekerengu
fault system shown on top of topography, including the hillshaded Digital Elevation Model from the lidar
data acquired in 2016 along the fault system (sourced from the LINZ Data Service and licensed for reuse
under the CC BY 4.0 license). The two yellow arrows refer to the 9-13 km offset that restores the Clarence
River path. Fault traces colored in red refer to the surface rupture of the 2016 Kaikōura earthquake.
5.3.2. Slip-rate sites
The Bluff Station, Shag Bend, Black Hut and McLean Stream sites are located along a relatively
structurally simple stretch of the Kekerengu fault (Figure 5.1c). The closest two sites are the McLean Stream

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and Black Hut sites and are located ~0.5 km apart. The farthest apart are the Bluff Station and the McLean
Stream sites, located ~18 km apart. At McLean Stream, the fault system becomes more complex southward,
with the Kekerengu fault splitting into the dextral Fidget fault and the Jordan thrust. The easternmost study
site is the Bluff Station site, which was previously studied by Van Dissen et al. (2017) who completed the
OSL (optically stimulated luminescence) dating of aggradational terraces recording large (600 to 950 m)
right-lateral offsets of the Kekerengu fault. The fault offsets at McLean Stream and Shag Bend sites have
been studied in prior field investigations, but have not been the subject of publications.
I detail each of the sites in a consistent manner, presenting first the offset measurements, as well as the
local coseismic displacements that occurred in the 2016 Kaikōura earthquake, and then the method to
determine the offset ages. The offset ages are still pending, so the results presented in this chapter mainly
focus on the description of cumulative offsets. I use a similar labeling system for all sites for the offset river
terraces. In the nomenclature I use, the older the terrace (i.e., the higher up terrace), the lower the numbering
referring to that terrace. Although there is a terrace T1 in all of the studied sites, it does not necessarily refer
to the same feature across all sites. In the text, for the sake of clarity, I add the acronym referring to the site
when I mention the name of a terrace. For instance, when designating terrace T3 at Shag Bend, I will use
the terminology “T3
SB”.
5.4. The Bluff Station site
The Bluff Station site is located on the NE/SW-oriented, structurally simple stretch of the Kekerengu
fault. This site exhibits a flight of well-defined fluvial terraces (referred to in the text as T#BS) that formed
during distinct climatic episodes which allowed to first form four successive aggradational terraces, located
in the western part of the site, and subsequently several fill-cut terraces, located in the eastern part of the
site, closer to the current riverbed. The four main recognized fill terraces, designated, from oldest to
youngest (i.e., highest to lowest), as the McLeod, Chaffey, Kulnine, Winterholme (also called T1BS) and

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T2BS terraces, indicate lateral erosion, whereas the cut terraces (T3BS to T7 BS) record incision by the
Kekerengu river into the most recent fill terrace T2BS (Figure 5.2).
Figure 5.2: Fluvial terraces mapped on hillshaded lidar (sourced from the LINZ Data Service and licensed
for reuse under the CC BY 4.0 license) at Bluff Station. The aggradational terraces are named after the
nomenclature defined by Van Dissen et al. (2017) and their OSL sampling campaign dating back to 1999-
2000. Grey circles are the OSL sample pit locations from Van Dissen et al. (2017), white circles are the
IRSL sample pit locations performed by our team in March 2019 and March 2023.
The fluvial depositional history of the Kekerengu River at Bluff Station began during latest Pleistocene
time when glacial outwash gravels aggraded throughout several valleys of the Kaikōura ranges (e.g., Bull
and Knuepfer, 1987; Knuepfer, 1992; Rother et al., 2014; Shulmeister et al., 2019). These fill terraces
formed during cold stadial or glacial periods in response to increased sediment supply (e.g., Litchfield and
Berryman, 2006). Oxygen isotope stages were tentatively assigned to each of the four fill terraces at Bluff
Station, based on comparisons with terrace sequences along the Charwell and Awatere rivers (located in
the northern Canterbury and Marlborough regions, respectively; Knuepfer, 1992) and the presence of

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Kawakawa tephra (dated at ca. 25.4 ka by Vandergoes et al., 2013) in the upper eolian deposits of the
Kulnine terrace. The McLeod terrace was correlated with Oxygen isotope stages 5 or 6, the Chaffey terrace
coincides with stage 3 to 4, the Kulnine terrace correlates with late stage 3, and the Winterholme terrace
(T1BS) coincides with the early periods of stage 2, or the peak of the Last Glacial Maximum (LGM). Finally,
terrace T2BS likely coincides with a later period of stage 2, which may have lasted until ca. 12-14 ka (e.g.,
Bull and Knuepfer, 1987; Zinke et al., 2019).
In response to regional uplift, the Kekerengu River started incising into the T2BS fill terrace after stage
2. This created cut (or degradational) terrace surfaces (T3BS to T7BS
) at progressively lower elevations where
the river floodplain temporarily stabilized for periods of hundreds to thousands of years, depositing
relatively thin units of sandy gravels and silts.
5.4.1. Bluff Station offsets
To measure the progressive displacements recorded in the geomorphology of the Bluff Station site, I
combine mapping using 1-m resolution lidar data (Figure 5.1,Figure 5.2) with observations made during
field seasons carried out in 2019 and 2023, expanding on the earlier work on the aggradational terrace
offsets by Van Dissen et al. (2017). I document six cumulative offsets that pre-date the 2016 Kaikōura
earthquake, marked by the displacement of fluvial markers, such as streams, channels, and terrace risers.
To measure each offset, I backslip one side of the feature relative to the other along the fault and visually
determine a preferred (most likely) displacement value. The reported uncertainties refer to 95% brackets,
and represent the maximum and minimum limits of sedimentologically allowable configurations, based on
acceptable curvature of the water flow that created the displaced features (Figure D.2 to Figure D.5, Figure
C.3 and Figure C.4).

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5.4.1.1. 915 ± 40 m offset of the Chaffey/Kulnine riser
The largest recorded offset is marked by the gently-sloped, ~25-m-tall riser between the aggradational
terraces Chaffey and Kulnine, downstream of the fault, and Glencoe stream, upstream of the fault (Figure
5.3a). I restore these features in order to obtain a configuration that corresponds to the period when Glencoe
Stream incised into the Chaffey terrace, and occupied a floodplain which now is the Kulnine terrace. I
determine an offset value of 915 ± 40 m (Figure 5.3b, Figure D.2). This is in agreement with the
measurement done by Van Dissen et al. (2017) who visually determined an offset of 950 ± 100 m, using
air photographs.

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Figure 5.3: Interpreted hillshaded lidar maps showing (a) unrestored configuration and (b) restored
configuration at 915 m for the offset Chaffey/Kulnine riser. The gray box masks fault-related topography
to aid visualization of the offset geomorphic features.
5.4.1.2. 860 ± 40 m offset of the Kulnine inset channel
The channel incised into the Kulnine surface is a prominent feature, ~16-25 m deep. It formed after the
Kulnine terrace was abandoned, when Glencoe stream started incising into the Kulnine surface. Restoring
the Kulnine channel with the path of the Glencoe stream upstream of the fault, I obtain an offset of 860 ±

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40 m (Figure 5.4 and Figure D.3), whose estimate slightly overlaps with the largest offset found for the
Chaffey/Kulnine riser. This measurement agrees within uncertainties with the 850 ± 150 m dextral offset
obtained by Van Dissen et al. (2017), who used aerial photographs to determine this offset.
Figure 5.4: Restoration at 860 m of the Kulnine abandoned channel offset. The gray box masks faultrelated topography to aid visualization of the offset geomorphic features.
5.4.1.3. 625 ± 40 m offset of the Kulnine/Winterholme riser
Southwest of Glencoe stream, the steeply-sloping, ~13-m-tall riser between Winterholme and Kulnine
terraces is roughly perpendicular to the Kekerengu fault. A gully located along the riser and draining into
the Kekerengu river ~1 km farther south incised sometime after abandonment of the Winterholme terrace
(Figure 5.5a). The configuration that best restores Glencoe Stream upstream of the fault, which I correlate
as the upstream equivalent of the riser, with the Kulnine/Winterholme riser itself, is found for a 625 ± 40
m restoration (Figure 5.5b and Figure D.4). This measurement compares well with the 600 ± 50 m offset
determined by Van Dissen et al. (2017) from air photographs.

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Figure 5.5: Interpreted hillshaded lidar maps showing (a) unrestored configuration and (b) restored
configuration at 625 m for the offset Kulnine/Winterholme riser gully offset. The gray box masks faultrelated topography to aid visualization of the offset geomorphic features.
5.4.1.4. 225 +10
/-15 m offset of the T1BS/T3BS riser
To the east of the easternmost Winterholme terrace, post-Winterholme terrace abandonment is marked
by a curved, steeply-sloping, ~40-m-tall riser, downstream of the fault. A 225+10/-15 m offset restores an
arcuate incision of Winterholme (T1BS) terrace down to the T3BS terraces, as shown by the dashed line in
Figure 5.6a. At this configuration, the Kekerengu river was eroding both the upstream bedrock and the T1BS
terrace along an arcuate western edge of the active river floodplain. Floodgate Stream was incising
Winterholme (T1BS) terrace with a high-angle deflection (Figure 5.6a). The current aspect of the northern
edge of the middle Winterholme terrace supports this interpretation, since it is incised in a direction
subparallel to the fault trace. In this restored configuration, the part of Glencoe Stream upstream of the fault
must have had a riverbed or canyon located about a few tens to a hundred meters to the east of the present
canyon. The stream was likely flowing through a path different from the present path of the thalweg, which
has continued to incise during subsequent fault offset, going through a gap recognized in the
geomorphology (Figure 5.6b). Downstream of the fault, the stream was flowing through a path that was
either similar to the contemporary flowing path, or, alternatively, a few tens of meters towards the west of
the present flowing path.

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Figure 5.6: (a) 225 m restoration of Winterholme (T1BS)/T3BS offset. (b) Zoom into the crossing between
Glencoe Stream and the Kekerengu fault that describes the potential channel path taken by Glencoe Stream
at the time of this configuration. Contour lines are every 50 cm.

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5.4.1.5. 33 +4/-3 m offset of the T3BBS/T6BS riser
I characterize smaller offsets that are delineated by the lower terraces, closer to the modern Kekerengu
riverbed. Upstream (north) of the fault, the T3BBS/T6BS riser is defined by a NW/SE-oriented curve that
slightly changes direction at an inflexion point located ~15 m north of the fault (Figure 5.7a). I use the trend
of the curve between this inflexion point and the fault to restore the offset. The southern analogue of the
T3BBS/T6BS riser feature has a very small portion preserved, due to the presence of a road cut crossing the
feature. The restored configuration is obtained for 33 +4/-3 m of backslipping (Figure 5.7b and Figure C.3).
Figure 5.7: (a) Unrestored configuration of the T3BBS/T6BS riser offset. (b) Configuration restored at 33 m. The
background is a 30-cm-resolution Digital Elevation Model acquired after T3BBS was extensively bulldozed.
5.4.1.6. 20 ± 4 m offset of the T6BS/T7ABS riser
The T6 BS/T7A BS riser is characterized by a N/S trend ~80 m upstream of the fault, and gently curves
with a NW/SE trend closer to the fault. Downstream of the fault, the T6BS/T7ABS riser is subparallel to the
fault, oriented east-west (Figure 5.8a). The backslipping of 20 ± 4 m restores this offset riser to an arcuate
shape (Figure 5.8b, Figure C.4). The arcuate shape of the T6BS/T7ABS riser bears some similarity with the
morphology of the T3BBS/T6BS riser, since both risers exhibit almost an identical curvature that is likely

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inherited from relatively stable fluvial dynamics of the Kekerengu river, during the time interval that spans
the formation of both risers.
Figure 5.8: (a) Unrestored configuration of the T6
BS/T7ABS riser offset. (b) Configuration restored at 20
m. The background is a 30-cm-resolution Digital Elevation Model acquired after T3BBS was extensively
bulldozed.
5.4.1.7. Kaikōura earthquake 9.7 ± 0.8 m coseismic displacement
The Bluff Station site experienced large displacements during the Kaikōura earthquake: ~10 m of
displacement were reported there, which is nearly twice as much as the mean net slip recorded on the total
~83 km-long rupture of the Kekerengu-Jordan system (Figure 5.1c), and close to the maximum
displacement of 11.8 m, measured ~1 km east of Bluff Station (Kearse et al., 2018). I precisely document
the Kaikōura earthquake displacement at the Bluff Station site in CHAPTER 4. Specifically, I use coseismic
displacements of a hedge row and two farm tracks documented by Kearse et al. (2018) (Figure 5.9a), west
of the Kekerengu riverbed, to obtain an average coseismic displacement of 9.7 ± 0.8 m (CHAPTER 4;
Figure 5.9b).

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Figure 5.9: (a) Current configuration at the Bluff Station cottage. Coseismic displacement measurements
(with 2σ uncertainty) from Kearse et al. (2018) of hedge row, and two farm tracks are shown by the black
crosses. I used these three displacements to infer the average coseismic slip (9.7 ± 0.8 m) at the Bluff Station
site. (b) Modern configuration before the Kaikōura earthquake, restored at 9.7 m (the white arrows are
pointing at edge of displaced hedge).
5.4.2. Bluff Station age determinations
The sampling pit locations are shown in Figure 5.2. My collaborators and I have collected IRSL samples
during 2019 and 2023 field seasons, during which we collected samples from the Winterholme terrace
(T1BS) down to the lower terraces. I also document the location of the OSL samples that were collected
during 1998-2000 field seasons, which were aiming at characterizing the soil stratigraphy of the older fill
terraces (McLeod, Chaffey, Kulnine and Winterholme) of Bluff Station.
5.4.2.1. OSL ages from older fill terraces at Bluff Station
Van Dissen et al. (2017) studied the soil stratigraphy of the fill terraces of Bluff Station between 1998
and 2000. They collected OSL samples at four localities on the Kulnine and Winterholme terraces (Figure
5.2), and carried out correlations of the observed stratigraphy with the descriptions from ~70 other localities.
For this, they used a combination of natural exposures, hand-dug soil pits, and auger holes. They obtained
OSL ages from multiple depths in the pits dug into Winterholme and Kulnine terraces.

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5.4.2.2. IRSL ages
Figure 5.10: Schematic diagram of IRSL sample locations at Bluff Station, and their relative elevation.
The horizontal distances are arbitrary. The morphostratigraphic profiles located above the elevation line
refer to sampling sites located upstream of the fault, whereas profiles located below the elevation line refer
to sampling locations downstream of the fault. The sample numbers indicated in bold are samples that have
been dated.
My collaborators and I collected IRSL samples at the Bluff Station site, focusing on the T1BS terrace
down to the T7ABS terrace. In total, we sampled at 11 locations and collected 30 samples (Figure 5.10).
Combining the luminescence ages (both OSL and IRSL) with the fault offsets will allow us to document
the slip history of the Kekerengu fault at Bluff Station. The abandonment age of each terrace, once obtained,
will be determined by the youngest gravel floodplain bedload deposits. In this study, I will use a lowerterrace reconstruction, in which the age of a riser is best constrained by the abandonment age of the lower
terrace tread (e.g., Hubert-Ferrari et al., 2002; Van Der Woerd et al., 2002; Cowgill, 2007; Zinke et al.,
2017, 2019), because the Kekerengu river has had sufficient erosive power to laterally trim faulted risers.
Indeed, the time at which a river ceases trimming a riser is inferred to coincide with the abandonment of
the floodplain (see section 5.8 for details).

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The Kulnine terrace was abandoned before being incised into a lower level. At some point later during
subsequent aggradational event, the new floodplain was partially refilled to an elevation lower than the
Kulnine terrace tread. Thus, the youngest bedload gravels from Winterholme (T1BS) will best define the
age at which the Kulnine/Winterholme riser began to be offset, which took place after Winterholme
floodplain incision and abandonment.
The arcuate riser between T1BS and T3 BS that defines the 225+10/-15 m offset cannot have formed at any
configuration smaller than 210 m (the minimum bound to this restoration; Figure 5.6). As a consequence,
anything inset into this riser must be younger than the time that corresponds to the 225 m configuration.
The T2BS aggradational terrace is older than the incision of the curved T1BS/T3BS riser, equivalently, older
than the 225 m offset, hence, even though the amount of time that elapsed between T2BS abandonment and
the curved incision is unknown, there has been more than 225 m of slip since the abandonment of the T2BS
terrace. Therefore, the T2BS abandonment age will provide a maximum age constraint on the 225 m offset.
Furthermore, the curved incision occurred before T3BS time. The age of T3BS thus provides a minimum age
constraint on this 225 m offset.
The T3BBS /T6BS riser is younger than the T3BBS tread and was cut by the river that finally formed the
floodplain now marked by the T6BS terrace surface. Based on a lower-terrace reconstruction, the T3BBS
/T6BS riser cannot have accumulated displacement until after abandonment of terrace T6BS. Similarly, the
T6BS /T7ABS riser will be dated by the abandonment age of T7ABS
.
5.4.2.3. Radiocarbon age
My collaborators and I sampled five charcoal samples coming from location 23-02 (Figure 5.2), within
the deposit cut into terrace T6BBS
. All the charcoal samples came from a relatively small volume (< 3 cm3
)
of the shallow part of a thick (> 1 m) sandy silt layer, located below a 20-cm-thick layer of terrace gravels,
suggesting that they may all have come from the same piece of burnt wood, a possibility supported by the

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very similar ages obtained for the five samples we analyzed (see Table 4.1 from previous chapter). The
samples were processed and analyzed at the University of California, Irvine, Keck accelerator mass
spectrometer facility. Radiocarbon age results were then calibrated to calendric years using the most up-todate Southern Hemisphere calibration curve, SHCal20, in OxCal software (Bronk Ramsey, 2001, 2017;
Hogg et al., 2020).
5.5. The Shag Bend site
The Shag Bend site is located at the intersection of the main Clarence River and the Big Stream
tributary. Big Stream is characterized by a relatively small watershed of ~4.5 km2
, whereas the Clarence
River is a large catchment that flows NNE to SSW along the strike of the Kekerengu fault.
The Shag Bend site is located on a straight, structurally simple stretch of the Kekerengu fault, ~11 km
southwest of the Bluff Station site (section 5.4). The Shag Bend terraces described in this section (referred
to in the text as T#SB) are degradational surfaces that formed during incision of Big Stream into T1SB and
T2SB and that are eroded to the east and southeast by the SW-flowing Clarence River (Figure 5.11a). Each
of the terrace treads represents the level of the floodplain of Big Stream when it temporarily stabilized for
periods ranging from hundreds to thousands of years. Big Stream successively created the terraces shown
in Figure 5.11, T1SB to T5SB, from oldest to youngest, which are separated by steep terrace risers. Periods
of high-energy streamflow, during which Big Stream streampower was sufficient to laterally trim the
adjacent risers, are consistent with the coarse-grained, pebble gravels and sand deposits that make up the
bedload deposits of the terrace (e.g., Lensen, 1964; Bull and Knuepfer, 1987).

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Figure 5.11: Fluvial terraces mapped on hillshaded lidar (sourced from the LINZ Data Service and licensed
for reuse under the CC BY 4.0 license) at Shag Bend, with inset zoomed into the T5SB lower terraces. The
white dots indicate the IRSL sampling locations. The white hexagon marks the radiocarbon sample
collected below the T4SB surface. The black crosses are the Kaikōura earthquake coseismic displacements
(with 2σ uncertainty) measured by Kearse et al. (2018) that I use to infer the average coseismic displacement
at the Shag Bend site.
5.5.1. Shag Bend offsets
Using lidar data and observations made during the 2023 field season, I identified three offsets that can
be restored across the Kekerengu fault at the Shag Bend site. These include, from largest to smallest: (1) a
limestone bedrock ridge and associated onlap of fluvial terrace T3SB; (2) the T3SB/T4SB riser; and (3) the
T4SB/T5SB riser. As I did for the Bluff Station site, I used hillshaded lidar visualization and topography to
determine sedimentologically plausible offset values, including maximum, minimum and preferred values
for each offset (with the total range covering a 95% uncertainty interval).

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5.5.1.1. 78 +16/-8 m offset of bedrock/T3SB inner edge onlap
The terrace T3SB surface onlaps the bedrock ridge located west of the Shag Bend site both downstream
and upstream of the fault, and we use this inner edge of the T3SB terrace as the piercing line for our offset
reconstruction (Figure 5.12a).
Restorations of fault offset of the contact between the bedrock ridge and terrace T3SB, yield to a
preferred offset of 78 +16/-8 m (Figure 5.12b, Figure D.6). This offset may be minimum, because of a possible
landslide that likely hides the limit between the bedrock and terrace T3SB
.
Figure 5.12: (a) Unrestored configuration at Shag Bend. (b) Restoration of the bedrock/T3SB contact at 78
m. This is likely a minimum restoration, because of the presence of a landslide potentially hiding the contact
between the bedrock and T3SB. The gray box masks fault-related topography to aid visualization of the
offset geomorphic features.
5.5.1.2. 41+5/-7 m offset of the T3SB/T4SB riser
The NW-trending riser between terraces T3SB and T4SB is relatively straight, and is recognizable on
both sides of the Kekerengu fault. About 100 m upstream of the fault, the T3SB/T4SB riser is ~12 m tall, and
becomes lower towards the fault where it is ~10 m high. Directly downstream of the fault, the riser is ~9-
m tall and gradually diminishes in height downstream to an ~3-m height 190 m southeast of the fault. Part
of the riser upstream of the fault has been slightly modified by a farm track that crosses the Kekerengu fault

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(Figure 5.13a). Directly upstream of the fault, this farm track does not affect the location of the riser.
However, ~25 m north of the fault, the fill slope resulting from the construction of the road may have
covered the T3SB/T4SB riser. Considering this, the T3SB/T4SB riser offset restores best at 41+5/-7 m (Figure
5.13b, Figure D.7). This asymmetrical uncertainty accounts for the potential modification of the riser
upstream of the fault, resulting from the construction of the farm track across the fault.
Figure 5.13: (a) Unrestored configuration at Shag Bend. (b) Restoration of T3SB/T4SB riser at 41 m. The
gray box masks fault-related topography to aid visualization of the offset geomorphic features.
5.5.1.3. 22 ± 5 m offset of the T4SB/T5SB riser
Terrace T4SB is a discontinuous surface upstream of the fault, and is characterized by a narrow (~5 m
wide) strip across the fault (Figure 5.14a). Upstream of the fault, the T4SB has been incised by Big Stream
down to the modern floodplain, and the riser between T4SB and the modern floodplain is ~4-m tall.
Downstream of the fault, terrace T5SB has been preserved, and the T4SB/T5SB riser is ~2-m tall. I restore the
riser down to terrace T4SB that leads to the modern floodplain north of the fault, to a remnant of T5SB south
of the fault. The best configuration is obtained at 22 ± 5 m (Figure 5.14b). This restoration provides a
minimum offset measurement for the T4SB/T5SB riser, because T5SB has been entirely eroded upstream of
the fault.

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Figure 5.14: (a) Unrestored configuration at Shag Bend. (b) Minimum restored configuration of the
T4SB/T5SB riser offset at 22 m. The gray box masks fault-related topography to aid visualization of the offset
geomorphic features.
5.5.1.4. Kaikoura earthquake 9.1 ± 3 m coseismic displacement
Shag Bend exhibited large amount of slip during the Kaikōura earthquake. Kearse et al. (2018) reported
a fence line, located on T3SB east of Big Stream (Figure 5.11), that was displaced by 9.5 ± 0.4 m (measured
with RTK GPS), and a channel levée, located in the modern floodplain of Big Stream, that was displaced
by 8.6 ± 3.0 m (measured on lidar map). I use the average of these two measurements, 9.1 ± 3 m, as the
coseismic displacement for this site. Similar to Bluff Station, this coseismic displacement is nearly twice
as much as the mean net slip recorded on the entire Kekerengu rupture (Kearse et al., 2018).

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5.5.2. Shag Bend age determinations
Figure 5.15: Schematic diagram of IRSL sample locations visited at Shag Bend, and their relative
elevation. The horizontal distances are arbitrary. The morphostratigraphic profiles located above the
elevation line refer to sampling sites located upstream of the fault, whereas profiles located below the
elevation line refer to sampling locations downstream of the fault. The sample numbers indicated in bold
are samples that have been dated.
My collaborators and I collected 16 luminescence samples from cleaned natural exposures of the terrace
risers and one pit that was hand-dug into T5BSB. The terrace stratigraphy consists mainly of pebble-toboulder-sized river gravels locally interbedded with sand layers, and capped by soils. Silts appear to cap
the river sediments only at sampling location 23-09 on terrace T3SB north of the fault (Figure 5.15). In
addition, one radiocarbon sample was collected from terrace T4SB, downstream of the fault (Figure 5.11,
Figure 5.15), during a 2000 field season.
The ages of samples collected from terrace T3SB will help constrain the 78 m offset using a lowerterrace reconstruction. The abandonment age of terrace T4SB will constrain the age of the T3SB/T4SB riser

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that has been offset 41 m. The ages from samples collected from terrace T5SB will constrain the age of the
T4SB/T5SB riser that has been offset by at least 21 m.
5.6. The Black Hut site
The Black Hut site is located 5 km to the southwest of Shag Bend. It is bordered to the southwest by
McLean Stream (detailed in section 5.7) and to the east by the Clarence River. The Black Hut site is
characterized by steep fill terraces that exhibit slope gradients of 20 to 30%, and whose materials were
transported through the steep canyons north of the Kekerengu fault trace that have incised the Pahau terrane
rocks (Figure 5.16). In contrast to the terraces at other sites, I refer to the Black Hut terraces by letters
instead of numbers. This is because the site is close to the McLean Stream site (section 5.7) and I do not
want to use a labeling system that would seem to imply an age correlation between the terraces at the Black
Hut site and those at the McLean Stream site.
Terrace TaBH is the oldest terrace recognized at Black Hut. Terrace TbBH is a younger terrace that
extends across the Kekerengu fault and across the different canyons (labeled a to c in Figure 5.16). I also
mapped three younger terraces incised into TbBH, referred to as TcBH, TdBH, and TeBH (Figure 5.16).

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Figure 5.16: Hillshaded lidar map (sourced from the LINZ Data Service and licensed for reuse under
the CC BY 4.0 license) showing the Black Hut site, with interpreted terraces. The white dots indicate IRSL
sampling locations. The black crosses are the Kaikōura earthquake coseismic displacements measured by
Kearse et al. (2018) that I use to infer the average coseismic displacement at the Black Hut site.
Black Hut marks the point where the structural complexity begins to increase southwestward. For
example, the Kekerengu splays southward into the reverse Waiautoa fault, which then feeds slip into the
Papatea fault, ~3 km to the south. Both of these reverse faults ruptured during the 2016 Kaikōura
earthquake, with the block located west of these structures being uplifted relative to the eastern block. In

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addition, the morphology of the 2016 surface rupture along the Kekerengu fault at the Black Hut site is
somewhat less linear than at the other sites (Bluff Station and Shag Bend) and more complex, with a multistranded and curved fault trace throughout the length of the Black Hut site. The site experienced an average
coseismic displacement of 7.5 ± 0.3 m, based on two RTK GPS measurements of a fence line offset during
the 2016 Kaikōura earthquake (Kearse et al., 2018) (Figure 5.16).
5.6.1. Black Hut 310 ± 30 m offset
Figure 5.17: (a) Unrestored configuration at Black Hut, with 2-m contour lines highlighting the channel
located along TaBH/TbBH riser. (b) Restoration of TaBH/TbBH riser offset at 310 m.
The ~10-m-tall riser between terraces TaBH and TbBH is gently curved. A channel, referred to here as
Channel A, located along this riser marks an incision period that occurred sometime after abandonment of
the TbBH terrace (Error! Reference source not found.a). TbBH has also been incised more to the northeast,
where it now has several inset terraces (TcBH to TeBH). The potential sources for the incision of TbBH are
the upstream canyons labeled A to C (Figure 5.16, Figure 5.17).
I measured one large cumulative offset that restores the deeply incised canyons north of the fault
relative to the abandoned channel between terrace TaBH and TbBH, and the incised canyon south of the fault.
I used hillshaded lidar combined with contoured topography (as well as a slope map; see Figure D.9), and

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restored these geomorphic features at 310 ± 30 m. The reported uncertainties refer to 95% brackets that
represent the maximum and minimum limits of sedimentologically allowable configurations, based on
acceptable curvature of the water flow that created the channel and canyons. In the restored configuration
(shown in Figure 5.17b), canyon A restores as a continuous drainage with canyon B’ incised into TbBH
downstream of the fault. In addition, canyons B and C would be, at this configuration, the source of Channel
A along the TaBH/TbBH riser, downstream of the fault (Figure 5.17b).
5.6.2. Kaikōura earthquake coseismic displacement at Black Hut
Kearse et al. (2018) documented two coseismic displacements of fence lines at the Black Hut site after
the Kaikōura earthquake. These were offset by 7.6 ± 0.4 m and 7.3 ± 0.4 m (Figure 5.16).
5.6.3. Black Hut sample locations
Figure 5.18: Relative elevations of terraces at the Black Hutt site, and luminescence sample locations.
To constrain the age of the 310 m offset, my collaborators and I hand-dug two pits into terraces TaBH
and TbBH. We collected three samples at different depths within each pit (Figure 5.18). The abandonment

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age of terrace TaBH will provide a maximum age for the 310 m offset. Using a lower-terrace reconstruction,
the age of the 310 m offset will be best constrained by the abandonment age of terrace TbBH
.
5.7. McLean Stream site
The McLean Stream site consists of a two-kilometer-long flight of fluvial terraces located between
McLean Stream and George Stream, two SSE-flowing tributaries of the Clarence River (Figure 5.19).
The fault at McLean Stream site is more structurally complex than to the northeast, as the site lies within a
zone of strain transfer from the relatively structurally simple Kekerengu fault to the northeast and the Jordan
thrust and Fidget fault to the southwest. Specifically, the eastern section of the Kekerengu fault splits into
two strands: a northern strand that becomes the Fidget fault farther to the southwest, and a southern strand
that becomes the Jordan Thrust farther to the south. The northern strand bears a normal component of slip
recognizable in the geomorphology and seems to transfer right-lateral displacement to the southern strand
(Error! Reference source not found.). At this location, the southern strand is predominantly right-lateral,
and progressively becomes the Jordan thrust more to the south.
The terraces at McLean Stream (referred to in the text as T#ML) formed during different climatic
episodes that first formed aggradational terraces in the western part of the site, and subsequently cut several
terraces, closer to the current active floodplain of McLean Stream. A series of high aggradational terraces
(T1ML) is located downstream of the southern strand (Figure 5.19). Unlike all other terraces discussed in
this chapter, I interpret T1ML as primarily as a large alluvial fan complex that was deposited from canyons
deeply incised by east-flowing rivers in the mountains located west of the site. At the northern and southern
extents of the large T1ML complex, it appears to morph into fluvial terraces associated with McLean Stream
to the north, and George Stream to the south (Figure 5.19). The more recent degradational terraces are
located to the north of the site, southeast of the single-stranded section of the Kekerengu fault, and represent
old floodplains of McLean Stream that were progressively abandoned during incision down to the current

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level of the streambed. They record several amounts of right-lateral displacements. Between the fill terraces
and the degradational terraces, a pressure bulge occupies the contractional stepover between the splays of
the Kekerengu fault zone.

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Figure 5.19: Geomorphology of McLean stream site mapped on hillshaded lidar (sourced from the LINZ
Data Service and licensed for reuse under the CC BY 4.0 license). White circles are the IRSL sample
locations visited in March 2019 and March 2023. White crosses are measurements (in m, with 2σ
uncertainty) from Kearse et al. (2018) of coseismic dextral displacement of the Kaikōura rupture. In (a),
the map displays the two-stranded Kekerengu fault system, with the southern strand becoming the Jordan
thrust. The Fidget fault is indicated with dashed red line, and further continues to the southwest of the

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displayed map. (b) is a zoom into the white rectangle displayed in (a), showing the displaced lower terraces
at McLean Stream.
5.7.1. McLean Stream offsets
I document five cumulative offsets for the McLean Stream site: (1) a large offset of 480 ± 50 m that
restores streams incising into terrace T1ML across the southern strand of the fault; (2) another large offset
of 380 ± 40 m that restores the incised canyons across the northern fault strand; (3) a double offset of 176
± 14 m on riser T1ML/T2ML measured on both the northern and southern strands; (4) an offset of 20+6/-5 m
that restores the T2ML/T3ML riser; and (5) a 12 ± 4 m offset recorded by both T3ML/T4ML and T4ML /T6ML
risers (Figure 5.19Error! Reference source not found.). The reported uncertainties refer to 95% brackets,
and represent the maximum and minimum limits of sedimentologically allowable configurations, based on
acceptable curvature of the water flow that created the displaced features.
5.7.1.1. 480 ± 50 m offset on southern strand
The aggradational terraces at McLean stream are incised by two prominent east-flowing streams, stream
1 and stream 2, respectively characterized by 30- and 15-m-deep canyons. Stream 1 flows along the riser
inset into the aggradational terrace T1ML. Stream 2 is located ~450 m north of stream 1 and flows parallel
to it (Figure 5.20a). Canyons B’ and C’, located between the two fault strands, are also parallel and located
~450 m from each other. I restore canyons B’ and C’ upstream of the southern strand of the Kekerengu
fault, with streams 1 and 2, respectively. The preferred configuration is found for a 480 ± 50 m restoration
(Error! Reference source not found.b), which places the prominent canyons B’ and C’ as the sources of
streams 1 and 2.

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Figure 5.20: (a) Unrestored configuration at McLean Stream site. (b) 480 m offset that restores the main
drainages across the southern strand.
5.7.1.2. 380 ± 40 m offset on northern strand
The deeply incised canyons located north of the southern strand are also right-laterally offset. They are
characterized by a deflected trend across the northern strand of the Kekerengu fault: North of the northern
strand (where we refer to them as canyons B, C and D), they are oriented E/W, whereas directly south of
the northern strand (canyons B’ and C’), they are oriented NW/SE (Figure 5.21a). I measured a large offset
on the northern strand, that restores canyons B, C and D (upstream of the northern strand) with canyons A’,
B’ and C’ (downstream of the northern strand), respectively. The preferred configuration is found for a 380
± 40 m restoration, that conserves the deflected trend of the canyons. This offset cannot be related to the
previously documented offset (480 ± 50 m), nor can it be dated.

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Figure 5.21: (a) Unrestored configuration at McLean Stream site. (b) 380 m offset that restores the canyons
and drainages across the northern strand.
5.7.1.3. 176 ± 14 m offset of the T1ML/T2ML risers
To the east of the aggradational terraces of the McLean Stream site, terraces T1BML and T1CML are
inset into the main T1AML alluvial fan complex. North of the northern strand, there is a terrace remnant into
which terrace T2ML is inset. Given this relationship with the T2ML terrace, I suspect that this terrace remnant
may be T1BML, or possibly T1CML. The T1B-C
ML/T2ML riser therefore exists on both sides of the doublestranded fault, and records displacement that occurred on both strands of the fault. Between the two fault
strands, the pressure ridge is characterized by a slope that has the same steepness as the T1BML/T2ML riser
south of the southern strand (Figure 5.22a).
I restore the T1B-C
ML/T2ML riser based on the assumption that the riser north of the northern strand and
the riser south of the southern strand are the same feature spanning both strands of the fault. Restoration
along the northern strand gives a preferred offset of 128 ± 8 m, with errors defined by the curvature of
possible stream flow that shaped the riser. The offset across the southern strand restores at an offset of 48
± 6 m. Thus, in total, the double offset for the T1B-C
ML/T2ML riser amounts to 176 ± 14 m (Figure 5.22b,
Figure D.10). The observation of these features in Google Earth imaging suggests that the inter-fault
pressure ridge may have grown to the east by successive landslides. This observation suggests that the

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erosive features that relate to the T1ML/T2ML riser between the two strands has been buried under the
landslide deposits, and thus that the measured offset is a minimum.
Figure 5.22: (a) Unrestored configuration at McLean stream, east of the T1 terraces. (b) Double offset
restored on the two strands of the Kekerengu fault (total of 176 m).
5.7.1.4. 20 +6/-5 m offset of the T2ML/T3AML riser
Terraces T2ML and T3AML are separated by a 7-m-tall, SE-trending riser. This riser is straight on both
sides of the Kekerengu fault, which at this point in the northeastern part of the McLean Stream site is singlestranded (Figure 5.23a). Backslipping by 20+6/-5 m best restores the T2ML/T3ML riser with errors limits
defined by the maximum and minimum sedimentological limits on possible streamflow geometries, based
on acceptable curvature of the water flow that trimmed the riser (Figure 5.23b, Figure D.11).

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Figure 5.23: (a) Unrestored configuration of fill cut terraces at McLean Stream, with 50-cm contour
intervals. (b) 20 m offset restores T2ML/T3AML riser. The gray area masks fault-related topography to aid
visualization of the offset geomorphic features.
5.7.1.5. 12 ± 4 m offset
Terraces T3ML and T4ML are separated by a 11-m-tall, SE-trending riser. Terraces T4ML and T6ML are
separated by an ~10-m-high riser that is slightly curved across the fault (Figure 5.24a).
Curiously, the T4ML/T6ML terrace riser appears to be offset more than the adjacent, older T3ML/T4ML
riser. However, since the T4ML/T6ML riser is a younger feature, it cannot record a larger offset than
T3ML/T4ML. Therefore, both risers must have been offset by the same amount. Using this reasoning, I obtain
a preferred right-lateral restoration of 12 ± 4 m, that accounts for a linear trend of the T3ML/T4ML riser across
the fault, and a slightly deflected geometry of the T4ML/T6ML riser across the fault (Figure 5.24b,Figure
D.12). The uncertainties therefore represent acceptable curvatures of the water flow that created both risers.
In addition, the road cut that crosses the T3AML tread and the T3ML/T4ML riser has been taken into account
in this restoration, and based on historical images comparing photographs taken before and after the
construction of the road, I show in Figure D.13 that it did not change the aspect of the T3ML/T4ML riser
between the fault and the road cut.

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Figure 5.24: (a) Unrestored configuration of fill cut terraces at McLean Stream, with interpreted terraces,
with 50-cm contour intervals. (b) 12 m offset restores both T3ML/T4ML and T4ML/T6ML offsets. The gray
area masks fault-related topography to aid visualization of the offset geomorphic features.
5.7.1.6. Kaikōura earthquake coseismic displacement
The Kaikōura earthquake coseismic displacement observed at the McLean Stream lower terraces was
5.8 ± 1.0 m, based on the averaging of tape measure measurements of offsets of a farm track and a fence
line (both recording 5.8 ± 1.0 m of displacement) (Kearse et al., 2018; Figure 5.19b). However, by the time
of our 2019 field season (2.5 years after the earthquake), McLean Stream had aggraded by several meters
in response to introduction of large amounts of sediment from upstream coseismic landslides, completely
covering several young terraces that had been observed to be offset during reconnaissance mapping and
lidar surveying following the 2016 earthquake. To the west of the lower McLean Stream terraces, where
the fault becomes double-stranded, both strands ruptured during the 2016 Kaikōura earthquake and
recorded 2 to 4 m of horizontal displacement (Kearse et al., 2018; Figure 5.19).
One basic observation that can be made from these five offsets and the 2016 coseismic displacements
along strike is that slip appears to transfer southwestward from the northern strand, which farther west
becomes the Fidget fault, onto the southern strand, which becomes the Jordan thrust fault to the south.

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5.7.2. McLean Stream age determinations
My collaborators and I sampled the terrace landforms at McLean Stream in order to constrain the age
of the offset features with pIR-IRSL dating protocol. In all, we collected 29 IRSL samples from eight
different sites (Figure 5.19 and Figure 5.25). We either hand-excavated pits into the terrace treads or directly
sampled from cleaned existing riser cuts. In addition to sampling the older terraces, we also sampled the
newly deposited alluvium within the channel, which covers the T6ML surface (sample 19-06; Figure 5.19b
and Figure 5.25Error! Reference source not found.).
Figure 5.25: Morphostratigraphic diagram of sample pits and ages relative to elevation at McLean Stream.
Horizontal scale is arbitrary. The morphostratigraphic profiles displayed below the elevation profile refer
to sampling sites located south of the southern strand. The ones displayed above the elevation profile refer
to sampling sites located north of the northern strand. The sample numbers indicated in bold are samples
that have been dated.
Using a lower-terrace reconstruction, the 176 ± 14 m offset will be constrained by the age of the terrace
T2ML abandonment age. The 20 +6/-5 m offset that displaces the T2ML/T3AML riser will be dated by the
abandonment age of terrace T3AML. The 12 m offset that defines both the T3ML
/T4ML
riser and the
T4ML/T6ML riser would be best dated by the abandonment age of terrace T6MS, using a lower-terrace

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reconstruction. However, by the time my collaborators and I first visited the McLean Stream site, there had
been massive aggradation due to the introduction of landslide materials that have been carried downstream
the river. Therefore, the original T6ML surface is buried under post-2016 gravels, and we could no longer
sample that surface to date it. We thus sampled terrace T5BMS (sample pit location 23-18; Figure 5.19b) to
provide a maximum age for the 12 m offset.
5.8. Combined incremental slip-rate history of the Kekerengu fault
Once the luminescence dating is complete, I will generate an incremental slip-rate history for the
Kekerengu fault using the four study sites documented in this chapter. To do so, I will compare the terrace
ages with the measured geomorphic offsets. For rivers that have sufficient erosive power to laterally trim
faulted risers, as is the case for high-energy perennial-flow rivers such as the Kekerengu river at Bluff
Station, Big Stream at Shag Bend, and McLean Stream, the riser age is best constrained by the abandonment
age of the lower terrace tread (Hubert-Ferrari et al., 2002; Van Der Woerd et al., 2002; Cowgill, 2007;
Zinke et al., 2017, 2019). Indeed, the time when such a river ceases trimming a riser coincides with the
abandonment of the floodplain. This “lower-terrace reconstruction” model (Cowgill, 2007), applies to all
rivers that have shaped the terraces offset by the Kekerengu fault and documented in this chapter, and to
most rivers that record fault displacement in New Zealand (e.g., Carne et al., 2011; Ninis et al., 2013; Zinke
et al., 2017, 2019). In particular, the coarse grain size of the terrace bedload gravels (typically cobbles and
boulders) that my collaborators and I sampled reveals that these rivers have erosive power that is high
enough to trim displaced risers.
One aspect that will need attention is the possibility of constructing an incremental slip-rate record from
these four different sites. This requires that the fault has had similar behavior along strike at the different
sites. This seems to be the case for the single-stranded, structurally simple stretch of the northeastern
Kekerengu fault, which applies for the Bluff Station and Shag Bend sites, but this simplifying assumption
may not apply for the other sites, which are along a more complex section of the Kekerengu fault,

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particularly at the southeasternmost sites where strain is transferred amongst the Kekerengu fault, Fidget
fault and Jordan thrust.
The construction of the slip-rate record will entail the calculation of the terrace tread abandonment ages
using a two-step OxCal Bayesian statistical model, following the method used in Zinke et al. (2017, 2019,
2021), and using the statistical methods from Bronk Ramsey (2001) and Rhodes et al. (2003). I will then
use the RISeR code from Zinke et al. (2017, 2019) to build an incremental slip-rate model for the Kekerengu
fault, accounting for the uncertainties in both the displacements and the luminescence ages.
5.9. Elements of discussion and conclusions
The slip-rate record of the Kekerengu fault will add to a growing number of incremental slip-rate studies
in the Marlborough fault system of South Island, New Zealand (Zinke et al., 2017, 2019, 2021; Hatem et
al., 2020; Dolan et al., 2023). I expect some variations in the slip-rate behavior of the Kekerengu fault, as
it is embedded within the structurally complex Marlborough fault system (see discussion in Gauriau and
Dolan (2021) - CHAPTER 2).
One key element this study may emphasize is the comparison between the slip-rate record of the Hope
fault (Hatem et al., 2020), and the one that will result from this study for the Kekerengu fault. This
comparison will further describe what Van Dissen and Yeats (1991) defined as the slip transfer model
between the Hope fault and the Kekerengu fault. Similarly, having an incremental slip-rate record for the
Kekerengu fault will allow for future comparison with the incremental slip-rate record of the Wairarapa
fault (the focus of CHAPTER 6), which is the northern continuation of the Jordan-Kekerengu fault system
in North Island, New Zealand. Together, these data will enable a complete, system-level understanding of
one of the fastest onshore faults in New Zealand.
Part of this work may also highlight slip-per-event behavior, such as at Bluff Station, where I presented
small offsets of 9.8 m (the Kaikōura earthquake coseismic displacement), 20 m, and 33 m, and where a co-

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located paleoseismic record that documents four paleoearthquakes in the past two millennia is available
(see previous chapter). In addition, the coseismic dextral displacement at McLean Stream (5.8 ±1.0 m),
combined with the cumulative displacements of 12 ± 4 m and 20 +6/-5 m may also reveal information on the
slip-per-event behavior on this more complex section of the Kekerengu fault. Specifically, if it is assumed
that each of these offsets represents the cumulative displacement after a single earthquake, then the past
three earthquakes displaced 5.8 m, 6.2 m, and 8 m. The earthquake ages related to these displacements can
then be constrained thanks to the chronological relationships between the displaced river terraces, dated by
luminescence dating.
Finally, these results will provide a path forward for more accurate estimation of time-dependent
seismic hazard in the Marlborough region of New Zealand.
5.10. Acknowledgements
I would like to thank Mat Hill (from GNS Science) for providing us with Digital Surfaces Models
(DSM) and orthophotographs of the lowest terraces of Bluff Station. I thank the Murray family who allowed
my collaborators and me to sample on their property at Bluff Station, Sandy Chaffey for letting us do land
reconnaissance on her land at the fill terraces west of Bluff Station, owners Steve and Shirley Millard who
allowed us on their land at McLean Stream, and Ecca Tanfana for letting us stay at his Black Hut property
and sampling on his property.

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CHAPTER 6 Holocene incremental slip rate of the central Wairarapa fault at
Waiohine River, New Zealand
6.1. Abstract
The Wairarapa fault is a major component of the North Island Dextral Fault Belt of New Zealand’s
north Island which, by and large, accommodates the lateral component of the obliquely convergent plate
motion at this latitude. Here, we analyze a series of progressively displaced post-Last Glacial Maximum
fluvial terrace risers and treads along the north-central Wairarapa fault at the famous Waiohine River site.
Lidar- and field-based mapping of the site reveals the right-lateral and vertical components of the
displacements recorded by offset of these terraces. We collected depth-arrayed sequences of infra-red
stimulated luminescence (IRSL) samples from the key terraces to document the ages of the terraces, which
once dated, will allow us to determine an incremental slip-rate record for the Wairarapa fault. Specifically,
we will use lower-terrace reconstructions to document both the overall, oblique-dextral-reverse incremental
slip-rate record as well as the horizontal and vertical components of slip through time. In total, we will
document four slip increments preserved within the terrace flight, which may highlight periods of
acceleration and deceleration of the strain release rate. For now, we infer a long-term dextral slip rate of
10.0 ± 1.0 mm/yr, averaged since 12 ka, based on our oldest offset measurement and published OSL and
radiocarbon ages. Our study also provides a revised estimate of coseismic slip at this site during the 1855
earthquake, a result that informs discussions of slip-per-event behavior on the Wairarapa fault. Moreover,
once the IRSL ages are determined, our results should allow us to compare the incremental slip rate behavior
of the Wairarapa fault with that of the neighboring Wellington fault, which has been shown to have varied
considerably over the past 10 ky.

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6.2. Introduction
The Wairarapa fault is one of the major dextral faults of New Zealand, and is part of the North Island
dextral fault belt, a network of dextral strike-slip and oblique-reverse faults that extend through the North
Island and accommodate almost half of the relative plate motion between the Australian and Pacific plates
(Van Dissen and Berryman, 1996; Beanland and Haines, 1998; Nicol et al., 2007). The right-lateral, oblique
reverse-strike-slip Wairarapa fault ruptured most recently during the 1855 Mw ≥ 8.1 earthquake, which
resulted in landslides, severe ground shaking, and an ~120-km-long onshore surface rupture with surficial
displacements averaging > 12 m and reaching up to 18 m at Pigeon Bush (Rodgers and Little, 2006) (Figure
6.1).
Developing a better understanding of the hazard posed by this fault is important because it has the
potential to generate large earthquakes similar to the 1855 earthquake close to Wellington, New Zealand’s
capital, which is one of the largest cities of the country and located, in part, on a sedimentary basin prone
to ground-motion amplification effects (Kaiser et al., 2021). The Wairarapa fault is connected to the south
to the offshore Needles fault, which itself extends southwards as the Kekerengu fault in the northeastern
part of South Island, which ruptured in the Mw = 7.8 2016 Kaikōura earthquake. That earthquake increased
failure stresses along the southern part of the Wairarapa fault (e.g., Hollingsworth et al., 2017; Litchfield et
al., 2018; Manighetti et al., 2020). The slip rate of the Wairarapa fault is commonly accepted to be ~8-12
mm/yr, averaged over the past 10,000 years (Little et al., 2009; Carne et al., 2011). Several studies have
documented offset landforms across the central part of the Wairarapa fault near Waiohine River (Lensen
and Vella, 1971; Grapes and Wellman, 1988; Carne et al., 2011), but due to incomplete dating, none of
them generated an incremental slip-rate record, as has been done systematically on the major faults of the
Marlborough Fault System (MFS) in northeastern South Island (Zinke et al., 2017, 2019, 2021; Hatem et
al., 2020; Dolan et al., 2023). The most recent slip-rate study of the Wairarapa fault (Carne et al., 2011)
was based on geomorphological observations of offset fluvial terraces at the Waiohine River using a Digital

160
Elevation Model (DEM) that was produced at the site from points that were surveyed using Real-Time
Kinematic (RTK) Global Positioning System (GPS) and Electronic Distance Meter (EDM) methods. In
addition, the ages of the restored terraces in the Carne et al (2011) study were obtained with Optically
Stimulated Luminescence (OSL) dating of feldspar grains, which likely provided overestimates of the
terrace ages, due to insufficient exposure to sunlight for a complete bleaching of the grains. In this study,
we hope to refine this previous work by applying more recent methods. Our aim is to document an
incremental slip-rate record for the Wairarapa fault. Specifically, we use 1-meter resolution lidar data
acquired in 2012 along the Wairarapa fault (Manighetti, 2020), to map the terraces and their edges in detail,
to revise our understanding of the history of fluvial terrace formation, and to remeasure the offsets of these
landforms across the fault, both right-lateral and vertical, from those previously documented by Carne et
al. (2011). Secondly, we plan to better constrain the ages of those incremental offsets, by using post-IRIRSL infrared stimulated luminescence dating protocol (Rhodes, 2015; Ivester et al., 2022), which has been
used successfully on other strike-slip faults in the MFS (Zinke et al., 2017, 2019, 2021; Hatem et al., 2020).
The IRSL ages are pending as of this writing, and as a consequence, this chapter focuses solely on the offset
observations and some preliminary inferences on the slip-rate behavior of the Wairarapa fault.
The incremental slip-rate record of the Wairarapa fault will potentially not only improve the basic input
data for probabilistic seismic hazards assessment (PSHA) of New Zealand, but will also help determine the
relative constancy or non-constancy of the fault slip rate through time. This issue is of critical importance
for the use of geological slip rates in PSHA and studies of geodetic-geologic rate comparisons. This is
particularly important in the case of the Wairarapa fault, because it lies within a structurally complex fault
network, and is therefore more likely to exhibit highly variable slip rates and rates of elastic strain
accumulation through time (Gauriau and Dolan, 2021, 2024). Such incremental slip-rate data at the scales
of several to several tens of earthquakes are necessary to ground-truth earthquake simulator results.
Specifically, combined with paleoearthquake timing results, incremental slip-rate records bridge the timespan between long-term tectonic processes and single-earthquake occurrence processes. Such records also

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help constrain mechanical processes that may lead to temporally irregular rates of fault slip and elastic
strain accumulation on faults, and to assess potential spatial and temporal fault interactions amongst the
major faults of the New Zealand plate-boundary system.
6.3. Tectonic context
In central New Zealand, relative motion between the Pacific and Australian plates occurs at ~40 mm/yr
along an azimuth of ~260° (DeMets et al., 1994; Beavan et al., 2002; Wallace et al., 2007). Faults in the
southern part of North Island, including the Wairarapa fault, which form the North Island Dextral Fault Belt
(NIDFB) are part of the partitioned, obliquely convergent plate-boundary fault system where oblique
subduction of oceanic lithosphere occurs along the northeast-trending Hikurangi trough. In South Island,
the New Zealand plate boundary transitions into a dextral strike-slip fault system called the Marlborough
fault system (MFS), which connects to the south with the oblique continental collision in the Southern Alps
(Figure 6.1a).
Oblique convergent motion in the southern part of North Island is partitioned between thrust faulting
along the Hikurangi subduction interface and its accretionary prism and right-lateral strike-slip faulting in
the upper plate. The Wairarapa fault, together with the offshore Kapiti Manawatu Fault System (Lamarche
et al., 2005), Shepard’s Gully, Ohariu, and Wellington faults, accommodate up to ~18 mm/yr of the dextral
Hikurangi margin-parallel motion, most of which is taken up by the Wairarapa fault (e.g., Rodgers & Little,
2006; Van Dissen & Berryman, 1996; Wang & Grapes, 2008; Figure 6.1b). At the surface, these faults have
steep dips, typically to the northwest and a subordinate component of vertical slip, typically up to the
northwest. The Wairarapa, Wellington, Ohariu and Shepherd’s Gully faults intersect the gently dipping
Hikurangi slab at depths ranging from ~ 20 km to 30 km (Williams et al., 2013; Seebeck et al., 2022; Figure
6.1b).

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The dextral-reverse Wairarapa fault initiated in the Pliocene as a reverse fault and reactivated as a
strike-slip fault in the Pleistocene (e.g., Beanland, 1995; Beanland and Haines, 1998). The southern section
of the fault is structurally complex, including several strands, including the west-dipping Wharekauhau
thrust on its eastern margin, which shows evidence of recent motion on some sections (Little et al., 2008).
The central section of the fault consists of left-stepping en échelon faults, separated by contractional bulges
or folds (Grapes and Wellman, 1988; Rodgers and Little, 2006; Carne and Little, 2012). To the north, the
Wairarapa fault splays and transfers slip northward onto three major northeast-striking, right-lateral faults
– the Mokonui, Carterton, and Masterton faults (Zachariasen et al., 2000; Townsend et al., 2002; Figure
6.1b). The Wairarapa fault continues further north as the Alfredton fault, which is more diffusely expressed
and slower-slipping (Schermer et al., 2004). The Mokonui, Carterton, and Masterton faults splaying off the
northern Wairarapa fault do not seem to have ruptured in the 1855 earthquake, based on the presence of
unbroken young river terraces (Zachariasen et al., 2000; Begg et al., 2001; Townsend et al., 2002); whereas
the Alfredton fault did rupture in 1855 (e.g., Schermer et al., 2004; Langridge et al., 2005).
This study focuses on the Waiohine River site, located in the northern central section of the Wairarapa
fault, four kilometers south of where the fault intersects and transfers slip northward onto the Carterton
fault, and 20 km north of the Pigeon Bush site, where the largest 1855 earthquake coseismic offsets were
reported (Figure 6.1b).

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Figure 6.1: (a) Tectonic context of New Zealand. MFS: Marlborough Fault System; NIDFB: North Island
Dextral Fault Belt. Arrows show Pacific/Australia relative convergence (e.g., Beavan et al., 2002). (b)
Major faults of northern South Island and southern North Island. The right-lateral Kekerengu-Needles fault
system directly connects to the Wairarapa fault. The brown lines represent the depth of the Hikurangi slab
interface (after Williams et al., 2013).

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6.4. The Waiohine River site
6.4.1. Geomorphology of the Waiohine River site
The Waiohine River flows southeastward nearly perpendicular to the northeastward strike of the
Wairarapa fault (Figure 6.2). This site is notable for a geomorphically well-defined flight of nine fluvial
terraces that are progressively offset by slip on the Wairarapa fault. The progressive offsets defined by the
terrace risers are predominantly right-lateral, with a subordinate vertical component (typically northwest
side up). The terrace flight was formed by successive downcutting of the river into alluvial gravels of the
oldest and highest Waiohine fill terrace, which was deposited following the Last Glacial Maximum (16-20
ka; e.g., Bull and Knuepfer, 1987). The Waiohine gravel terrace, referred to as T1 in this chapter (Figure
6.2), is an extensive aggradation surface recognized throughout the Wairarapa Valley across four different
major rivers, including the Waiohine River. The Waiohine surface is the youngest and most extensive
aggradational surface in the Wairarapa Valley, and formed by coalescing alluvial fans centered on the major
rivers entering the Wairarapa Valley from the Tararua Ranges to the west (Grapes, 1991; Figure 6.2). The
age of this surface has been debated since the mid-1950s, with current best estimates indicating that it was
likely abandoned during river downcutting ca. 10-12 ka (e.g., Wellman, 1955, 1972; Lensen and Vella,
1971; Suggate and Lensen, 1973; Milne and Smalley, 1979; Wang and Grapes, 2008; Little et al., 2009).
After abandonment of this highest, widespread fill terrace, repeated episodes of river incision cut the lower
degradational terraces, the edges of which are used as slip markers in this study (terraces T2 down to T8;
Figure 6.2), correlate in time with deglaciation following the LGM and consequent sea level rise at the
beginning of isotope stage 1 ca. 10 ka (Shackleton, 1987).
In our mapping of the Waiohine River site, we recognize nine terrace surfaces, designated as T1 (the
highest and oldest terrace tread, i.e., the Waiohine surface) to T9 (the lowest and youngest terrace tread of
our geomorphic interpretation, located ~2 m above the active stream floodplain). We did not label terraces
that are lower than terrace T9 and that are not displaced by the fault (Figure 6.2b). In addition, to facilitate

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our estimates of fault displacement, we have also designated five offset terrace risers (T1/T2, T2/T3, T3/T4,
the riser down to T8 (here termed the T7/T8 riser), and T8/T9), and two offset channels, Ch1 and Ch2
(Figure 6.2b).
Figure 6.2: Geomorphology at the Waiohine River site shown by hillshaded lidar. Inset (a) shows the
location of the Waiohine River and the trace of the right-lateral Wairarapa fault. (b) shows the mapped
fluvial terraces and location of IRSL sample pits. Terrace treads are labeled T1 to T9. The two offset
channels are labeled Ch1 and Ch2.
6.4.2. Previous offset and slip-rate studies
The Waiohine River site and its flight of displaced terraces were first noted by Wellman (1955), who
interpreted the displaced Waiohine surface as an aggradational terrace. He inferred a ~120 m lateral offset
for the Waiohine surface at Waiohine River. He based this offset on the vertical displacement of the

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Waiohine surface and the vertical-to-horizontal slip ratio of the younger risers and terraces below it. Lensen
and Vella (1971) reported the horizontal and vertical components of displacement of all offset terrace risers
at Waiohine River, using a measuring tape and level. They recognized eight terrace surfaces, using a
different geomorphic interpretation than ours (for instance, they interpreted our terrace surfaces T5 and T7
as being the same surface, wedging our terrace T6; Figure 6.2). Grapes and Wellman (1988) described a
flight of seven terraces, again not recognizing the any intermediate-aged surface between our T5 and T7,
and instead interpreted these three terraces (T5, T6, T7) as the same surface. The most recent research at
the Waiohine River site was conducted by Carne et al. (2011), who reinterpreted the fault displacements
after constructing a new topographic map of the fault and terraces using an RTK GPS. These authors also
attempted to date terrace surfaces T2 and T8 (using our terminology; Figure 6.2) using optically stimulated
luminescence (OSL) and radiocarbon dating. They also cited earlier OSL ages from Wang and Grapes
(2008) to constrain the timing of abandonment age of the Waiohine surface T1. In this study, we provide
an updated interpretation of the terrace riser offsets using more precise geomorphic mapping based on
analysis of 1-m-resolution lidar data. We plan to constrain the age of abandonment of the offset terraces
with infra-red stimulated luminescence (IRSL) dating.
6.5. Offset measurements
6.5.1. Measurement of right-lateral displacements
Our documentation of the progressive fault offsets at the Waiohine River site are based on geomorphic
mapping using both our field observations and analysis of 1-m resolution lidar data (Manighetti, 2020).
From the lidar data, we constructed a hillshaded digital elevation model (DEM) and topographic maps that
are contoured at intervals ranging from 10 cm to 50 cm. We recognize dextral offset on four distinct offset
groups defined by seven geomorphic features: (1) the T1/T2 terrace riser; (2) the T2/T3 riser, Channel 1,
and what we interpret as the T3/T4 riser; (3) the large riser down to terrace T8; (4) the T8/T9 riser and
Channel 2 (Figure 6.2b). To measure each offset using the lidar data, we backslipped one side of the feature

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relative to the other along the fault trace and visually determined the most sedimentologically likely
displacement value. We report displacements with 95% confidence intervals, whose bounds represent the
maximum and minimum limits of the sedimentologically acceptable configurations.
Figure 6.3: Offset markers restored to their preferred values for (a) T1/T2 riser, (b) T2/T3, Channel 1 and
T3/T4 risers, (c) Riser down to T8, and (d) T8/T9 riser and Channel 2. The arrows indicate the primary
features restored, along with the dashed lines that represent the thalweg trend of the channels. The
restoration presented here are the preferred values, indicated in each inset. The shaded sections mask faultrelated topography to aid visualization of the offset geomorphic features.
The largest displacement recognized at Waiohine River is defined by the lateral offset of the T1/T2
riser. The T1/T2 riser is a distinct and sharp feature that has a consistent linear trend both upstream and
downstream of the fault. This linear marker yields a robust dextral displacement of 100 ± 4 m (Figure 6.3a,
Table 6.1, Figure E.1). This is in agreement with the displacement of 101.1 ± 3.4 m (2σ) determined by
Carne et al. (2011) for the same offset feature.

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Terrace riser T2/T3 is dextrally displaced by 68 ± 4 m (Figure E.2). Carne et al. (2011) found a
cumulative displacement of 73.2 ± 2.3 m, which overlaps with our estimate within 2σ uncertainties. Channel
1, incised into terrace T3 is offset 65 ± 8 m, which is similar to the T2/T3 offset we determined, within
uncertainties (Figure E.3). Carne et al. (2011) found cumulative displacements of 85.8 ± 9.9 m using the
thalweg of Channel 1, and 83.7 ± 12.1 m using the northeastern edge of the channel, both of which are
slightly larger than our estimate, within 2σ uncertainties, with some overlap if we use their 83.7 ± 12.1 m
displacement. The relatively large uncertainties for this displacement estimate are due to the curved
morphology of the channel downstream of the fault. The trend of the downstream part of Channel 1 is
indeed slightly different from the upstream part of Channel 1, which allows for sedimentologically plausible
scenarios ranging from 57 m to 73 m (Figure E.3). Because Channel 1 and riser T2/T3 are offset the same
amount within uncertainties, which means that the lower terrace (T3) is offset by the same amount as the
riser, a lower-terrace reconstruction can be used to date both the riser and the channel (see explanations in
section 6.7). We therefore can combine both measurements for these two offset features.
Terrace riser T3/T4 is dextrally displaced by 75 +18/-12 m. The large error for this measurement arises
due to the difference of trend of the riser segments on each side of the fault. In addition, the asymmetrical
distribution of plausible scenarios is due to the presence of a terrace remnant that is lower than T3, directly
downstream of the fault, and directly southwest of the westernmost remnant of T3 which may either be due
to a collapse of the western side of this T3 remnant, which would have changed the trend of the T3/T4 riser,
or related to a surface incised into T3. In the first case, the trend of the T3/T4 riser downstream of the fault
should ignore the portion where the surface lower than T3 is located. This case relates to the range of
sedimentologically plausible scenarios ranging between the minimum (63 m) and preferred (75 m)
restorations (Figure E.4). In the second case, the trend of the T3/T4 riser downstream of the fault accounts
for the surface lower than T3 and considers it as part of the original upper terrace tread. This case relates to
the range of plausible scenarios ranging between the preferred (75 m) and the maximum (93 m) restorations
(Figure E.4). For this offset, Carne et al. (2011) determined a displacement of 81.8 ± 20.9 m, which also

169
bears large uncertainties, and, although centered around a larger value than our preferred estimate, provide
an estimate that compares well with ours, within 2σ uncertainties.
The T3/T4 riser, defined as such, based on the upstream relationship between the neighboring upper
(T3) and lower (T4) terraces, would be defined as T3/T5 riser directly downstream of the fault. Therefore,
the 75 +18/-12 m measurement is a minimum estimate for this dextral displacement, since the T5 tread
downstream of the fault would have been formed after some incision into T4, and along the east-west
trending riser down to terrace tread T3.
The displacement determined for the T3/T4 riser is larger than our dextral displacement determined for
the T2/T3 riser offset, and similar to our displacement determined for the Channel 1 offset, within 2σ
uncertainties. Since the T3/T4 riser cannot be displaced more than the T2/T3 riser offset, we consider that
the T3/T4 riser has been displaced the same amount as Channel 1 and the T2/T3 riser. By combining the
three displacement measurements of 68 ± 4 m, 68 ± 4 m and 75 +18/-12 m, we obtain a final best estimate of
69 +7/-5 m (Figure 6.3b).
The next younger offset is defined by the large (up to 12-m-high) riser that leads down to terrace T8.
The upper part of this riser is made of terrace treads T4, T5, T6 and T7. This large erosional feature provides
evidence for a period of extensive lateral trimming that eroded downward through all four generations of
terraces at this location. We measured a dextral displacement of this prominent riser of 28 ± 7 m (Figure
6.3c, Figure E.5).
We measured two additional displacements: one defined by the offset T8/T9 riser, and the other defined
by offset Channel 2. For the former, we measured a dextral displacement of 12.6+5.2/-3.8 m (Figure E.6), and
for the latter, we obtained a 14.6 ± 3.4 m (Figure E.7) dextral displacement. Carne et al. (2011) determined
displacements of 11.4 ± 1.7 m and 12.4 ± 0.8 m for the T8/T9 riser and the offset channel, respectively.
Our measurement of the T8/T9 offset has asymmetrical uncertainties, due to the difference in trend of the

170
riser segments on either side of the fault, with a much more curved morphology upstream of the fault, which
may be inherited from erosion of the riser. Considering a lower-terrace reconstruction, the lower terrace
should be offset by the same amount as the riser. Therefore, Channel 2 and T8/T9 riser should have the
same displacement. Combining the two displacements yields a dextral displacement of 13.6+3.4/-2.9 m
(Figure 6.3d). We interpret this dextral displacement to record the displacement during the most recent
event (the 1855 Wairarapa earthquake), as did Carne et al. (2011).
6.5.2. Measurement of vertical displacements
To measure vertical terrace displacements at the Waiohine River site, we used the topographic tool
available in Quantum GIS (QGIS) software package that allows to trace a topographic profile along a
customized line. We traced several profiles across the fault to determine the vertical component of each of
the offsets described in the previous section. Insofar as we use a lower-terrace reconstruction (see further
explanation in section 6.7), the key datum we are using for the vertical component of each of the cumulative
displacements is the difference in paleo-floodplain elevation at the top of bedload gravels (the terrace tread),
measured across the fault between the two segments of the displaced riser terrace located on either side of
the fault.
We traced profiles as close as possible to the riser that defines the cumulative offset, at the same distance
from the bottom of the riser on both sides of the fault, to account for the lateral component of the slip. For
example, for the T1/T2 riser, we used a profile located 5 m from the base of the T1/T2 riser, on both sides
of the fault. Figure 6.4 illustrates the topographic profiles we traced to obtain the vertical components of
each of the cumulative fault displacements. We determined the two trends of the profile sections (shown in
red in Figure 6.4) that define the gradient of the paleo-floodplains, on both sides of the fault. By comparing
them, we measured the maximum and minimum vertical difference (95 % confidence interval) between the
paleo-floodplains (i.e., the terrace treads on each side of the fault). The methodology for calculating the
vertical component of the displacement is shown in Figure E.9.

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Figure 6.4: Elevation profiles across the fault used to measure vertical offsets of the displaced terraces. (a)
and (g) are topographic maps displaying the drawing of the profiles, with the red sections referring to the
red sections of the displayed profiles (in insets b to f, and h to j), which are used to estimate the gradient of
the paleo-floodplain. Calculation of the uncertainties for the vertical component of the fault displacement
is described in Appendix E. Errors are 2σ intervals.

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Based on this method, we calculate a vertical offset for terrace T2 and riser T1/T2 with profile AA’
(Figure 4b), which amounts to 15.15 ± 0.44 m (2σ).
The vertical offset of terrace tread T3, Channel 1, and the T3/T4 riser should be the same, as mentioned
in section 6.5.1. To assess the throw, we measured one vertical offset for each of these three features: 12.08
± 0.54 m for profile BB’ on the northernmost sections of terrace T3, 11.42 ± 0.48 m for profile CC’ along
the thalweg of Channel 1 (profile traced using a Strahler order map - which highlights the preferential water
path, and therefore the exact position of the deepest part of thalwegs; see Figure E.7), and 12.10 ± 0.80 m
for profile DD’ on the southwestern sections of terrace T3, and 11.41 ± 0.34 m for profile EE’ along the
T3/T4 riser (Figure 6.4a, c, d, e). We use an average of these measurements (using propagation of
uncertainties with quadratic root values) as the vertical displacement for this single offset, yielding a value
of 11.75 ± 0.21 m.
To determine the vertical offset of terrace T8, we profiled the northeastern edge of T8 (profile FF’), at
a distance of 10 m from the base of the T4/T8 riser on both sides of the fault. The resulting vertical
measurement is 4.19 ± 0.30 m, using profile FF’ (Figure 6.4g, h).
The measurement we obtain for the vertical component of the total offset defined by both Channel 2
and the T8/T9 riser considers both geomorphic markers. The vertical difference across the fault obtained
from profile GG’ traced along the thalweg of Channel 2 (using a Strahler order map in QGIS – Figure E.6)
is 0.69 ± 0.50 m (Figure 6.4i). That from profile HH’, traced along the western edge of Channel 2 thalweg
(using an aspect map, which enhances the direction of the slopes derived from the digital terrain model -
Figure E.6Figure E.7), is 1.10 ± 0.70 m. Both measurements provide relatively large uncertainties, due to
the small extent of the profile on each side of the fault. Since we consider that both features recorded the
same finite displacement, as explained in the previous section, we use an average of these vertical
measurements and obtain a preferred estimate of 0.90 ± 0.40 m.

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6.5.3. Comparison of horizontal and vertical displacements
We record all our lateral and vertical measurements for each offset in Table 6.1, as well as the related
horizontal to vertical component ratios (H/V ratios). We compare them to those of Carne et al. (2011).

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Table 6.1: Horizontal and vertical measurements from cumulative displacements at Waiohine River, and related horizontal to vertical component
ratios (H/V ratios). Comparison between our results and the ones published in Carne et al. (2011).
Carne et al. (2011) This study
Restored feature
equivalent
Horizontal
component of
displacement (m)
Vertical
component of
displacement (m)
H/V ratio Restored features
Horizontal
component of
displacement (m)
Vertical
component of
displacement
(m)
H/V ratio
T1 129 ± 18 * 19.67 ± 0.09 6.9 ± 3.5 T1 120 ± 12
# 18.80 ± 0.19 6.4 ± 0.6
$
T1/T2 101.1 ± 3.4 15.74 ± 0.04 6.4 ± 0.2 T1/T2 riser 100 ± 4 15.15 ± 0.44 6.6 ± 0.3
T2/T3 73.2 ± 2.3 12.99 ± 0.03 5.6 ± 0.2
T2/T3, Channel 1, T3/T4
riser
69
+7
/-5 11.75 ± 0.21 5.9
+0.6
/-0.4 Channel 1 84.8 ± 15.6 -
T3/T4 81.8 ± 20.9 11.85 ± 0.03 6.9 ± 1.8
T4/T8 riser 22.1 ± 1.5 - T4-T7/T8 riser 28 ± 7 4.19 ± 0.30 6.7 ± 1.7
Channel 2 12.4 ± 0.8 1.3 ± 0.02 9.5 ± 0.6 T8/T9 riser, Channel 2 13.6
+3.4
/-2.9 0.90 ± 0.40 15.0
+8
/-7
* inferred from their average H/V ratio for Waiohine terraces and vertical displacement of the Waiohine surface
#
inferred from our average H/V ratio
$
and vertical displacement of the Waiohine surface (see Figure E.8)
$ The ratio is calculated based on an average excluding the most recent offset, which seems to be an outlier in terms of H/V ratio

175
Except for the most recent offset (recorded by the T8/T9 riser), the H/V ratios of the cumulative
displacements agree with the average 6:1 ratio obtained by Grapes (1991) and Carne et al. (2011). For the
most recent offset, which records the displacement during the MRE in 1855, we obtained a H/V ratio of 15,
higher than the ratio of 9.5 obtained by Carne et al. (2011), but with large uncertainties, due to the fact that
the profiles obtained for the smaller offsets provided large uncertainties on the gradients of the paleofloodplain. Our results are different from the results obtained by Carne et al. (2011) because the RTK GPSderived topographic map from Carne et al. (2011) displays Channel 2 downstream of the fault as a feature
that is trending N-S, whereas the topographic map that we derive from the 1-m-resolution lidar data
indicates it is trending NNW-SSE. This change results in a larger lateral slip measurement for Channel 2
and riser T8/T9 in our study. Our method in calculating the vertical offsets also differ in the consideration
of uncertainties (see Appendix E for explanations).
The Wairarapa fault at Waiohine River appears to have slipped during the 1855 MRE with an unusually
large H/V ratio. Using our measurements (except the apparent outlier of the MRE), we obtain an average
H/V ratio of 6.4 ± 0.6 for displacements since abandonment of the T2 terrace. This allows us to infer the
likely horizontal component of the finite displacement relative to the Waiohine surface (T1, which lacks a
higher riser). To do this, we apply the above ratio to the precisely measured vertical displacement of that
terrace, as from analysis of three profiles located along the upper edge of the T1/T2 riser (see Figure E.8).
The resulting estimate of the horizontal component of the displacement is 120 ± 12 m.
6.6. Determination of terrace ages
We excavated sample pits at seven locations on six different terrace surfaces. At each sample pit, two
to three IRSL samples were sampled in a stratigraphic sequence to yield a total of 20 samples (Figure 6.5).
We obtained assistance of a backhoe digger to dig pits that were ~2-3 m long, ~1 m wide, and ~1.5 m deep,
into the fluvial cobble-boulder gravels that comprise the terrace deposits. Our IRSL samples were collected

176
by hammering steel tubes into cleaned vertical faces, and the depth of each sample was measured relative
to the ground surface.
Figure 6.5: Morphostratigraphic diagram of sample pits and ages relative to elevation and respective
geomorphic surfaces. Sediment types are illustrated in relationship with the depth location of IRSL samples.
The pit excavations expose one or more bedload gravel units (granule- to boulder-sized clasts in a silty
sand matrix) overlain by silts and soils, or solely by the active surface soil in the case of the easternmost
remnant of T3 north of the fault (pit 23-05), and T2 (pit 23-04; Figure 6.2, Figure 6.5). The abandonment
age of each terrace, once obtained, will be taken to coincide with the age of the youngest gravel floodplain
bedload deposits. These record the final phase of deposition when the river trimmed the channel margins.
The silt deposits (40-70 cm deep) that cap most of the excavated terraces at Waiohine River were deposited
either as overbank flooding when the erosive power of the river was assumed to be too weak to laterally
trim the coarse-grained terrace risers, or as eolian deposits. The silt ages will therefore provide a minimum

177
age for the abandonment of the related terrace during the incision event that involved the river downcutting
to its next-youngest floodplain level.
Within the gravel layers, we attempted to sample the sandy matrix between the large terrace gravel
clasts. This proved challenging in these packed cobble and boulder gravels, and in many instances required
angling the sample tube around clasts not visible in the walls of the sample pit. The resulting samples will
be processed according to the post-IR-IRSL225 single grain K-feldspar procedure (Rhodes, 2015) at the
University of Sheffield luminescence laboratory.
Once the IRSL ages are obtained, we will calculate the terrace tread abandonment ages using a two-step
OxCal Bayesian statistical model, following the method used in Zinke et al. (2017, 2019a, 2021a), using
the statistical methods from Bronk Ramsey (2001) and Rhodes et al. (2003).
6.7. Determination of incremental slip rates
We will develop an incremental slip rate history for the Wairarapa fault at the Waiohine River site by
attributing terrace abandonment ages to the measured geomorphic offsets. For rivers that have sufficient
erosive power to laterally trim faulted risers, as is the case for high-energy perennial-flow rivers such as the
Waiohine River at the study site, the riser age is best constrained by the abandonment age of the lower
terrace tread (e.g., Hubert-Ferrari et al., 2002; Van Der Woerd et al., 2002; Mériaux et al., 2004; Cowgill,
2007; Zinke et al., 2017, 2019a). Indeed, the time when such a river ceases trimming a riser coincides with
the abandonment of the floodplain. This model, called the “lower-terrace” reconstruction (Cowgill, 2007),
applies to all offset terrace risers at Waiohine River. In particular, the coarse grain size of the terrace bedload
gravels (cobbles and boulders) that we dug into for sampling reveals that the Waiohine River has high
erosive power to trim the riser. In addition, the fact that risers T2/T3-T3/T4 and T8/T9 are offset by the
same amount as channels Ch1 and Ch2, respectively, confirms that the risers were completely trimmed at
the time of lower terrace abandonment (Cowgill, 2007).

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With the determination of the sampled terrace ages, we will be able to develop an incremental slip-rate
record composed of the following increments (using the lateral measurements as key data):
- From today to 1855: no slip since the MRE;
- An increment between the 13.6 m MRE offset and the 28 m offset;
- An increment between the 28 m and 69 m offsets;
- An increment between the 69 m offset and the largest recorded offset of 100 m;
- Finally, an increment between the 100 m offset and the 120 m offset inferred from the average H/V
ratio for the older terrace offset.
Figure 6.6: Made-up incremental slip-rate record for the Wairarapa fault at Waiohine River. The ages used
in this plot are fake, apart from the age of the MRE and the estimate of the Waiohine surface age (used here
as 12±1 ka). The displacements reported are the ones determined in this study. The black lines refer to the
slip-rate functions between two successive Monte Carlo sampling of displacements and ages (code from

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Zinke et al., 2017). The slip-rate values and the age/displacement values (within black rectangles) are shown
with 95% uncertainties.
For the sake of visualization of what such a record could look like, we generated a hypothetical
incremental slip-rate record for the Wairarapa fault (Figure 6.6). This record uses the displacement values
determined in the study, and the two age constraints we have available so far (i.e., the 1855 most recent
event and the age estimate of the Waiohine surface T1). Once the IRSL ages are processed, we will obtain
a final record that describes the four main increments between the five offset features observed at Waiohine
River.
Using the Waiohine surface ages from Wang and Grapes (2008) and both the vertical and inferred
horizontal components of the displacement of T1, we can estimate a long-term slip rate for the Wairarapa
fault. Wang and Grapes (2008) obtained three ages from the Waiohine surface: 10.0 ± 0.8 ka, 13.0 ± 0.9 ka
and 10.2 ± 1.2 ka obtained from loess capping the gravels. These ages might therefore postdate the
abandonment phase of the Waiohine surface. In addition, Little et al. (2009) obtained a maximum
abandonment age for the Waiohine Terrace of ~12 ka at Pigeon Bush (Figure 6.1) based on radiocarbon
dating for that surface.
Using a preferred age of 12 ka, based on the overlap between the oldest minimum age from Wang and
Grapes (2008) of 13 ± 0.9 ka and the ~12 ka maximum age obtained from radiocarbon dating presented by
Little et al. (2009), and the inferred horizontal component of the T1 displacement of 120 ± 12 m, we obtain
an estimate of the horizontal slip rate of 10.0 ± 1.0 mm/yr. Similarly, using the vertical component of the
T1 displacement of 18.80 ± 0.19 m, we determine a vertical component rate of 1.57 ± 0.01 mm/yr.
Considering a total oblique offset of 121 ± 12 m (combining horizontal and vertical components), the net
oblique slip rate for the Wairarapa fault at Waiohine river is 10.1 ± 1.0 mm/yr. These outcomes agree with
the long-term slip rates published in Carne et al. (2011), Little et al. (2009) and Wang and Grapes (2008).

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6.8. Elements of discussion and conclusion
While the IRSL ages for this study are still pending, a few key results and elements of discussion can
be highlighted here, regarding the overall behavior of the Wairarapa fault at Waiohine River.
The dextral slip rate of the Wairarapa fault, averaged over ~12 ka is 10.0 ± 1.0 mm/yr. Having four
distinct increments recording the slip behavior of the fault through time will help us determine if the slip
rate has been constant or varying (as would an incremental slip-rate record such as the one exemplified in
Figure 6.6 would suggest) throughout these four sampled time intervals. Considering that the structural
complexity surrounding the central Wairarapa fault, and the active faults it might interact with, such as the
Wellington fault, the Ohariu, the Shepherd’s Gully and the Boo Boo faults, as well as the Wairarapa fault
splays to the northeast, such as the Carterton, Masterton and Mokonui faults, and the Hikurangi megathrust,
we might expect that the slip rate of the Wairarapa fault will be variable through time. This would imply
potential phases of slow slip release, and other periods of faster slip release. The idea that faults embedded
in complex settings are more prone to slip-rate variability is detailed in CHAPTER 2 (Gauriau and Dolan,
2021). This has been shown for other New Zealand strike-slip faults within the Marlborough fault system
using the same methods as in this study (Zinke et al., 2017, 2019, 2021; Hatem et al., 2020). Furthermore,
the Marlborough fault system as a whole has been shown to accommodate a steady plate-boundary slip rate,
with each of its four main components (the Wairau, the Awatere, the Clarence and the Hope faults)
coordinating accelerations and decelerations in the accommodation of the overall relative plate-boundary
motion (Dolan et al., 2023). A similar situation might exist regarding the collective behavior of the North
Island dextral fault belt, although potential interactions between the Australian upper-plate faults with the
Hikurangi megathrust brings even more complexity (e.g., Wallace et al., 2009, 2012). A detailed
incremental slip-rate record obtained for the Wellington fault exhibits high variability in the strain release
rates (Ninis et al., 2013). Specifically, Ninis et al. (2013) found two intervals of accelerated slip rate, one
from ~10 to 8 ka, and another spanning the last ≤ 4.5 ka, and an intermediate phase of relative quiescence

181
between 8 and 4.5 ka. Comparing these periods of acceleration and deceleration for the Wellington fault
with an incremental slip-rate record of the Wairarapa fault will be of great interest to see if there is any sort
of coordination between the two faults, although their combined behavior is likely influenced by their
coupling with the megathrust.
In addition to the likely interactions with the neighboring upper-plate active faults, the Wairarapa fault’s
ductile shear roots also likely reach the Hikurangi subduction zone at about 20 km depth, and the
Wellington, Ohariu and Pukerua Shepherd faults cross the megathrust at depths of ~25-30 km (Henrys et
al., 2013; Williams et al., 2013). This renders the three-dimensional fault zone of the Wairarapa even more
complex, and potentially its slip behavior even more prone to variability, related to the activity of the
Hikurangi megathrust. For instance, the conjoint rupture between the Hikurangi megathrust and upper-plate
fault may have occurred for the Kekerengu fault (and other faults) during the Kaikōura earthquake (e.g.,
Duputel and Rivera, 2017; Hollingsworth et al., 2017; see also CHAPTER 4). Although this is the subject
of some debate, there is evidence that the southern plate interface underwent up to 0.5 m of slow slip after
the Kaikōura earthquake (Wallace et al., 2018). This type of interaction between an upper-plate fault and
the Hikurangi slab may also have occurred during the 1855 Wairarapa earthquake, and could account for
the extraordinarily large displacement observed on the Wairarapa fault at Pigeon Bush (Rodgers and Little,
2006; Figure 6.1b).
Furthermore, the acquisition of an incremental slip-rate record for the Wairarapa fault will help
determine a small-displacement slip-rate value, averaged between the MRE and the 28 m offset. Comparing
the age of the 28 m offset with the paleoseismic chronology of the southern Wairarapa fault from Little et
al., (2009) will also allow to infer the number of earthquakes cumulatively responsible for this
displacement, and therefore, the average slip per event for this obtained number of earthquakes. The access
to a small-displacement slip-rate value for the Wairarapa fault will not only help us compare that value to
the large-displacement slip rate, but it will also provide a key datum to potentially detect differences with

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the geodetic slip-deficit rate for the Wairarapa fault. Using the geodetic deformation model for New Zealand
of Johnson et al. (2024), the geodetic slip-deficit rate for the central Wairarapa fault at the Waiohine river
site is 6.0 +1.0/-1.2 mm/yr. This value, compared with the small-displacement geologic slip rate, may
emphasize some variations in the Wairarapa fault’s elastic strain accumulation rate, and perhaps a behavior
suggesting either (1) an entrance into a fast mode, if the geodetic rate is faster than the small-displacement
geologic rate, or (2) an entrance into a slow mode, if the geodetic rate is slower than the small-displacement
geologic rate (Gauriau and Dolan, 2024; see CHAPTER 3).
The H/V ratios derived from this study highlight the fact that these ratios seem to be consistent for the
older offsets, whereas the 1855 Wairarapa earthquake was characterized by a higher H/V ratio. This may
indicate that the Wairarapa earthquake was an outlier in terms of slip behavior. Furthermore, Carne et al.
(2011) derived the amount of slip that occurred at Waiohine River for the penultimate event, by using the
displacement observed at riser T4/T8 and the displacement related to the MRE. They inferred a slip of 9.7
m for the event that preceded the 1855 earthquake. We argue that the T4/T8 offset, for which we measure
a 28 ± 7 m horizontal component, could either (1) record both the 1855 earthquake and the penultimate
event, as inferred by Carne et al. (2011), or (2) record at least three earthquakes, including the 1855
earthquake, for which we determined a 13.6 m offset. Option (1) would mean that the penultimate event
would have generated an offset of 14 ± 8 m, which agrees with the large 1855 displacement observed at
Waiohine River and the one of 18.7 ± 1.0 m observed at Pigeon Bush (Figure 6.1; Rodgers and Little, 2006;
Little et al., 2009). Option (2) would imply that we cannot infer the coseismic slip estimate of the
penultimate event that occurred on the central Wairarapa fault. However, if option (2) is valid, we can
combine this information with the paleoearthquake ages obtained by Little et al. (2009). They found ages
for their penultimate and third event of 920-800 and 2340-2110 cal. years B.P., respectively. The age of the
third event would provide a minimum age for terrace T8, which, in case (2), could not have been abandoned
after 2.3-2.1 ka.

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In addition, comparing these earthquake ages with megathrust earthquake timing and magnitude might
help us differentiate the potential events that may have ruptured both the megathrust and the Wairarapa
fault, from the ones that ruptured solely the Wairarapa fault. Specifically, Clark et al. (2019) evaluate
evidence of Holocene coseismic coastal deformation and tsunamis along the Hikurangi margin, and found
that the last subduction earthquake occurred 520-470 years B.P. along the southern and potentially central
portion of the Hikurangi interface, and the penultimate event occurred at 870-815 years B.P. This very well
constrained penultimate event was a great subduction earthquake which ruptured a potential length of 350
km. It matches the timing of the penultimate event that occurred on the Wairarapa fault (Little et al., 2009).
The record from Clark et al. (2019) does not provide strong evidence of older earthquakes on the southern
portion of the subduction interface, which they consider justifiable by a sequence of upper-plate fault
earthquakes or smaller ruptures of the slab. In any case, the available paleoseismic records allow us to infer
that the Wairarapa fault may have ruptured in earthquakes that involved the Hikurangi subduction interface
(such as the penultimate event), and in earthquakes where it potentially ruptured in isolation from other
faults.
Large coseismic displacements on the Wairarapa fault seem to be the consistent trend, as shown by
Rodgers and Little, (2006) over at least the past two earthquakes, and by Manighetti et al. (2020) over what
they interpret as being seven single earthquakes. The rupture length related to these large events is
consistently much smaller than what the model from Wells and Coppersmith (1994) would predict.
Explanations for this unusually large coseismic-displacement-to-rupture-length ratio include an abnormally
large stress drop, or a rupture that extends several tens of kilometers down dip towards the Hikurangi
megathrust interface (Rodgers and Little, 2006).
These conundrums about the slip behavior of the Wairarapa fault will further be addressed once we
obtain more results on the IRSL ages that constrain the chronological aspects of our incremental slip-rate
record. Adding knowledge about the Wairarapa fault slip rate will be key to disentangle contributions of

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deeper-seated processes to upper-plate uplift in this subduction setting (Ninis et al., 2023). We hope to
resolve more questions regarding not only the consistency of the large coseismic displacements occurring
on the Wairarapa fault, but also the variability of its slip rate through time, and what it may entail for the
entire North Island dextral fault belt.
6.9. Acknowledgements
We would like to thank farmers Hank Van den Bosch and Wayne Birchall for letting us spend the time
we needed on their properties to do site reconnaissance and to sample into the fluvial terraces.

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CHAPTER 7 Pleistocene slip rate of the northern Elsinore fault at Glen Eden
7.1. Abstract
In this chapter, I describe preliminary work on the development of an incremental slip-rate record for
the northern Elsinore fault in southern California. I performed geomorphic mapping to characterize two
offset landform features at the Glen Eden site by combing geomorphological observation from former
studies, old aerial photographs taken before land development, and digital elevation models obtained from
lidar data. I also collected luminescence samples for dating these offset features. Once completed, these
luminescence ages will enable me to document a slip-rate record with two increments, each spanning
numerous earthquake cycles. This work will add up to the emerging record of slip-rate studies along the
Elsinore fault and other faults within the greater San Andreas fault system, and will facilitate analysis of
potential slip-rate variations through time and space for the Elsinore fault.
7.2. Introduction
Fault slip rates provide important information on strain release behavior of active faults. Some faults
exhibit constant slip rates (e.g., Kozacı et al., 2007; Salisbury et al., 2018; Grant-Ludwig et al., 2019),
whereas other faults exhibit slip rates that vary through time, with periods of seismic lull and periods of
accelerated strain release (e.g., Zinke et al., 2017, 2019; Wechsler et al., 2018; Hatem et al., 2020). The
difference of behavior between faults that slip constantly through time and faults that exhibit variations in
their slip rate has been shown to be controlled by the relative structural complexity that surrounds the fault.
Specifically, Gauriau and Dolan (2021) (CHAPTER 2) have shown that a fault embedded in a complex
fault system where the plate-boundary tectonic load is shared with other faults is prone to slip-rate
variations, whereas an isolated fault tends to have a constant slip-rate behavior. Such observations
necessitate the averaging of slip rate over a number of earthquake cycles that is small enough so that the
periods of strain release acceleration can be distinguished from the periods of slow release (e.g.,

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Mouslopoulou et al., 2009; Hatem et al., 2021). In other words, slip rates will be considered steady when
averaged over a large amount of earthquake cycles (e.g., Daëron et al., 2004; Oskin et al., 2004; Blisniuk
et al., 2010), whatever the potential intermediate variations it may (or not) have undergone.
The Elsinore fault is embedded within the southern San Andreas fault system, and accommodates the
total relative plate motion between the Pacific and North American plates together with the southern San
Andreas fault and the San Jacinto fault (e.g., Matti and Morton, 1993; Magistrale and Rockwell, 1996). The
relatively complex structural setting in which the Elsinore fault is embedded renders this fault prone to sliprate variability. The Elsinore fault may therefore have undergone periods of accelerated and decelerated
strain release, according to Gauriau and Dolan (2021). In this chapter, I intend to develop a Holocene-late
Pleistocene slip-rate record for the Elsinore fault, which will add to the growing number of studies that
characterize the Elsinore fault zone, and the southern San Andreas fault system as a whole. To that end, my
collaborators and I have revisited the Glen Eden site on the Glen Ivy section of the Elsinore fault (Figure
7.1), where the geomorphology was initially mapped by Millman (1988). I used high-resolution lidar
microtopographic data to revise the mapping from Millman (1988) and accurately measure fault
displacements. I also collected infrared stimulated luminescence (IRSL) samples to determine the age of
the offset landforms, following a protocol established by Rhodes (2015) has been successfully used in many
previous fault slip-rate studies (e.g., Zinke et al., 2017, 2019, 2021; Hatem et al., 2020). From these
luminescence ages I will be able to determine two slip rates likely spanning latest Pleistocene-Holocene for
the northern Elsinore fault at the Glen Eden site. Such a record is key to understanding the behavior of the
southern San Andreas fault system through time and space, with basic implications for earthquake
recurrence in a densely populated area, system-level fault interactions, and plate-boundary mechanics.
7.3. Regional tectonic setting
The Elsinore fault is one of the three principal strands of the onshore San Andreas fault system in
southern California (e.g., Wallace, 1990; Matti and Morton, 1993; Magistrale and Rockwell, 1996) (Figure

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7.1a). It accommodates up to ~10% of the total relative plate motion between the Pacific and North
American plates, in a region that is highly populated, with the urban centers of Los Angeles and San Diego
located less than 50 km from the northern and southern ends of the fault, respectively.
The fault extends for 230 km from the Santa Ana river near the southeastern limits of the Los Angeles
basin southward to the Yuha Basin (Figure 7.1b). To the northwest, the Elsinore fault feeds slip directly
into the Los Angeles basin via the Whittier fault, with the remainder of slip being transferred northward on
the Chino fault. The Elsinore fault is structurally simple and single-stranded north of Lake Elsinore, and
splits further south into two parallel active strands (Magistrale and Rockwell, 1996), with the eastern strand
defined as the Earthquake Valley fault, which itself transfers slip southeastward to the southern San Jacinto
fault zone (Rockwell et al., 2013; Gordon et al., 2015). Farther south, the Elsinore fault is single-stranded
to its southern termination south of the Coyote mountains, where slip is transferred across the Yuha Basin
to the Laguna Salada and related faults in northern Baja California (Figure 7.1b). The northern Elsinore has
recorded 10-15 km of slip (Weber, 1977; Woyski et al., 1991; Hull and Nicholson, 1992), whereas the
central Elsinore fault at Granite Mountain is characterized by a shorter cumulative displacement of 2.5-3
km (Magistrale and Rockwell, 1996).
The slip rate of the northern Elsinore fault has been characterized in previous studies (Millman and
Rockwell, 1986; Vaughan, 1987; Millman, 1988; Vaughan et al., 1999; Rockwell et al., 2000a) through
mapping of offset geomorphic features combined with age estimates constrained by a soil chronosequence
from the Ventura region (Rockwell, 1983; Millman, 1988). Aside from the fact that these soil development
ages have uncertainties that are large and difficult to quantify, the soils of the Ventura region were
developed under coastal conditions that likely result in soil development that is more rapid than at the drier,
inland setting of the Elsinore fault (Rockwell et al., 2000a). These studies indicated a long-term slip rate of
~5 mm/yr, with large uncertainties of 40-60%, related to the use of soil chronology to date the offset
features. In addition, Rockwell et al. (2000) used radiocarbon dating of charcoal from a buried channel

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offset by 9.8±0.5 m in the vicinity of the city of Murrieta to document a short-term slip rate, averaged over
the past 1.9 ky, of 4.9+1.0/-0.6 mm/yr. However informative this short-term slip rate is, the longer-term slip
behavior and rate of the northern Elsinore cannot be directly inferred from this.
Farther south, Magistrale and Rockwell (1996) documented a long-term slip rate of ~2.8 mm/yr for the
central Elsinore fault, averaged over the past 900 ky. Near the southern end of the Elsinore fault, in the
Coyote Mountains, Rockwell et al. (2019) resolved a slip rate of 2.4±0.4 mm/yr using U-series dating of
pedogenic carbonate from alluvial fans that have been offset by the fault. Using the same methods, Fletcher
et al. (2011) determined a slip rate of 1.6±0.4 mm/yr at the very southern end of the Elsinore fault, just
north of where displacement dies to zero as slip is transferred southward to the Laguna Salada and related
faults.

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Figure 7.1: Tectonic setting of southern California, showing (a) the main active faults of southern
California, including the San Andreas fault system and the Eastern California shear zone; and (b) map of
the Elsinore fault system. The background is the 30-m digital elevation model of California (Anon, 2000).
In this study, I examined the Glen Ivy segment, a ~40-km-long section of the Elsinore fault that is
bounded to the south by a right step across Lake Elsinore, and to the north by the bend to the Whittier fault
segment. The Glen Ivy segment most recently ruptured during the 1910 ~M6-6.2 Glen Ivy earthquake,
which produced ~25 cm of right-lateral displacement at Glen Ivy Marsh (Rockwell, 1989; Figure 7.2a) and
45 cm of right-lateral displacement at the Glen Eden site ~6 km farther south (Rockwell et al., 2016).
Paleoseismic trenching at Glen Ivy Marsh revealed at least seven ground-breaking earthquakes in the past

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~1,200 years and an average earthquake recurrence interval of ~180 years (Rockwell et al., 1986; Rockwell,
2017).
7.4. The Glen Eden site
The Glen Eden site is located on the northern Elsinore fault, also referred to at this location the Glen
Ivy North fault; the parallel Glen Ivy South fault is a shorter normal fault located ~1 km to the west for
which no Holocene activity has been demonstrated (Figure 7.2a). All dextral strike-slip along this stretch
of the Elsinore fault appears to be accommodated on Glen Ivy North strand, which I study here. The Glen
Ivy North strand also accommodates some vertical movement, at least at the Glen Ivy Marsh site, where
trench exposures indicate ~3 m of vertical separation in the last 1000 years (Rockwell et al., 1986; Figure
7.2a).
When describing geomorphic surfaces, I use the terminology established by Millman (1988), who
mapped the area between the Glen Eden site and a site ~4 km northwest of Glen Ivy Marsh (Figure 7.2a).

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Figure 7.2: (a) Geomorphology of the northern Elsinore fault around Glen Ivy (lidar data from NOAA
2003). (b) Geomorphological interpretation of the Glen Eden study site using terrace designations of
Millman (1988). The background image uses an orthorectified 1953 air photograph (UCSB Air Photograph
Database; Flight AXM_1953B, Frame 13K-7) that was taken prior to development. White dots are sample
locations.

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The Glen Eden site consists of a river canyon, called Indian Canyon, incised into old alluvial gravels
that cap the local hilltops (Qf10+). This major canyon was subsequently partially filled by the upper reaches
of a major alluvial fan complex (Qf5), the remnants of which extend across the fault and downstream to the
northeast for several kilometers. Several younger fluvial terraces are inset into the Qf5 fan deposits. The
riser between one of these younger terraces (Qf3) and the northwestern edge of the Qf5 surface has been
offset by the Elsinore fault and is preserved on both sides of the fault. I use vintage-1953 air photographs
to cross-validate the interpretation of the different geomorphic surfaces (Figure 7.1, Figure 7.2) because
subsequent to the initial mapping by Millman (1988), the site has been locally disturbed by excavations for
road cuts and building construction. It is important to note, however, that all major geomorphic features
have been at least partially preserved intact.
7.5. Offset measurements
To measure the geomorphic displacements recorded at Glen Eden, I combined field observations with
mapping on aerial lidar data (Anon, 2019) and the ortho-rectified, pre-development 1953 air photographs
(Flight AXM_1953B, Frame 13K-7, 1953; courtesy of UCSB Library Geospatial Collection), which
provided additional confidence for the characterization of each landform.
I identified two offset features that can be restored across the fault – the Qf5/Qf3 riser and the inner
edge of the Qf5 terrace buttressed against older gravels in the canyon wall (Figure 7.2). Using hillshaded,
contoured topography and other visualizations from the lidar-derived digital terrain model (DTM), I
progressively back-slipped one side of each offset feature relative to the other until I visually determined
the maximum and minimum sedimentologically plausible offset values (thus spanning 95% confidence
limits), as well as a preferred value.

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Figure 7.3: Glen Eden site geomorphology. The background layer is a 1953 air photograph (UCSB Air
photo Database, Flight AXM_1953B, Frame 13K-7), and the geomorphological interpretation is inspired
from the original mapping of Millman (1988). (a) Configuration at 44±5 m of back-slip, which restores the
inner edge of the Qf3 terrace preserved on both sides of the fault (black dashed line and white arrows). The
sample pit location is indicated, and the related ages will provide the age for that offset, using a lowerterrace reconstruction (see text for explanations). (b) Configuration at 190+25/-15 m of back-slip that restores
the inner edge of the Qf5 terrace deposits that cap the local top of the incised canyon wall, north-east of the
fault, with the linear eroded wall of Indian Canyon upstream of the fault. Location of samples GE22-05 to
GE22-08 are indicated. Once obtained, their age will provide the age of that offset.
The piercing line I use to restore the smaller offset is defined by the Qf5/Qf3 riser along the southeastern
wall of the drainage. Back-slipping the fault by 44±5 m restores this riser well (Figure 7.2a, Figure F.3). To
restore the larger offset, I use a piercing line defined by the linear inner edge of Qf5 deposit at the base of
the riser up to the incised older fluvial gravel deposits (Qf10+) that cap the local hills atop the wall of Indian
Canyon north of the fault, and the linear reach of the incised canyon wall upstream (southwest) of the fault.
A backslip of 190+25/-15 m restores these linear upstream and downstream features well, as shown in Figure
7.2b and Figure F.4. Subsequent erosion after abandonment of the Qf5 terrace has trimmed the southeastern
canyon wall immediately southwest of the fault, and right-lateral strike-slip has carved a curved channel
wall that was then infilled by the younger Qf3 fluvial terrace deposit. This offset represents a robust
maximum offset, but is also our preferred offset. Smaller restorations result in a sedimentologically less
plausible “z” bend in the canyon wall at the fault.

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7.6. Age determinations
My collaborators and I sampled the landforms at Glen Eden site using a sedimentologically informed
protocol for infrared stimulated luminescence (IRSL) dating (Rhodes, 2015). We collected eight samples,
four of them taken from a pit hand-dug into the Qf3 terrace deposit, and four of them taken at different
places on a vertical exposure in a road cut into the Qf5 terrace deposits. These ages will provide constraints
on the timing of abandonment of terrace surfaces Qf3 and Qf5. Specifically, these ages will date the time
when the Indian River started abandoning its floodplain to incise into these surfaces.
The samples we collected were processed according to the post-IR-IRSL225 K-feldspar single grain
procedure (Bronk Ramsey, 2001, 2017; Rhodes et al., 2003). The final ages are still pending, but once these
are completed I plan to use a two-step Bayesian age model (based on Zinke et al. (2017, 2019, 2021)) to
refine the terrace ages on the basis of stratigraphic relationships of known relative age. The first step of the
age model will trim the gravel and silt ages in each location according to their lithostratigraphic ordering.
That will provide age estimates for each sample constrained by the results of the other samples in their
respective pit or location. The second step will consist of building a morpho-stratigraphic sequence age
model for the two terraces.

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Figure 7.4: Three-dimensional schematic diagram showing relationship between the fluvial terraces
studied at the Glen Eden site and the locations of IRSL sample locations on surfaces Qf3 and Qf5.
7.7. Slip-rate determination
Once the luminescence ages are determined, the aim is to obtain a slip-rate record with two increments,
since we will have time constraints on two displaced geomorphic markers: risers Qf10/Qf5 and Qf5/Qf3.
For river and streams that have sufficient erosive power to laterally trim faulted risers, as is the case for the
Indian Canyon river that has incised the different Quaternary surfaces at Glen Eden, the riser age is best
constrained by the abandonment age of the lower terrace tread (e.g., Hubert-Ferrari et al., 2002; Van Der
Woerd et al., 2002; Mériaux et al., 2004; Cowgill, 2007; Zinke et al., 2017, 2019). This model, called the
“lower-terrace” reconstruction (Cowgill, 2007), likely applies to the two offsets studied here.
Therefore, the two increments of the slip-rate record will be: (1) an increment averaged between today
and the age of Qf3, corresponding to the age of the 44 m offset, and (2) another increment averaged between
the age of Qf3 and the age of Qf5, the latter of which dates the age of the 190 m offset.

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I will use a Markov Chain Monte Carlo simulation to take into account all the probability density
functions of the offsets and the luminescence ages, to retrieve allowable slip-rates within uncertainties. This
analysis will be based on the RISeR code established by Zinke et al. (2017).
7.8. Elements of discussion
This study of the Elsinore fault, once completed, will allow me to compare two slip rates averaged over
different time scales to determine whether there are any variations in the overall slip-rate record, as might
be expected for a fault embedded in a complex plate-boundary fault system such as that in southern
California (Gauriau and Dolan, 2021 - CHAPTER 2). Based on soil age determinations (Millman, 1988),
we expect the age of surface Qf5 to be several tens of thousands years old, which will provide a long-term
slip rate for the northern section of the Elsinore fault. This large displacement will average over many
earthquakes and will therefore not bear any information regarding potential variations in slip-rate behavior,
which we might expect to see in incremental slip-rate data averaged over displacement scales of a few tens
of meters (Dolan et al., 2023). In this regard, the smaller offset (44 m) dated by the age of abandonment of
Qf3, may or may not provide a different slip-rate value from the long-term rate, as this is on the edge of the
displacement range that likely encompasses several tens of earthquakes. But paleoseismic trench results at
the Glen Ivy Marsh site on the North Glen Ivy strand reveal that the Elsinore fault ruptures with an average
recurrence of ~180 years and an average slip per event of ~90 cm (Rockwell et al., 1986; Rockwell, 2017).
Thus, the 44 m displacement rate I will determine at the Glen Eden site may average over too many
earthquakes to observe any expected variations in slip-rate behavior.
The comparison of geodetic slip-deficit rates and the geologic slip-rate averaged over the 44 m offset
at Glen Eden would enable considerations on the potential seismic hazard posed by the Elsinore fault. As
suggested by Gauriau and Dolan (2024) (CHAPTER 3), a discrepancy between the geodetic slip-deficit
rate and the geologic fault slip rate averaged over small displacements (<~50 m) may provide an indication
on how a fault is likely to behave in the near future. For example, if the Elsinore fault has been slipping at

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~5 mm/yr (as suggested in published studies from Millman and Rockwell, 1986; Vaughan, 1987; Millman,
1988; Vaughan et al., 1999; Rockwell et al., 2000), its slip-deficit rate of 2.8±1.0 mm/yr given by geodetic
compilation of California faults by Evans (2018) suggests that the Elsinore fault may have entered a slow
mode. This would potentially reveal a decelerating phase of elastic strain accumulation originating from
the ductile shear zone roots of the seismogenic Elsinore fault, and signaling a potentially lower near-future
seismic hazard.
Finally, this study will provide an additional slip-rate datum on the Elsinore fault zone, which is a key
input into probabilistic seismic hazard models, and will add to the growing global data set of potential
spatial variations of slip rates along the fault.
7.9. Acknowledgements
This project was funded by SCEC grant 22124 (Dolan and Rockwell) and a GSA Graduate Research
grant (Gauriau). I would like to thank field assistants and colleagues Chris Anthonissen, Caje Weigandt,
Dannielle Fougere, and Luke Gordon. Chris Anthonissen also helped generate a digital elevation model
from 1981 air photographs of the site, with the Agisoft software package.

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CHAPTER 8 Islands of chaos in a sea of periodic earthquakes: How chaotic
earthquake recurrence patterns explain behavior of one of the most regular
strike-slip faults in the world
This chapter is based on the published article:
Gauriau, J., Barbot, S., Dolan, J. F. (2023). Islands of chaos in a sea of periodic earthquakes. Earth and
Planetary Science Letters 618: 118274.
8.1. Abstract
Long paleoseismic records on mature faults suggest potentially chaotic recurrence patterns with cycles
of strain accumulation and release that challenge simple slip- or time-predictable recurrence models. In
apparent contradiction, the relatively small variability of earthquake recurrence times on these faults is often
characterized as quasi-periodic, implying much regularity in the underlying mechanics. To reconcile these
observations, we simulate one of the longest paleoearthquake records – the 24-event record from the Hokuri
Creek site on the Alpine fault in New Zealand – using a physical model of rate- and state-dependent friction.
In a parameter space formed by three non-dimensional parameters, a sea of parameters produces periodic
earthquake recurrence behavior. Only a few models are characterized by fundamentally aperiodic
recurrence patterns, in parametric islands of chaos. Complex models that produce partial and full ruptures
of the Alpine fault can explain the earthquake recurrence behavior of the Alpine fault, reproducing up to
11 consecutive events of the Hokuri Creek paleoseismic record within uncertainties. The breakdown of the
slip- and time-predictable recurrence patterns occurs for faults that are much longer than the characteristic
nucleation size. The quasi-periodicity of seismic cycles is compatible with the nonlinear and potentially
chaotic underlying mechanical system, posing an inherent challenge to long-term earthquake prediction.

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8.2. Introduction
Our understanding of the recurrence pattern of large earthquakes relies on paleoseismic records, which,
in exceptional cases, span prolonged periods, as for the Dead Sea fault, with 12 recognized events in the
past 14,000 years (Ferry et al., 2011), the San Andreas fault (SAF) at Wrightwood, with 15 characterized
events since ca. 500 CE (Weldon et al., 2004), and the San Jacinto fault at Hog Lake, with 21 events
documented since 1,800 BCE (Rockwell et al., 2015). In those studies, the seismic cycle is often
characterized by the coefficient of variation (CoV) of the recurrence times, associated with quasi-periodic
(CoV < 1), random (CoV~1) or clustered (CoV > 1) events (e.g., Kagan and Jackson, 1991). For example,
the Dead Sea fault is characterized by earthquake recurrence times ranging from random to clustered
(Marco et al., 1996), whereas other faults reveal quasi-periodic behavior, such as the San Jacinto fault’s
record at Hog Lake with a CoV of the recurrence times of 0.57 (Rockwell et al., 2015). Similarly, the San
Andreas fault’s recurrence behavior at Wrightwood is quasi-periodic, with a CoV~0.7 (Scharer et al., 2011),
and the SAF Carrizo Plain section is characterized by a CoV of earthquake recurrence of ~0.5 (Akçiz et al.,
2010).
The evolution of the slip deficit, i.e., the difference between the expected slip based on the average
loading rate and the actual slip per event, provides another means of characterizing the recurrence pattern
(Shimazaki and Nakata 1980). For a slip-predictable behavior, the final stress is constant, but the initial
stress can vary from event to event, implying a correlation between recurrence time and displacement
between two successive earthquakes. Time-predictable behavior occurs when the amount of time following
an earthquake depends upon its size, implying rupture initiation at a fixed yield stress. Testing the validity
of the time- or slip-predictable models in nature requires pairing records of paleo-earthquake ages with slipper-event data. Slip-predictable behavior has been shown in a few locations with relatively short records.
For instance, both the Carrizo Plain record, based on the six most-recent earthquakes (Akçiz et al., 2010),
and the slip history of the North Anatolian fault, based on the displacements and ages of the past four major

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earthquakes (Kondo et al., 2010), suggest slip-predictable earthquake behavior. However, the timepredictable model has been systematically rebutted in other cases (e.g., Wechsler et al., 2014).
The quasi-periodic behavior of earthquakes is often interpreted to indicate regularity in the underlying
fault mechanics, implying bounds on the dynamics or persistent mechanical conditions within the fault
zone. Conversely, if paleoseismic records deviate from the time-predictable and slip-predictable models,
this suggests non-stationary physical conditions, such as changing loading rate or fault microstructure,
affecting rupture nucleation and propagation from one event to the next. This conundrum is perhaps best
exemplified by the 24-event, 7,900-year-long, Hokuri Creek (HC) paleoseismic record on the Alpine fault
in New Zealand, one of the longest ever produced. The catalog features seismic events every 329 ± 68 years
(±1σ; Figure 8.1c) with a very low CoV of ~0.3 (Berryman et al., 2012b), qualifying the southwestern
Alpine fault as quasi-periodic (Figure 8.1b, c). Despite the relatively regular recurrence pattern, inferred
slip-per-event data suggest that the HC paleoseismic record departs markedly from both behaviors predicted
by time- and slip-predictable models (Figure 8.1d). The record is actually characterized by internal
variability of pre- to post-event recurrence time (Figure 8.1e).
These observations raise fundamental questions: What are the underlying mechanics responsible for an
apparently periodic seismic cycle that (a) breaks the slip- and time-predictable patterns, (b) produces a
relatively broad range of recurrence intervals, and (c) can be characterized by a low CoV? To address these
questions, we use numerical simulations to explore the physical parameters that control the recurrence
pattern of earthquakes on the Alpine fault. We identify the physical conditions most compatible with the
paleoseismic record at the HC site. When the fault is sufficiently unstable, the recurrence patterns evolve
from periodic to aperiodic. The transition involves repeating cycles of multiple earthquakes, whereas the
truly aperiodic sequences result from deterministic chaos. Some of these models can explain the most
consecutive events – more than even documented in most other paleoseismic records – while featuring a

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low CoV and digressing from the slip- and time-predictable models. Finally, we discuss the possible
underlying causes for the earthquake recurrence behavior of the Alpine fault.
Figure 8.1: Overview of the Hokuri Creek (HC) paleoseismic site and data (modified after Berryman et
al., 2012b). (a) Location of the HC paleoseismic site within the Alpine fault system, New Zealand
(Langridge et al., 2016). (b) Ages of 24 surface-rupturing earthquakes on the Alpine Fault at HC. Intervals
are the 95% brackets inferred from radiocarbon ages. The red cross refers to the most recent event (MRE)
in 1717 C.E. (De Pascale and Langridge, 2012). (c) Recurrence time intervals, with probability density

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functions. (d) Slip deficit of the HC record through time, with inferred average slip per event of 7.5 ± 2.5
m, and its relation to the time-predictable (upper bound) and slip-predictable (lower bound) models, as
defined by Shimazaki and Nakata (1980). (e) Plot of the time to succeeding event against the time since
preceding event for the HC record.
8.3. Methods
We consider a two-dimensional approximation in condition of in-plane strain relevant to long strikeslip faults such as the Alpine fault. Although some key end-members such as period-multiplying cycles of
slow and fast ruptures can only be accounted for in three-dimensional models (Veedu and Barbot, 2016;
Barbot, 2019b; Veedu et al., 2020), the two-dimensional approximation is sufficient to capture recurrence
patterns and rupture styles, which is the target of this study.
8.3.1. Governing equations and physical assumptions
Our model incorporates the constitutive relationships governing fault slip along the fault over multiple
seismic cycles (Dieterich, 1979; Rice and Ruina, 1983; Ruina, 1983). For the frictional constitutive
behavior, we assume a physical model of rate- and state-dependent friction in isothermal conditions, where
the shear traction is formulated in the multiplicative form (Barbot, 2019a, 2022):
= 0̅ (

0
)

0
(
0

)

0
where μ0 is the static coefficient of friction, ̅ is the effective normal stress, L is the characteristic weakening
distance that controls the rate of weakening, V0 is the reference sliding velocity, and V is the local sliding
velocity. The non-dimensional parameters a and b are power exponents that represent the velocity- and
state-dependence of the frictional response. Specifically, the velocity dependence at steady-state relates to
whether (b - a) is negative, in which case friction is velocity-strengthening, leading to stable creep behavior,
or (b - a) is positive, in which case the constitutive relationship is potentially unstable, i.e., velocityweakening (Rice and Ruina, 1983; Ruina, 1983). The state variable ϴ (s) represents the surface memory of

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the sliding history. It classically represents the age of the contact and follows, under isothermal conditions,
the aging law (Dieterich, 1979; Ruina, 1983):
̇ = 1 −

where the first term on the right-hand side corresponds to healing at stationary contact and the second term
corresponds to weakening during contact rejuvenation.
8.3.2. Non-dimensional parameters
Our parametric study involves the exploration of three non-dimensional parameters that are key to the
resolution of the seismic cycle and that exert a strong control on rupture dynamics and styles (Barbot,
2019b). The Dieterich-Ruina-Rice number, defined as
=
( − )̅
≈

ℎ
∗
involves the characteristic weakening distance L, the rate dependence at steady state (b – a), the alongstrike length of the velocity-weakening region W, the effective normal stress ̅, and the rigidity of
surrounding rocks G. The Ru number can be represented as the ratio of the seismogenic zone size to a
characteristic rupture nucleation size h* and represents how unstable a fault can be. Stable-weakening fault
slip is associated with Ru ≤ 1, owing to large nucleation sizes compared to the fault dimension. The Ru
number increases for larger weakening behavior with unstable slip, i.e., for Ru > 1. In other words, the larger
this number, the smaller the resolvable nucleation size of an earthquake, the more variability in potential
earthquake nucleation sizes and hence the more unstable the fault. Former studies have shown that for a
fixed value of 0.6 for the coefficient of friction μ0, the physical properties of the velocity-weakening region
that fall in increasing values of Ru exhibit cycles of characteristic and periodic ruptures, transitioning to

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more chaotic sequences (e.g., Cattania, 2019; Barbot, 2019b). In these cases, higher values of Ru seem to
give rise to chaotic cycles with full ruptures followed by partial ruptures of varying sizes.
The second non-dimensional parameter explored in this study is the ratio of frictional parameters that
control the dynamic and static stress drops:
=
−

.
This parameter controls the state evolutionary effects of rate- and state-dependent friction and the
emergence of complex slow-slip events. It falls into a range between zero and one for velocity-weakening
patches, with Rb approaching zero for near-neutral weakening and Rb approaching one for strong
weakening. The terms a and b control the static and dynamic stress drops that occur after the direct
strengthening effect at increased slip speed observed in velocity-step laboratory experiments. Therefore,
the Rb number also controls the ratio of dynamic to static stress drops during rupture in some conditions,
thereby affecting the style of the rupture.
Laboratory experiments on granite gouge (Blanpied et al., 1995, 1998) indicate a=10-2
and b=1.4·10-2
at depths and temperatures relevant to the seismogenic zone. This implies an Rb value of 0.286, a value that
is typically used in numerical models of the seismic cycle. In reality, large deviations from Rb =0.286 can
be expected in nature due to the presence of different rock types, different gouge layer thickness, and
different temperature and pore pressure conditions. Vanishing values create the conditions for seismogenic
slow-slip events (Nie and Barbot, 2021), i.e., slow-slip events that produce small low-frequency
earthquakes during their sluggish propagation. In contrast, larger values represent strong weakening during
seismic ruptures, which is hypothesized to be a common mechanism for large earthquakes.
Finally, we explore the reference static coefficient of friction μ0, the ratio of the shear stress to the
normal stress at failure, which stands for the amount of friction that needs to be overcome to start the process

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of fault motion. This parameter controls the strain and stress capacity of the brittle crust, and provides a
maximum bound on the static friction drop during earthquakes. When the rate dependence at steady state
is significantly smaller than the coefficient of friction, the effect of μ0 on fault dynamics is thought to be
negligible. However, μ0 greatly influences the seismic cycle as it approaches the value of (b - a) in the
velocity-weakening region (Barbot, 2019b). Rocks are usually characterized by friction coefficients
between 0.60 and 0.85 under high effective normal stress (Byerlee, 1978), but the frictional strength of a
rock may be highly reduced in a fault zone due to damage accumulation, fracturing, shear fabric formation
(e.g., Collettini et al., 2009; Lockner et al., 2011; Yassaghi and Marone, 2019), the presence of
phyllosilicates (e.g., Tesei et al., 2012; Copley, 2018; Ikari, 2019), or fault maturity in terms of long-term
cumulative slip. In fact, the stress orientation around major faults (e.g., San Andreas, North Anatolian)
indicates a low value (e.g., Carpenter et al., 2011, 2015; Pınar et al., 2016). As the reference friction
coefficient affects the dynamics of rupture and is poorly known in nature, we explore possible values within
a range applicable to seismic events, in order to document the effects of this parameter on the seismic cycle.
8.3.3. Model architecture
We model the structurally isolated southwestern Alpine fault as a two-dimensional (2D) structure of a
400-km length which is characterized by unstable, velocity-weakening friction (Figure 8.2). We consider a
two-dimensional approximation because the numerical complexity of a finite fault constrains the range of
model parameters that can be explored effectively (Erickson et al., 2020; Jiang et al., 2022). Additional
400-km sections on each side are added as velocity-strengthening regions. We develop quasi-dynamic
simulations of seismic cycles with adaptive time steps, with the 4/5th
-order Runge-Kutta method, to explore
a broad spectrum of seismic and aseismic activity while maintaining high numerical accuracy (Barbot,
2019b). We use the spectral boundary-integral method to efficiently resolve the stress interactions during
rupture dynamics (Barbot, 2021). We consider a point located at a quarter distance within the velocityweakening region as a proxy for the HC site. The mirror point located across the seismogenic zone is

206
another viable proxy because of the symmetry of the model setup and produces virtually identical results
of interest.
Figure 8.2: Representation of the Alpine fault zone model with a reference map (a) indicating the location
of the Hokuri Creek site. (b) Distribution of the rate dependence at steady state b-a (kept at -4·10-3
in the
velocity-strengthening zone, and at +4·10-3
in the velocity-weakening zone, for all models in this study)
and parameters a (kept at 10-2
for all models) and b (varied for different values of Rb) throughout the total
modeled length of the fault. (c) Example plot of the cumulative slip along the fault for Ru=95, Rb=0.286,
and μ0=0.50. The cumulative slip is plotted for a total of 17 events in the selected simulation, between years
12,285 and 13,714 (within a whole 20,000-year-long simulation). A proxy for the location of HC is shown
and the vertical line above it shows where the sampling is done. The orange isochrons feature cumulative
coseismic slip every 20 seconds, and the gray contours show slip isochrons every 20 years in the
interseismic periods.
The fault is loaded at a uniform rate of VL = 23 mm/yr (or 7.3·10-10 m/s), in agreement with the longterm steady slip-rate estimate of 23 ± 2 mm/yr in the southwestern section of the Alpine fault (Berryman et
al., 1992; Sutherland et al., 2006), and as inferred from the time recurrence intervals of the HC paleoseismic

207
record and its assumed amount of slip per event (Berryman et al., 2012b). Indeed, the magnitudes of the
recorded events are assumed to be on the same order of the most recent event (Mw = 8.3) and the
antepenultimate one (Mw = 7.6) (Sutherland et al., 2007). Similar large horizontal single-event
displacements of 7.5 m are inferred for all recorded events (see Appendix G for details).
For all models considered, the numerical grid size is chosen to resolve the cohesion zone on the rupture
front:
=

̅
We choose a grid size of 150 m, smaller than Lb/6 to avoid numerical difficulties to resolve models
when we explore Rb values that are closer to one.
We try to constrain the inter-event time (Tr) to the recurrence time observed at HC, i.e., 329±129 years,
which depends on the effective normal stress at first order, but also on the characteristic weakening distance
and essentially all the other frictional parameters. Hence, we explore ̅ in the range 11-17 MPa as part of
the exploration of the model space. The final results presented here are for a single value, i.e., ̅=13 MPa.
Complexity towards the north of the Alpine fault, due to proximity with the Marlborough fault system
is not accounted for in our model, although it may impact the behavior of the northern central section of the
fault (CHAPTER 2: Gauriau and Dolan, 2021). The apparent symmetry of the recurrence pattern behavior
of our models suggests that including some structural complexity (nearby sub-parallel faults, faults splaying
off from the northern central Alpine fault and partitioning of the whole relative motion accommodation)
would be necessary to obtain a more realistic behavior which provides different types of behaviors from a
section to another, as is starting to be better understood (Howarth et al., 2018). Nonetheless, the technical
complexity of such models constrains us to a simpler and straightforward setup with a single-stranded,

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simple, isolated fault, for several reasons including computing time, necessity of parallelism between
modeled faults, and high-demanding computing time for 3D models.
8.3.4. Model exploration strategy
To keep the model exploration tractable, we use the following strategy: We first explore the set of
parameters Ru and μ0 for a fixed Rb = 0.286, a value typical of fault zones derived from laboratory
experiments (Tse and Rice, 1986; Blanpied et al., 1995). We then jointly explore the Rb and Ru numbers
using a friction coefficient μ0 selected from the previous step. For each combination of parameters, we
conduct 20,000-year-long simulations and analyze the behavior of the fault after a few thousand years to
mitigate the effects of the initial conditions (e.g., Barbot, 2020; Sathiakumar and Barbot, 2021). For each
simulation, we select the 24-earthquake interval in the remaining period that best explains the HC record.
8.4. Results
We first examine the earthquake recurrence patterns that emerge in the parameter space (Figure 8.3).
We document the periodicity of earthquake cycles based on recurrence times alone, irrespective of the
amount of slip, rupture velocity, or other source mechanisms. We consider slip events as earthquakes if the
peak velocity exceeds 1 mm/s. Under most conditions, the seismic cycle converges to a repeat cycle
consisting of one or more earthquakes, which we refer to as period-n cycles, where n is the smallest number
of earthquakes that form a repeat sequence. The different types of earthquake recurrence time behavior are
exemplified in Figure 8.3c to h and in Figure G.2.
In the {Ru; μ0} parameter space, truly periodic behavior (period-1) that follows the time- and slippredictable models is observed for low Ru values (10 to 30), at different μ0 values, as well as for Ru values
spanning 50 to 95 for relatively low friction coefficients (Figure 8.3a). Truly aperiodic behavior, i.e.,
deterministic chaos, appears in isolated islands of parameters and in a somewhat larger model space for Ru

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between 45 and 55 and μ0 between 0.40 and 0.55. Other cases that deviate from the time- and slippredictable models represent period-n behavior, i.e., period-n cycles with n > 1. Similarly, truly periodic
behavior is observed within large sections of the {Ru; Rb} parameter space, and chaotic behaviors appear
in isolated patches (Figure 8.3b).
Figure 8.3: Overview of the periodicity styles obtained for σ =13 MPa in the parameter spaces formed by
Ru and μ0 and by Ru and Rb at the sampling point (marked HC in Figure 8.2c). In (a) and (b), each color
refers to a type of earthquake repeat time periodicity, and insets (c) to (h) show the evolution of slip deficit
through time for the last 8,000 years of the whole simulation. (f) refers to the period-5 model that fits well
the HC data, using both ranking criteria we use in Figure 8.5c and Figure 8.5d. (h) refers to the chaotic
model, also shown in Figure 8.2c, Figure 8.5e, and Figure 8.5f, that fits the HC data well, using both ranking
criteria.
We aim to quantitatively explain the recurrence times at HC within the explored parameter space, which
will naturally produce low CoV and a set of events that resembles the succession of paleoearthquakes of
the HC record. We therefore compare the numerical simulations of seismic cycles with the HC paleoseismic
record. To mitigate any bias in recurrence times from aftershocks and other small earthquakes, we trim the

210
simulated sequences according to paleoseismic assumptions. Those assumptions tackle: (a) the timing of
successive earthquakes, i.e., two earthquakes occurring within a brief amount of time will not necessarily
be distinguishable in the stratigraphy, and (b) the amount of slip per event, i.e., a small-magnitude event
might not be recorded in the stratigraphy (see further details in Appendix G). We then compare the newly
obtained catalog of recurrence times with the HC data. For each simulation, we select the sequence of 23
recurrence times that features the maximum number of successive recurrence times falling within the 95%
confidence intervals of the HC record. This conservative criterion is used to discard models that explain
data for short, isolated intervals and to account for uncertainties in the paleoseismic record. The results are
displayed in Figure 8.4 for the parameter spaces Ru and μ0 at fixed Rb = 0.286 (Figure 8.4a) and for Ru and
Rb at fixed μ0=0.50 (Figure 8.4b), correspondingly to Figure 8.3.

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Figure 8.4: Results of fitting of best sequences of 23 recurrence intervals to the Hokuri Creek paleoseismic
data (a) for the parameter spaces formed by Ru and μ0 and (b) by Ru and Rb. (a) and (b) display the highest
number of successive recurrence intervals that fall within the 95% confidence intervals of the HC
paleoseismic time recurrence intervals for each simulation. The best results are obtained for simulations
with a period-2 earthquake recurrence behavior (shown as black squares), and are displayed in insets (c) to
(f). (c) shows the recurrence time sequence of the selected interval for the period-2 simulation of parameters
Ru=80, μ0=0.55, Rb=0.286, compared with the HC data. For this selected sequence, the CoV is 0.264 and
the average recurrence time is 277.9 yr. (d) shows the entire 20,000-year-long simulation for the preferred

212
chaotic model and the selected interval in light of the HC data. (e) and (f) show the same series of plots for
period-2 simulation of parameters Ru=50, μ0=0.50, Rb=0.50. This selected sequence is characterized by a
CoV of 0.322 and an average recurrence time interval of 278.0 yr.
The best fit models occur for period-2 behaviors (Figure 8.3a), at {Ru=75; μ0=0.60} and {Ru=80;
μ0=0.50 and 0.55} for fixed Rb=0.286, as well as for {Ru = 50; Rb=0.50} for fixed μ0=0.50 (Figure 3b), with
13 successive recurrence times that fall within the 95% confidence intervals of the HC record. Two of these
best-fitting models are displayed in Figure 8.4c to f. Other good fit models produce 11 or 10 successive
recurrence times within the 95% confidence intervals of the observations. This occurs for {Ru=90; Rb=0.50;
μ0=0.50}, corresponding to a period-2 behavior, and for {Ru=100; Rb=60; μ0=0.50} corresponding to a
chaotic behavior (Figure 8.4b), as well as for the simulations with parameters {Ru=60; μ0=0.60; Rb=0.286},
exhibiting a period-11 behavior, {Ru=90; μ0=0.60; Rb=0.286}, characterized by a period-5 behavior and
{Ru=95; μ0=0.50; Rb=0.286}, showing deterministic chaos.
Since the behavior of the Alpine fault at HC is neither slip-predictable, nor time-predictable (Figure
8.1e), and since the earthquake recurrence time is not perfectly constant (CoV of 0.3), we should rule out
any type of model that manifests true periodicity (equivalent to CoV=0) (Figure 8.1e). Therefore, we further
select the models with a distribution of recurrence times most compatible with the HC record in a statistical
sense. We perform two-sample Kolmogorov-Smirnov tests between the HC data and the selected best
sequences of 23 recurrence times, i.e., the ones that feature the maximum number of successive recurrence
times falling within the 95% uncertainties of the HC record, in order to test if the two populations come
from the same distribution. Our working null hypothesis H0 is that the two populations were drawn from
the same distribution. We look at the related p-values (i.e., the probability that the Kolmogorov-Smirnov
statistic is larger than the maximum difference between the cumulative distributions of the two samples,
assuming H0 is true) for each of the performed tests and display them in the explored parameter spaces in
Figure 8.5a and b. The smaller the p-value, the smaller the probability of making an error by rejecting H0.

213
In other words, higher p-values relate to recurrence time interval distributions that are closer to the
distribution of HC recurrence times.
Figure 8.5: Additional method for ranking the selected sequence of 23 intervals (Figure 8.4), with the use
of p-values of the related tested null-hypothesis H0: “The HC record and the selected simulated record were
drawn from the same distribution”, using a two-sample Kolmogorov-Smirnov test. The p-values are

214
displayed in (a) for the parameter space formed by Ru and μ0 and in (b) for the parameter space formed by
Ru and Rb. The highest p-value is obtained for the simulation of parameters Ru=90, Rb=0.286, and μ0=0.60,
characterized by a period-5 recurrence time behavior, which also has a good fit with the HC data, using the
ranking criterion of Figure 4. Insets (c) and (d) show the recurrence time sequence of the selected interval
for this period-5 model, compared to the HC record. For this sequence, the CoV is 0.333 and the average
recurrence time is 307.4 yr. The other simulation that gathers a high p-value and a large number of
successive recurrence times falling within 95% uncertainties of the HC record is the chaotic simulation of
parameters: Ru=95, Rb=0.286, and μ0=0.50. (e) and (f) show the recurrence time sequence of the selected
interval for this chaotic model, compared to the HC record. For this selected sequence, CoV=0.380, the
average repeat recurrence time is 277.4 yr.
The overlap of the best models shown in Figure 4a and 4b with the highest p-values shown in Figure
8.5a and b helps us narrow down the allowable frictional parameter space that best explain the HC record.
With this additional consideration, we find that the best simulations are obtained for {Ru=90; μ0=0.60;
Rb=0.286}, characterized by a period-5 behavior, and {Ru=95; μ0=0.50; Rb=0.286}, showing chaotic
behavior. The period-5 model explains 10 successive recurrence times (i.e., 11 successive events from
interval Hk8-9 to interval Hk17-18), and a total of 16 disconnected recurrence intervals that fall within the
95% uncertainties of the HC record. The mean recurrence time of the 24-event selected sequence for this
model is 307 ± 102 years, corresponding to a CoV of 0.33, which falls within the 1σ confidence interval of
the mean recurrence time found at HC (Berryman et al., 2012b). Similarly, the chaotic model explains 10
successive recurrence times (i.e., 11 successive events from interval Hk12-13 to interval Hk21-22), a total
of 15 disconnected recurrence intervals that fall within the 95% uncertainties of the HC record, and the
scattered nature of pre- and post-event intervals (Figure 8.4c, d). The mean recurrence time of the simulated
24-event record for this chaotic model is 277 ± 105 years, corresponding to a CoV of 0.38, which falls
within the 1σ confidence interval of the mean recurrence time found at HC, characterized by a CoV of ~0.3
(Berryman et al., 2012b).
8.5. Discussion
Our study suggests that seismic cycles with low CoV that break the time- and slip-predictable endmember recurrence models are related to the nonlinear dynamics of the fault system. The two best-fitting

215
models that explain the most consecutive events and produce a distribution of recurrence times that most
resemble the HC record produce period-5 sequences or aperiodic deterministic chaos. Multiple-periodic
cycles are generally understood as transitional behavior toward chaotic cycles. For example, the transition
from periodic events to deterministic chaos in a Lorenz attractor involves bifurcations with period-doubling
(Lorenz, 1963). Nonlinear dynamic systems showing a period-3 recurrence pattern (see an example in
Figure 3e) are capable of chaotic behavior (Li and Yorke, 2004). Our results support previous studies that
interpret earthquake recurrence as chaotic in historical catalogs (Ito, 1980; Huang and Turcotte, 1990;
Iliopoulos and Pavlos, 2010), earthquake sequences (De Santis et al., 2010; Shelly, 2010), and laboratory
earthquakes (Gualandi et al., 2023). Chaotic sequence of earthquakes can be due to the complexity of the
rate- and state-dependent friction law in spring-slider systems (Becker, 2000; Erickson et al., 2008) and
appear in continuum models for sufficiently small nucleation (Kato 2014, Cattania 2019, Barbot 2019, Nie
& Barbot 2022).
The two best models in the parameter space explored in this study are found for {Ru=90; μ0=0.60;
Rb=0.286}, characterized by a period-5 behavior, and {Ru=95; μ0=0.50; Rb=0.286}, showing chaotic
behavior. These parameters suggest that the Alpine fault is characterized by a small nucleation size relative
to its overall length and by a friction coefficient compatible with Byerlee’s law (Byerlee, 1978). Although
the structural maturity of the Alpine fault would suggest a lower friction coefficient in the seismogenic zone
(Collettini et al., 2009; Boulton et al., 2017; Copley, 2018), rocks cored from the central section in the Deep
Fault Drilling Project reveal friction coefficients of 0.50-0.75 at the relevant temperatures (Boulton et al.,
2012, 2014; Ikari et al., 2014; Valdez et al., 2019), compatible with our findings.
The Ru and Rb numbers control the relative amplitude of different energy sources and sinks during
seismic rupture, i.e., fracture energy, heat, and radiated energy (Kanamori and Rivera, 2006), by affecting
the amount of weakening and the slip-weakening distance, which directly alters the fracture energy. The
trade-off between Rb and Ru displayed in Figure 8.4b, Figure G.4, Figure G.8, and Figure G.9 indicates an

216
upper bound on the fracture energy: For sufficiently large fracture energy per unit area during rupture, the
complex recurrence patterns of HC cannot be reproduced, whether it is due to a large weakening distance
(small Ru) or a large direct effect (small Rb). This suggests that the Alpine fault earthquakes dissipate
relatively little energy in fracture, diverting it instead into seismic radiation and heat.
The small nucleation patch size for the Alpine fault implies a wide range of possible earthquake sizes.
For instance, when Ru=95, the smallest allowable rupture size (~7 km) represents 2% of the fault length.
Although most simulated earthquakes for both models represent unilateral ruptures of the entire fault
(Figure 8.2c and Figure G.11b), a few ruptures break segments of less than 100 km length, only a few of
which reach the HC site. Consistent with this observation, the paleoseismic record of large-magnitude
earthquakes shows that the Alpine fault does not generate frequent earthquakes that are less than a Mw ~7
range, except for aftershocks. Indeed, the magnitudes of the paleo-seismic events are assumed to be on the
same order of the most recent event (Mw=8.1-8.3) and antepenultimate (Mw=7.6) Alpine fault earthquakes
(Sutherland et al., 2007; De Pascale and Langridge, 2012). Full-length ruptures of the entire Alpine fault
are probably the dominant means by which the seismic moment of the fault is released. The last three events
that occurred at HC correlate with the well-constrained ages of ruptures along the central section of the
Alpine fault (Howarth et al., 2016), suggesting that the most-recent event (MRE) in 1717 C.E., as well as
the penultimate and antepenultimate events ruptured both the Central and Southern sections. The MRE not
only ruptured those sections, but also potentially the southern end of the Alpine fault's northern section,
with a calculated magnitude Mw = 8.1–8.3 and a rupture length of ~410–450 km (Yetton and Nobes, 1998;
De Pascale and Langridge, 2012; Stirling et al., 2012; Cochran et al., 2017; Howarth et al., 2018). However,
the time ranges of older events recorded at the HC and John O'Groats sites on the Southern section do not
match those recorded at Lake Ellery on the Central section (Figure 8.2a), indicating that the ruptures
recorded on the Southern section did not simultaneously rupture the Central section as well, and vice versa.
This suggests that ruptures do not consistently break the entire length of the fault. Specifically, 52% of the
events recognized on the Alpine fault are restricted to either the Southern section or the Central section,

217
whereas 48% of the events rupture both sections at the same time (Howarth et al., 2016, 2018; Cochran et
al., 2017).
The average earthquake recurrence intervals found for the two best fit models (277 ± 105 years for the
chaotic model and 307 ± 102 years for the period-5 model) are somewhat shorter than the average
recurrence interval documented by Berryman et al. (2012b) at HC (329 ± 68 years), although they are not
significantly different at the 1σ confidence interval. Some paleoseismic studies at other sites on the
southwestern Alpine fault have revealed shorter average recurrence intervals, such as (a) 291 ± 23 years
(CoV=0.41) for the combination of HC ages and the seven paleoearthquake ages obtained at John O’Groats
wetland (Figure 8.2a), 25 km south of HC (Cochran et al., 2017); and (b) 263 ± 63 years (CoV = 0.26) on
the Central section of the Alpine fault, starting less than 100 km north of the HC site, based on the evidence
of the eight most-recent earthquakes (Howarth et al., 2018). For similar amounts of slip per event to that
inferred in our work at the HC site (7.5 m per event), the recurrence time intervals resulting from these
other sites would require a faster slip rate than the average loading rate used in our study (23 mm/yr). For
instance, Cochran et al. (2017) used the ~ 28 mm/yr long-term slip rate from Barth et al. (2014), which was
based on restoration of ~ 270 ka glacial deposits displaced by the Alpine fault.
This bias could be recovered by employing a different long-term slip rate, which would have minimal
consequences for the seismic cycle behavior beyond changing the recurrence time of earthquakes, but
would be incompatible with the well-constrained incremental slip-rate record of Sutherland et al. (2006)
which was measured over four separate displacement intervals and pertains directly to the section of the
fault containing the HC site. Perhaps other additional physical processes could help to reconcile our results
with both the actual distribution of recurrence intervals obtained at HC, and the recurrence intervals
observed at other sites along the Alpine fault. The frictional evolution of the fault may involve enhanced
weakening mechanisms, such as flash weakening (Beeler et al., 2008), thermal decomposition (Sulem and
Famin, 2009), thermal pressurization (Noda and Lapusta, 2013), or additional thermal effects that modulate

218
strength throughout the seismic cycle (Wang and Barbot, 2020, 2023; Barbot, 2022). Internal structural
complexity of the Alpine fault may also affect the recurrence patterns (Howarth et al., 2021). Another
reason why it is difficult to obtain a simulated sequence that gives an average time recurrence interval close
to the one of HC is that the variability of the record comes mainly from the eight most-recent events, for
which the recurrence intervals vary more (CoV = 0.37) than for the whole record. In contrast, the 15 oldest
recurrence intervals in the HC record indicate more regular earthquake occurrence, with a very low CoV of
0.24.
The numerical simulations obtained from this study provide further insights into the possible underlying
mechanics of seismic cycles that deviate from the time- and slip-predictable models. For a high Ru number,
the nucleation size is small enough to generate full and partial ruptures of the seismogenic zone due to
heterogeneous stress accumulation along the fault. The partial ruptures delay the nucleation of the
subsequent through-going ruptures, breaking the simple time-predictable pattern. To first order, the fault
slip produced during a seismic event is controlled by the rupture length. Consequently, partial ruptures also
create variability in the slip of individual earthquakes, even at uniform stress drops, breaking the slippredictable pattern, as well. Finally, partial ruptures leave behind stress concentrations that will affect
subsequent ruptures. As the stress inherited from previous ruptures affects the initial condition of any
subsequent rupture, a sequence of earthquakes yields an increasingly complex stress distribution along the
fault. As a result, a period-5 model, like the one obtained with parameters {Ru=90; μ0=0.60; Rb=0.286}
accomplishes these complex conditions, and represents an intermediate step towards a fully aperiodic,
chaotic system, as represented by the second model obtained with parameters {Ru=95; μ0=0.50; Rb=0.286}.
Under some conditions, as the ones reached in that latter model, the cycle becomes aperiodic. As the average
recurrence time remains controlled by the structural properties of the fault zone that evolve at time scales
much longer than the seismic cycle – fault length, structural maturity, and the distant boundary conditions

219
of tectonic plates (e.g., Gauriau and Dolan, 2021) – a chaotic seismic cycle such as the one we found still
operates within reasonable bounds, leading to a low CoV.
8.6. Conclusion
We explore a wide range of frictional parameters to model the quasi-periodic earthquake recurrence
pattern of the Alpine fault’s Hokuri Creek paleoseismic record. A sea of parameters produces strictly
periodic or period-n cycles of earthquakes. Only a few islands of parameters within the entire explored
space yield chaotic seismic cycles that break the time- and slip-predictable recurrence patterns and produce
low variations of recurrence times. Two models that explain the Hokuri Creek paleoseismic record exhibit
period-5 and chaotic behaviors, respectively, both of which are the trademark of nonlinear fault dynamics.
These models feature a highly unstable fault with a length much greater than a characteristic nucleation
size, and Byerlee-like friction coefficients. The compatibility of the quasi-periodicity of seismic cycles with
a complex underlying mechanical system poses an intrinsic obstacle to long-term earthquake prediction,
even if the fundamental laws governing fault slip were well established. Modeling approaches adapted to
nonlinear, chaotic systems may be best suited to tackle this challenge.

220
CHAPTER 9 Conclusions
In this thesis, I presented observations, results and discussions that provide important new insights into
fault and fault system behavior over several earthquake cycles, as well as earthquake recurrence and slip
patterns. In CHAPTER 2, I presented the CoCo metric as a tool to correlate the relative structural
complexity surrounding a fault of interest with its variability in slip rate behavior. In CHAPTER 3, I used
this metric to provide an explanation for discrepancies between geologic slip-rate data and geodetic slipdeficit rate values. I found that faults embedded in complex fault networks are more likely to undergo
variations in slip rate, exhibit different geologic slip rate and geodetic slip-deficit rate values, and therefore
are characterized by elastic strain accumulation rates that vary through time. On the other hand, faults that
are structurally isolated exhibit constant geologic slip rates that are equal to current estimates of the elastic
strain accumulation rates, which itself must be constant.
Preliminary reconstructions of incremental fault slip-rate histories on the Kekerengu fault, the
Wairarapa fault and the Elsinore fault illustrate the variability of slip rates for these faults embedded in the
complex fault systems of Marlborough, the North Island dextral fault belt, and the southern San Andreas,
respectively. Specifically, in CHAPTER 4, the study of allowable slip per event at Bluff Station during the
past four to five earthquakes that have been recorded there allow a better understanding of the rupture styles
of the Kekerengu fault. Combined with the slip-rate study of the Kekerengu fault, detailed in CHAPTER
5, we now have a comprehensive record of strain release through time at different places along this fault.
In CHAPTER 6, I documented the preliminary results for a similar study on the Wairarapa at the Waiohine
River site. Once completed, this study will provide further understanding of the interactions between the
Kekerengu fault and its northern continuation to the North Island of New Zealand, i.e., the Wairarapa fault,
as well as the strain release patterns on both faults which underwent historical, recent or relatively recent,
ruptures (2016 and 1855, respectively). In CHAPTER 7, I used similar methods to decipher the slip-rate
behavior averaged over the Pleistocene for the northern Elsinore fault in southern California. This fault is

221
embedded in the San Andreas fault system that is similar to the Marlborough fault system in New Zealand.
Once finalized, this study will provide key information on long-term strain release behavior on the Elsinore
fault, and on fault system mechanics within a densely populated area.
Finally, in CHAPTER 8, the study of the earthquake recurrence pattern of a fault structurally isolated
like the Alpine fault in New Zealand highlights that a quasi-periodic recurrence pattern is best represented
by deterministic chaos, therefore complicating attempts of earthquake forecasts. This study emphasizes the
importance of integrating existing geological datasets to feed earthquake models, and therefore the
significance of paleoseismic data and the knowledge we can acquire from them in multidisciplinary
approaches.
This thesis provides significant insights to the understandings of fault and fault systems behavior in
strike-slip plate boundary settings, and has the potential to question, if not impact, seismic hazard
assessments.

222
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255
Appendix A. Supplements for CHAPTER 2
A.1. Supplementary figures
Figure A.1: Incremental slip-rate data used for the Alpine-Marlborough fault system in New Zealand.

256
Figure A.2: Incremental slip-rate data used for the San Andreas fault system in California.
Figure A.3: Incremental slip-rate data used for the North Anatolian fault system in Turkey.

257
Figure A.4: Incremental slip-rate data used for the Dead Sea fault system in Turkey.

258
Figure A.5: CoCo values “standardized” by respective plate rate (i.e., CoCo value divided by relative plate-motion rate – unit is km-1
) plotted against
slip-rate variability (i.e., highest slip rate of the respective record divided by slowest slip rate of the record) for all sites, for a radius of 100 km. Fault
site acronyms are the same as the ones in Figure 2.7. The number of slip-rate increments in the published records are shown by colors, and the
durations of the entire slip-rate record are shown by the size of the dots.

259
Figure A.6: CoCo values “standardized” by respective plate rate (i.e., CoCo value divided by relative plate-motion rate – unit is km-1
) plotted against
slip-rate variability (i.e., highest slip rate of the respective record divided by slowest slip rate of the record) for all sites, for the three largest explored
radii (100, 150, 200 km). The durations of the entire slip-rate record are shown by the size of the dots. Fault site acronyms are the same as the ones
in Figure 2.7.

260
A.2. Supplementary tables
Table A.1: Active fault database of California - references for each fault and slip rates used for CoCo values computation.
Fault trace Slip rate
Fault name Fault zone Fault trace - reference
Slip-rate
range
(mm/yr)
Reference for slip rate
Agua Caliente fault San Felipe fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Anacapa-Dume fault Anacapa-Dume fault USGS IV fault database 1 - 3 UCERF-3
Arroyo Del Oso fault San Simeon fault zone USGS IV fault database 0 - 0.2 UCERF-3
Arroyo Laguna fault San Simeon fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Arroyo Parida fault Mission Ridge fault system USGS IV fault database 0.2 - 1 UCERF-3
Ash Hill fault Ash Hill fault USGS IV fault database 0 - 0.2 UCERF-3
Avalon-Compton fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 1 - 3 UCERF-3
Banning fault San Andreas fault zone USGS IV fault database 0.2 - 1 UCERF-3
Banning fault (Strand A) San Andreas fault zone USGS IV fault database 0 - 0.2 UCERF-3
Baseline fault Baseline fault USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Big Pine fault Big Pine fault zone USGS IV fault database 0.2 - 1 UCERF-3
Black Mountain fault Harper fault zone USGS IV fault database 0.2 - 1 UCERF-3
Blackwater fault Blackwater fault zone USGS IV fault database 0.2 - 1 UCERF-3
Blake Ranch fault Blake Ranch fault USGS IV fault database 0 - 0.2 UCERF-3
Blue Cut fault Blue Cut fault zone USGS IV fault database 1 - 3 UCERF-3
Bolsa-Fairview fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0.2 - 1 UCERF-3
Breckenridge fault Breckenridge fault USGS IV fault database 0 - 0.2 UCERF-3
Brown Mountain fault Panamint Valley fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Buck Ridge fault San Jacinto fault zone USGS IV fault database 0 - 0.2 UCERF-3
Buena Vista fault Buena Vista fault USGS IV fault database 0 - 0.2 UCERF-3
Bulito fault Santa Ynez fault zone USGS IV fault database 0 - 0.2 UCERF-3

261
Bullion fault Pisgah-Bullion fault zone USGS IV fault database 0.2 - 1 UCERF-3
Burnt Mountain fault Burnt Mountain fault zone USGS IV fault database 0.2 - 1 UCERF-3
Cabrillo fault Cabrillo fault USGS IV fault database 0 - 0.2 UCERF-3
Calico fault Calico-Hidalgo fault zone USGS IV fault database 1 - 3 UCERF-3
Cambria fault Cambria fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Camp Rock fault
Camp Rock-Emerson-Copper Mountain fault
zone
USGS IV fault database
1 - 3
UCERF-3
Campus fault Mission Ridge fault system USGS IV fault database 0.2 - 1 UCERF-3
Casa Blanca fault Crafton Hills fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Casa Loma fault San Jacinto fault zone USGS IV fault database 15 - 20 UCERF-3
Casmalia fault Casmalia fault zone USGS IV fault database 0.2 - 1 UCERF-3
Chatsworth fault Chatsworth fault USGS IV fault database 0 - 0.2 UCERF-3
Cherry Valley fault San Gorgonio Pass fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Cherry-Hill fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0.2 - 1 UCERF-3
Chicken Hill fault Crafton Hills fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Chino fault Elsinore fault zone USGS IV fault database 0.2 - 1 UCERF-3
Claremont fault San Jacinto fault zone USGS IV fault database 15 - 20 UCERF-3
Clark fault San Jacinto fault zone USGS IV fault database 3 - 5 UCERF-3
Cleghorn fault Cleghorn fault zone USGS IV fault database 1 - 3 UCERF-3
Cliff Canyon fault Southern Sierra Nevada fault zone USGS IV fault database 0.2 - 1 UCERF-3
Compton Thrust Leon et al. 2009 1 - 3 UCERF-3
Coon Canyon fault Blackwater fault zone USGS IV fault database 0.2 - 1 UCERF-3
Copper Mountain fault
Camp Rock-Emerson-Copper Mountain fault
zone
USGS IV fault database
0.2 - 1
UCERF-3
Coronado Bank Fault Zone Coronado Bank fault zone USGS IV fault database 3 - 5 UCERF-3
Coronado fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Coyote Creek fault San Jacinto fault zone USGS IV fault database 3 - 5 UCERF-3
Cucamonga fault Sierra Madre fault zone USGS IV fault database 1 - 3 UCERF-3
Cuddy Saddle fault San Andreas fault zone USGS IV fault database 0.2 - 1 UCERF-3
Death Valley Fault Black Mountain fault zone USGS IV fault database 3 - 5 UCERF-3
Demille fault San Gabriel fault zone USGS IV fault database 0.2 - 1 UCERF-3

262
Devils Gulch fault Faults near Oakview and Meiners Oaks USGS IV fault database 0.2 - 1 UCERF-3
Dillon fault San Gabriel fault zone USGS IV fault database 0.2 - 1 UCERF-3
Dolan (Sur fault zone) San Gregorio fault zone USGS IV fault database 1 - 3 UCERF-3
Eagle Rock fault Eagle Rock fault USGS IV fault database 0 - 0.2 UCERF-3
Earthquake Valley fault Elsinore fault zone USGS IV fault database 1 - 3 UCERF-3
East Bullion fault Pisgah-Bullion fault zone USGS IV fault database 0.2 - 1 UCERF-3
East Silverwood Lake fault Cleghorn fault zone USGS IV fault database 0.2 - 1 UCERF-3
East Valley Mountain fault Pisgah-Bullion fault zone USGS IV fault database 0.2 - 1 UCERF-3
East Wide Canyon fault Burnt Mountain fault zone USGS IV fault database 0.2 - 1 UCERF-3
El Paso fault Garlock fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Elsinore fault Elsinore fault zone USGS IV fault database 3 - 5 UCERF-3
Emerson fault
Camp Rock-Emerson-Copper Mountain fault
zone
USGS IV fault database
0.2 - 1
UCERF-3
Espinosa fault Rinconada fault zone USGS IV fault database 0.2 - 1 UCERF-3
Etiwanda Avenue fault Red Hill-Etiwanda Avenue fault USGS IV fault database 0 - 0.2 UCERF-3
Eureka Peak fault Eureka Peak fault USGS IV fault database 0.2 - 1 UCERF-3
Fort Rosecrans fault Point Loma fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Fossil Canyon fault Blackwater fault zone USGS IV fault database 0.2 - 1 UCERF-3
Foxen Canyon fault San Luis Range fault system (South Margin) USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Galway Lake fault
Camp Rock-Emerson-Copper Mountain fault
zone
USGS IV fault database
0.2 - 1
UCERF-3
Gandy Ranch fault San Andreas fault zone USGS IV fault database 0 - 0.2 UCERF-3
Garlock fault Garlock fault zone USGS IV fault database 7 - 10 UCERF-3
Garlock fault, South Branch Garlock fault zone USGS IV fault database 7 - 10 UCERF-3
Garnet Hill fault San Andreas fault zone USGS IV fault database 0.2 - 1 UCERF-3
Gaviotito fault Santa Ynez fault zone USGS IV fault database 0 - 0.2 UCERF-3
Gillis Canyon fault San Juan fault zone USGS IV fault database 0 - 0.2 UCERF-3
Glen Helen fault San Jacinto fault zone USGS IV fault database 1 - 3 UCERF-3
Glen Ivy North fault Elsinore fault zone USGS IV fault database 5 - 7 UCERF-3
Grass Valley fault Cleghorn fault zone USGS IV fault database 0.2 - 1 UCERF-3
Gravel Hills fault Harper fault zone USGS IV fault database 0.2 - 1 UCERF-3

263
Lost Hills Blind Thrust Great Valley Thrust Fault System
Community Fault Model
(SCEC)
1 - 3
Supposed same as
Kettleman and Coalinga
faults
Coalinga Blind Thrust Great Valley Thrust Fault System USGS IV fault database
1 - 3
IV Fault and Fold
Database of the US -
USGS
Kettleman Blind Thrust Great Valley Thrust Fault System USGS IV fault database
1 - 3
IV Fault and Fold
Database of the US -
USGS
Harper Lake fault Harper fault zone USGS IV fault database 0.2 - 1 UCERF-3
Harper Valley fault Harper fault zone USGS IV fault database 0.2 - 1 UCERF-3
Helendale fault Helendale-South Lockhart fault zone USGS IV fault database 0.2 - 1 UCERF-3
Hidalgo fault Calico-Hidalgo fault zone USGS IV fault database 0.2 - 1 UCERF-3
Hollywood fault Hollywood fault USGS IV fault database 0.2 - 1 UCERF-3
Holser fault Holser fault USGS IV fault database 0.2 - 1 UCERF-3
Homestead Valley fault Homestead Valley fault zone USGS IV fault database 0.2 - 1 UCERF-3
Hosgri Fault Zone Hosgri fault zone USGS IV fault database 1 - 3 UCERF-3
Hot Springs fault San Felipe fault zone USGS IV fault database 0 - 0.2 UCERF-3
Indian Hill fault Indian Hill fault USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Indianapolis fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0.2 - 1 UCERF-3
Inglewood fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0.2 - 1 UCERF-3
Javon Canyon fault Javon Canyon fault USGS IV fault database 1 - 3 UCERF-3
Johnson Valley fault Johnson Valley fault zone USGS IV fault database 0.2 - 1 UCERF-3
Kern Canyon fault Kern Canyon fault zone USGS IV fault database 0.2 - 1 UCERF-3
Kickapoo fault Johnson Valley fault zone USGS IV fault database 0.2 - 1 UCERF-3
La Vista fault Faults near Oakview and Meiners Oaks USGS IV fault database 0.2 - 1 UCERF-3
Lakeview fault Sierra Madre fault zone USGS IV fault database 0.2 - 1 UCERF-3
Lavic Lake fault Lavic Lake fault zone USGS IV fault database 0.2 - 1 UCERF-3
Leach Lake fault Garlock fault zone USGS IV fault database 3 - 5 UCERF-3
Lenwood fault Lenwood-Lockhart fault zone USGS IV fault database 0.2 - 1 UCERF-3
Lions Head fault Lions Head fault zone USGS IV fault database 0 - 0.2 UCERF-3
Little Lake Fault Little Lake fault zone USGS IV fault database 0.2 - 1
https://scedc.caltech.edu/
Live Oak Canyon fault Crafton Hills fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/

264
Lockhart fault Lenwood-Lockhart fault zone USGS IV fault database 0.2 - 1 UCERF-3
Lockwood Valley fault Big Pine fault zone USGS IV fault database 0.2 - 1 UCERF-3
Loma Linda fault San Jacinto fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Long Canyon fault Long Canyon fault USGS IV fault database 0 - 0.2 UCERF-3
Los Alamitos fault Los Alamitos fault USGS IV fault database 0.2 - 1 UCERF-3
Los Alamos fault Los Alamos fault USGS IV fault database 0.2 - 1 UCERF-3
Los Osos Fault Los Osos fault zone USGS IV fault database 0.2 - 1 UCERF-3
Los Osos Fault Zone Los Osos fault zone USGS IV fault database 0 - 0.2 UCERF-3
Lower Elysian Park fault USGS IV fault database 1 - 3 UCERF-3
Ludlow fault Ludlow fault USGS IV fault database 0.2 - 1 UCERF-3
Ludlow fault (east branch) Ludlow fault USGS IV fault database 0.2 - 1 UCERF-3
Ludlow fault (west branch) Ludlow fault USGS IV fault database 0 - 0.2 UCERF-3
Lytle Creek fault San Jacinto fault zone USGS IV fault database 1 - 3 UCERF-3
Malibu Coast fault Malibu Coast fault zone USGS IV fault database 0.2 - 1 UCERF-3
Manix fault Manix fault USGS IV fault database 0 - 0.2 UCERF-3
Mesa fault Mesa-Rincon Creek fault zone USGS IV fault database 0.2 - 1 UCERF-3
Mesa-Rincon Creek fault Mesa-Rincon Creek fault zone USGS IV fault database 0.2 - 1 UCERF-3
Mesquite Lake fault Mesquite Lake fault USGS IV fault database 0.2 - 1 UCERF-3
Middle Fork Lytle Creek fault San Jacinto fault zone USGS IV fault database 1 - 3 UCERF-3
Mining Ridge (Sur fault zone) San Gregorio fault zone USGS IV fault database 1 - 3 UCERF-3
Mirage Valley fault Mirage Valley fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Mission Creek fault San Andreas fault zone USGS IV fault database 0 - 0.2 UCERF-3
Mission Hills fault Mission Hills fault zone USGS IV fault database 1 - 3 UCERF-3
Mission Ridge fault Mission Ridge fault system USGS IV fault database 0.2 - 1 UCERF-3
More Ranch fault Mission Ridge fault system USGS IV fault database 0.2 - 1 UCERF-3
Morongo Valley fault Pinto Mountain fault zone USGS IV fault database 0 - 0.2 UCERF-3
Mount Lukens fault Sierra Madre fault zone USGS IV fault database 1 - 3 UCERF-3
Mule Spring fault Garlock fault zone USGS IV fault database 3 - 5 UCERF-3
Murrietta Hot Springs fault Murrietta Hot Springs fault USGS IV fault database 0 - 0.2 UCERF-3
Newport-Inglewood-Rose Canyon fault zone Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0.2 - 1 UCERF-3

265
North Branch Big Pine fault Big Pine fault zone USGS IV fault database 0.2 - 1 UCERF-3
North Branch fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0.2 - 1 UCERF-3
North Branch Helendale fault Helendale-South Lockhart fault zone USGS IV fault database 0.2 - 1 UCERF-3
North Branch Santa Ynez fault Santa Ynez fault zone USGS IV fault database 0.2 - 1 UCERF-3
North Fork Lytle Creek fault San Jacinto fault zone USGS IV fault database 1 - 3 UCERF-3
North Lockhart fault Lenwood-Lockhart fault zone USGS IV fault database 0.2 - 1 UCERF-3
Northeast Flank fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0.2 - 1 UCERF-3
Northridge Hills fault Northridge Hills fault USGS IV fault database 1 - 3 UCERF-3
Oak Ridge fault Oak Ridge fault USGS IV fault database 1 - 3 UCERF-3
Oak View fault Faults near Oakview and Meiners Oaks USGS IV fault database 0.2 - 1 UCERF-3
Oceanic fault Oceanic fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Oceano fault San Luis Range fault system (South Margin) USGS IV fault database 0 - 0.2 UCERF-3
Ocotillo Ridge fold North Frontal thrust system USGS IV fault database 0.2 - 1 UCERF-3
Oeanic fault Oceanic fault zone USGS IV fault database 0 - 0.2 UCERF-3
Olive Avenue fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0.2 - 1 UCERF-3
Ord Mountains fault zone North Frontal thrust system USGS IV fault database 0 - 0.2 UCERF-3
Ortigalita fault Ortigalita fault zone USGS IV fault database 0.2 - 1
https://scedc.caltech.edu/
Owens Valley fault Owens Valley fault zone USGS IV fault database 0.2 - 1 UCERF-3
Owl Canyon fault Blackwater fault zone USGS IV fault database 0.2 - 1 UCERF-3
Owl Lake fault Owl Lake fault USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Pacifico fault Santa Ynez fault zone USGS IV fault database 0 - 0.2 UCERF-3
Palos Verdes fault Palos Verdes fault zone USGS IV fault database 3 - 5 UCERF-3
Palos Verdes Hills fault Palos Verdes fault zone USGS IV fault database 0 - 0.2 UCERF-3
Panamint Valley fault Panamint Valley fault zone USGS IV fault database 1 - 3 UCERF-3
Peralta Hills fault Peralta Hills fault USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Pezzoni-Casmalia fault Casmalia fault zone USGS IV fault database 0.2 - 1 UCERF-3
Pinto Mountain fault Pinto Mountain fault zone USGS IV fault database 1 - 3 UCERF-3
Pisgah fault Pisgah-Bullion fault zone USGS IV fault database 1 - 3 UCERF-3
Pitas Point fault Pitas Point fault USGS IV fault database 5 - 7 UCERF-3

266
Pitas Point-North Channel structure (blind
thrust)
Pitas Point-North Channel structure (blind
thrust)
USGS IV fault database
5 - 7
UCERF-3
Pleito fault Pleito fault zone USGS IV fault database 0.2 - 1 UCERF-3
Potrero fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0.2 - 1 UCERF-3
Puente Hills Thrust USGS IV fault database 1 - 3 UCERF-3
Rainbow Canyon fault Blackwater fault zone USGS IV fault database 0.2 - 1 UCERF-3
Raymond fault Raymond fault USGS IV fault database 1 - 3 UCERF-3
Red Hill-Etiwanda Avenue fault Red Hill-Etiwanda Avenue fault USGS IV fault database 0 - 0.2 UCERF-3
Red Hills fault San Juan fault zone USGS IV fault database 0 - 0.2 UCERF-3
Red Mountain Red Mountain fault zone USGS IV fault database 0.2 - 1 UCERF-3
Red Mountain fault Red Mountain fault zone USGS IV fault database 0.2 - 1 UCERF-3
Red Mountain fault, north branch Red Mountain fault zone USGS IV fault database 0.2 - 1 UCERF-3
Red Mountain fault, north strand Red Mountain fault zone USGS IV fault database 0.2 - 1 UCERF-3
Red Mountain fault, south branch Red Mountain fault zone USGS IV fault database 0.2 - 1 UCERF-3
Red Mountain fault, south strand Red Mountain fault zone USGS IV fault database 0.2 - 1 UCERF-3
Red Pass fault Red Pass fault USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Redlands fault Crafton Hills fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Redondo Canyon fault Redondo Canyon fault USGS IV fault database 0 - 0.2 UCERF-3
Reliz Fault Zone Reliz fault zone USGS IV fault database 0.2 - 1 UCERF-3
Reservoir Canyon fault Crafton Hills fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Reservoir Hill fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0.2 - 1 UCERF-3
Rialto-Colton fault San Jacinto fault zone USGS IV fault database 1 - 3 UCERF-3
Rincon Creek fault Mesa-Rincon Creek fault zone USGS IV fault database 0.2 - 1 UCERF-3
Rinconada fault Rinconada fault zone USGS IV fault database 0.2 - 1 UCERF-3
Rodman fault Rodman fault USGS IV fault database 3 - 5 UCERF-3
Rose Canyon fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 1 - 3 UCERF-3
San Cayetano fault San Cayetano fault USGS IV fault database 1 - 3 UCERF-3
San Cayetano fault East San Cayetano fault USGS IV fault database 1 - 3 UCERF-3
San Clemente fault San Clemente fault zone USGS IV fault database 5 - 7 UCERF-3
San Diego Trough Fault Zone San Diego Trough fault zone USGS IV fault database 1 - 3 UCERF-3

267
San Dimas Canyon fault Sierra Madre fault zone USGS IV fault database 0.2 - 1 UCERF-3
San Felipe fault San Felipe fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
San Fernando fault Sierra Madre fault zone USGS IV fault database 0.2 - 1 UCERF-3
San Gabriel fault San Gabriel fault zone USGS IV fault database 0.2 - 1 UCERF-3
San Gorgonio Pass fault San Gorgonio Pass fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
San Guillermo fault Big Pine fault zone USGS IV fault database 0 - 0.2 UCERF-3
San Jacinto fault San Jacinto fault zone USGS IV fault database 1 - 3 UCERF-3
San Joaquin Hills Thrust USGS IV fault database 0.2 - 1 UCERF-3
San Jose fault San Jose fault USGS IV fault database 0.2 - 1 UCERF-3
San Luis Bay fault San Luis Range fault system (South Margin) USGS IV fault database 0 - 0.2 UCERF-3
San Marcos fault Rinconada fault zone USGS IV fault database 0.2 - 1 UCERF-3
San Rafael fault Eagle Rock fault USGS IV fault database 0 - 0.2 UCERF-3
San Simeon fault San Simeon fault zone USGS IV fault database 1 - 3 UCERF-3
Santa Ana fault Mission Ridge fault system USGS IV fault database 0.2 - 1 UCERF-3
Santa Cruz Catalina Ridge Fault Zone Santa Cruz_Santa Catalina Ridge fault zone USGS IV fault database 0.2 - 1 UCERF-3
Santa Cruz Island fault Santa Cruz Island fault USGS IV fault database 0.2 - 1 UCERF-3
Santa Monica fault Santa Monica fault USGS IV fault database 1 - 3 UCERF-3
Santa Monica fault (offshore) Santa Monica fault USGS IV fault database 1 - 3 UCERF-3
Santa Rosa fault Simi-Santa Rosa fault zone USGS IV fault database 0.2 - 1 UCERF-3
Santa Rosa Island fault Santa Rosa Island fault USGS IV fault database 0 - 0.2 UCERF-3
Santa Rosa Valley fault Simi-Santa Rosa fault zone USGS IV fault database 0.2 - 1 UCERF-3
Santa Susana fault Sierra Madre fault zone USGS IV fault database 5 - 7 UCERF-3
Santa Ynez fault Santa Ynez fault zone USGS IV fault database 0.2 - 1 UCERF-3
Seal Beach fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0.2 - 1 UCERF-3
Sharp (1972)San Jacinto fault San Jacinto fault zone USGS IV fault database 7 - 10 UCERF-3
Sierra Madre fault Sierra Madre fault zone USGS IV fault database 0.2 - 1 UCERF-3
Sierra Nevada fault Southern Sierra Nevada fault zone USGS IV fault database 0.2 - 1 UCERF-3
Simi fault Simi-Santa Rosa fault zone USGS IV fault database 0.2 - 1 UCERF-3
Sky High Ranch fault North Frontal thrust system USGS IV fault database 1 - 3 UCERF-3
South Branch fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0.2 - 1 UCERF-3

268
South Branch San Andreas fault (Banning
strand)
San Andreas fault zone USGS IV fault database
3 - 5
UCERF-3
South Branch San Gabriel fault (Vasquez
Creek)
San Gabriel fault zone USGS IV fault database
0.2 - 1
UCERF-3
South Branch Santa Ynez fault Santa Ynez fault zone USGS IV fault database 0.2 - 1 UCERF-3
South Bristol Mtns. fault South Bristol Mtns. fault USGS IV fault database 1 - 3 UCERF-3
South Fork Lytle Creek fault San Jacinto fault zone USGS IV fault database 1 - 3 UCERF-3
South Lockhart fault Helendale-South Lockhart fault zone USGS IV fault database 0.2 - 1 UCERF-3
South Lockwood Valley fault Big Pine fault zone USGS IV fault database 0 - 0.2 UCERF-3
Southern Sierra Nevada Fault Zone Southern Sierra Nevada fault zone USGS IV fault database 0.2 - 1 UCERF-3
Spanish Bight fault Newport-Inglewood-Rose Canyon fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Springville fault Simi-Santa Rosa fault zone USGS IV fault database 0.2 - 1 UCERF-3
Sur fault San Gregorio fault zone USGS IV fault database 1 - 3 UCERF-3
Sweetwater fault La Nacion fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Sylmar fault Sierra Madre fault zone USGS IV fault database 0.2 - 1 UCERF-3
Tank Canyon fault Tank Canyon fault USGS IV fault database 1 - 3 UCERF-3
Tin Mine fault Elsinore fault zone USGS IV fault database 1 - 3 UCERF-3
Tujunga fault Sierra Madre fault zone USGS IV fault database 0.2 - 1 UCERF-3
Upper Elysian Park Fault USGS IV fault database 1 - 3 UCERF-3
Upper Johnson Valley fault Johnson Valley fault zone USGS IV fault database 0.2 - 1 UCERF-3
Valley Mountain fault Pisgah-Bullion fault zone USGS IV fault database 0.2 - 1 UCERF-3
Ventura fault Ventura fault USGS IV fault database 5 - 7 UCERF-3
Verdugo fault Verdugo fault USGS IV fault database 0.2 - 1 UCERF-3
Villanova fault Faults near Oakview and Meiners Oaks USGS IV fault database 0.2 - 1 UCERF-3
West Bullion fault Pisgah-Bullion fault zone USGS IV fault database 0.2 - 1 UCERF-3
West Calico fault Calico-Hidalgo fault zone USGS IV fault database 0.2 - 1 UCERF-3
West Huasna fault West Huasna fault zone USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
West Johnson Valley fault Johnson Valley fault zone USGS IV fault database 0.2 - 1 UCERF-3
West Silverwood Lake fault Cleghorn fault zone USGS IV fault database 0.2 - 1 UCERF-3
West Valley Mountain fault Pisgah-Bullion fault zone USGS IV fault database 0.2 - 1 UCERF-3
Western Heights fault Crafton Hills fault zone USGS IV fault database 0 - 0.2 UCERF-3

269
Wheeler Ridge fault Wheeler Ridge fault zone USGS IV fault database 3 - 5 UCERF-3
White Mountains Thrust North Frontal thrust system USGS IV fault database 0.2 - 1 UCERF-3
White Wolf fault White Wolf fault zone USGS IV fault database 0.2 - 1 UCERF-3
Whittier fault Elsinore fault zone USGS IV fault database 1 - 3 UCERF-3
Wildomar fault Elsinore fault zone USGS IV fault database 3 - 5 UCERF-3
Willard fault Elsinore fault zone USGS IV fault database 3 - 5 UCERF-3
Wilmar Avenue fault San Luis Range fault system (South Margin) USGS IV fault database 0 - 0.2
https://scedc.caltech.edu/
Wolf Valley fault Elsinore fault zone USGS IV fault database 3 - 5 UCERF-3
Yucaipa Graben Complex Crafton Hills fault zone USGS IV fault database 0 - 0.2 UCERF-3

270
Table A.2: Active fault database of Turkey - references for each fault trace and its related slip rate. EMME refers to the Earthquake Model of the
Middle East.
Fault trace Slip rate
Name Compilation/Reference
Slip rate range
(mm/yr)
Reference
Acigol Graben System Emre et al. (2018) 0 - 0.2 inferred minimal
Acigol Graben System Emre et al. (2018) 0 inferred null
Acigol Graben System Emre et al. (2018) 3 - 5 EMME Project
Acigol Graben System Emre et al. (2018) 3 - 5 EMME Project
Acigol Graben System Emre et al. (2018) 0.2 - 1 EMME Project
Acipayam Fault Emre et al. (2018) 3 - 5 EMME Project
Afyon Aksehir Graben System Emre et al. (2018) 0 - 0.2 inferred minimal
Afyon Aksehir Graben System Emre et al. (2018) 0 inferred null
Afyon Aksehir Graben System Emre et al. (2018) 0.2 - 1 Topal et al. (2016)
Afyon Aksehir Graben System Emre et al. (2018) 0.2 - 1 Topal et al. (2016)
Afyon Aksehir Graben System Emre et al. (2018) 0 - 0.2 Topal et al. (2016)
Afyon Aksehir Graben System Emre et al. (2018) 0 - 0.2 Topal et al. (2016)
Afyon Aksehir Graben System Emre et al. (2018) 1 - 3 EMME Project
Afyon Aksehir Graben System Emre et al. (2018) 0.2 - 1 EMME Project
Afyon Aksehir Graben System Emre et al. (2018) 0.2 - 1 EMME Project
Akcapinar Fault Emre et al. (2018) 0 inferred null
Akdag Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Akdagmadeni Fault Emre et al. (2018) 1 - 3 EMME Project
Akdogan Lake Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Akharim Fault Emre et al. (2018) 0 inferred null
Akhoyuk Acilma Catlagi Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Akpinar Fault Emre et al. (2018) 1 - 3 EMME Project

271
Alacadag Fault Zone Emre et al. (2018)
0 inferred null
Aladag Fault Emre et al. (2018) 0
- 0.2 inferred minimal from Kaymakci et al. (2010)
Almus Fault Emre et al. (2018) 0.2
-
1 Bozkurt and Kocyigit (1996)
Altinekin Fault Emre et al. (2018)
0 inferred null
Altinova Fault Emre et al. (2018) 7
- 10 EMME Project
Altinova Fault Emre et al. (2018) 7
- 10 EMME Project
Amik Lake Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Antakya Fault Zone Emre et al. (2018) 3
-
5 EMME Project
Arizli Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Armutlu Fault Kuscu et al. (2009) 1
-
3 inferred from onshore fault slip rate (EMME)
Armutlu Fault Emre et al. (2018) 1
-
3 EMME Project
Asenovgrad Fault Glavcheva & Matova (2004) 0.2
-
1 EMME Project
Aslihanlar Fault Emre et al. (2018) 0.2
-
1 EMME Project
Aytos Fault Glavcheva & Matova (2004) 0.2
-
1 EMME Project
Ayvali Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Ayvali Fault Emre et al. (2018)
0 inferred null
Bahcekoy Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Bahcekoy Fault Zone Emre et al. (2018)
0 inferred null
Bahcekoy Fault Zone Emre et al. (2018) 1
-
3 EMME Project
Baklan Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Baklan Fault Emre et al. (2018)
0 inferred null
Baklan Fault Emre et al. (2018) 1
-
3 EMME Project
Bala Fault Emre et al. (2018) 1
-
3 EMME Project
Balik Lake Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Balik Lake Fault Zone Emre et al. (2018) 5
-
7 EMME Project
Balikesir Fault Emre et al. (2018) 3
-
5 Sozbilir et al. (2016)
Balikesir Fault Emre et al. (2018) 1
-
3 Sozbilir et al. (2016)
Bandirma Fault Emre et al. (2018) 3
-
5 Gasperini et al. (2011)
Barakfaki Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Barla Fault Emre et al. (2018)
0 inferred null

272
Barla Fault Emre et al. (2018) 0.2 - 1 EMME Project
Bekten Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Bekten Fault Emre et al. (2018) 3 - 5 EMME Project
Berendi Fault Emre et al. (2018) 0 inferred null
Bergama Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Besni Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Beyagac Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Beysehir Lake Fault Emre et al. (2018) 0.2 - 1 EMME Project
Beyyurdu Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Big Menderes Graben System Emre et al. (2018) 0.2 - 1 Mozafari et al. (2019)
Big Menderes Graben System Emre et al. (2018) 0.2 - 1 Mozafari et al. (2019)
Big Menderes Graben System Emre et al. (2018) 0 - 0.2 inferred minimal
Big Menderes Graben System Emre et al. (2018) 0 inferred null
Big Menderes Graben System Emre et al. (2018) 3 - 5 EMME Project
Big Menderes Graben System Emre et al. (2018) 0.2 - 1 EMME Project
Biga-Can Fault Emre et al. (2018) 3 - 5 EMME Project
Black Sea Margin Emre et al. (2018), EMME Project, Finetti et al. 1998 1 - 3 Finetti et al. (1988)
Bogazliyan Fault Emre et al. (2018) 1 - 3 EMME Project
Bolvadin Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Bozborun Fault Emre et al. (2018) 0 inferred null
Bozova Fault Emre et al. (2018) 1 - 3 EMME Project
Brezovo Fault Glavcheva & Matova (2004) 0.2 - 1 EMME Project
Bugdayli Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Bulamac Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Bulanik Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Burdur Grabeni Emre et al. (2018) 0 - 0.2 inferred minimal
Burdur Grabeni Emre et al. (2018) 0 - 0.2 inferred minimal
Burfa Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Burfa Fault Emre et al. (2018) 3 - 5 EMME Project
Caldiran Fault Emre et al. (2018) 5 - 7 EMME Project

273
Cameli Fault Emre et al. (2018) 3
-
5 EMME Project
Camlidere Fault Emre et al. (2018) 0.2
-
1 EMME Project
Camliyayla Fault Emre et al. (2018)
0 inferred null
Cankiri Fault Emre et al. (2018) 0.2
-
1 EMME Project
Cankurtaran Fault Emre et al. (2018)
0 inferred null
Cardak Fault Emre et al. (2018) 1
-
3 Duman and Emre (2013)
Cardak Fault Emre et al. (2018)
0 inferred null
Cat Fault Zone Emre et al. (2018) 0
- 0.2 EMME Project
Catalcam Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Cavdarhisar Fault Emre et al. (2018)
0 inferred null
Cekerek Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Celtikci Fault Zone Ozmen et al. (2014) + EMME 0
- 0.2 Karaca (2004)
Chirpan Glavcheva & Matova (2004) 0.2
-
1 Vanneste et al. (2006)
Cihanbeyli Fault Emre et al. (2018) 0.2
-
1 EMME Project
Cilimli Fault Emre et al. (2018) 1
-
3 inferred from Duman et al. (2005)
Cilimli Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Cine Fault Emre et al. (2018)
0 inferred null
Civril Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Civril Fault Emre et al. (2018) 0.2
-
1 EMME Project
Cokak Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Cukuroren Fault Emre et al. (2018) 0.2
-
1 EMME Project
Dagkizilca Fault Emre et al. (2018) 0
- 0.2 EMME Project
Datca Fault Emre et al. (2018)
0 inferred null
Davras Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Davras Fault Zone Emre et al. (2018)
0 inferred null
Davultar Fault Emre et al. (2018) 0.2
-
1 Mozafari et al. (2019)
Deliler Fault Emre et al. (2018) 0.2
-
1 Akyuz et al. (2012)
Deliler Fault Emre et al. (2018) 1
-
3 EMME Project
Demiroluk Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Denicli Graben System Emre et al. (2018) 0
- 0.2 inferred minimal

274
Denicli Graben System Emre et al. (2018) 3 - 5 EMME Project
Denicli Graben System Emre et al. (2018) 1 - 3 EMME Project
Denicli Graben System Emre et al. (2018) 0.2 - 1 EMME Project
Denizli Graben System Emre et al. (2018) 0 inferred null
Denizli Graben System Emre et al. (2018) 3 - 5 EMME Project
Denizli Graben System Emre et al. (2018) 1 - 3 EMME Project
Derinkuyu Fault Emre et al. (2018) 0 inferred null
Devrek Emre et al. (2018) 1 - 3 inferred from Duman et al. (2005)
Dinar Fault Emre et al. (2018) 1 - 3 Altunel et al. (1999)
Dinar Fault Emre et al. (2018) 1 - 3 Altunel et al. (1999)
Dinar Fault Emre et al. (2018) 1 - 3 inferred from Altunel et al. (1999)
Dinar Fault Emre et al. (2018) 1 - 3 inferred from Altunel et al. (1999)
Dinar Fault Emre et al. (2018) 1 - 3 EMME Project
Dinar Fault Emre et al. (2018) 1 - 3 EMME Project
Divrigi Fault Emre et al. (2018) 0.2 - 1 Akyuz et al. (2012)
Divrigi Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Divrigi Fault Emre et al. (2018) 0.2 - 1 EMME Project
Dodurga Fault Emre et al. (2018) + Ozmen et al. (2014) 1 - 3 Kocyigit et al. (2001)
Dodurga Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Dodurga Fault Emre et al. (2018) 0.2 - 1 EMME Project
Dogansehir Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Doxipara EMME + Pavlides et al. (2008) 0.2 - 1 EMME Project
Duvertepe Fault Zone Emre et al. (2018) 0 inferred null
Duvertepe Fault Zone Emre et al. (2018) 0.2 - 1 EMME Project
Duzbel Fault Emre et al. (2018) 0 inferred null
Duzici-iskenderun Fault Zone Emre et al. (2018) 0 - 0.2 inferred minimal
Duzici-Iskenderun Fault Zone Emre et al. (2018) 1 - 3 EMME Project
EAF Emre et al. (2018) 10 - 15 Cetin et al. (2003)
EAF - Amanos Emre et al. (2018) 1 - 3 Seyrek et al. (2007)
EAF - Erkenek Emre et al. (2018) 7 - 10 EMME Project

275
EAF
- Erkenek Emre et al. (2018) 10
- 15 EMME Project
EAF
- Ilica Emre et al. (2018) 0
- 0.2 inferred minimal
EAF
- Ilica Emre et al. (2018) 7
- 10 EMME Project
EAF
- Karliova Emre et al. (2018) 7
- 10 Emre et al. (2018)
EAF
- Palu Emre et al. (2018) 10
- 15 Cetin et al. (2003)
EAF
- Palu Emre et al. (2018) 7
- 10 EMME Project
EAF
- Palu Emre et al. (2018) 1
-
3 EMME Project
EAF
- Pazarcik Emre et al. (2018) 7
- 10 EMME Project
EAF
- Puturge Emre et al. (2018) 10
- 15 EMME Project
Ecemis Fault Emre et al. (2018) 0
- 0.2 inferred from Sarikaya et al. (2015)
Ecemis Fault Emre et al. (2018)
0 inferred null
Edincik Fault Emre et al. (2018) 3
-
5 EMME Project
Edremit Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Edremit Fault Zone Emre et al. (2018) 3
-
5 EMME Project
Efes Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Emet
-Gediz Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Emet
-Gediz Fault Zone Emre et al. (2018)
0 inferred null
Engizek Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Engizek Fault Zone Emre et al. (2018)
0 inferred null
Ercis Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Ercis Fault Emre et al. (2018) 5
-
7 EMME Project
Erciyes Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Erciyes Fault Emre et al. (2018) 1
-
3 EMME Project
Erkilet Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Erkilet Fault Zone Emre et al. (2018) 1
-
3 EMME Project
Erzurum Fault Zone Emre et al. (2018) 1
-
3 inferred from Omer et al. (2004)
Erzurum Fault Zone Emre et al. (2018) 1
-
3 inferred from Omer et al. (2004)
Esen Fault Emre et al. (2018)
0 inferred null
Esen Fault Emre et al. (2018) 1
-
3 EMME Project
Esenkoy Fault Emre et al. (2018) 0
- 0.2 supposed

276
Eskisehir Fault Emre et al. (2018) 0 inferred null
Eskisehir Fault Emre et al. (2018) 0.2 - 1 EMME Project
Evciler Fault Emre et al. (2018) 3 - 5 Kurçer et al. (2008); Pondard et al. (2007)
Gediz Graben System Emre et al. (2018) 1 - 3 EMME Project
Gediz Graben System Emre et al. (2018) 0 - 0.2 inferred minimal
Gediz Graben System Emre et al. (2018) 0 inferred null
Gediz Graben System Emre et al. (2018) 1 - 3 EMME Project
Gediz Graben System Emre et al. (2018) 1 - 3 EMME Project
Gediz Graben System Emre et al. (2018) 1 - 3 EMME Project
Gediz Graben System Emre et al. (2018) 0 - 0.2 EMME Project
Gediz Graben System - Manisa Fault Emre et al. (2018) 0.2 - 1 Ozkaymak et al. (2011)
Gelenbe Fault Zone Emre et al. (2018) 0 - 0.2 inferred minimal
Gelenbe Fault Zone Emre et al. (2018) 0.2 - 1 EMME Project
Gelendost Fault Emre et al. (2018) 0.2 - 1 EMME Project
Gemlik Fault Emre et al. (2018) 3 - 5 Gasperini et al. (2011)
Gemlik Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Gemlik Fault Emre et al. (2018) 0 inferred null
Gencali Fault Emre et al. (2018) 3 - 5 inferred from Gasperini et al. (2011)
Gencali Fault Emre et al. (2018) 3 - 5 inferred from Gasperini et al. (2011)
Geras Gulf Fault EMME + Pavlides et al. (2008) 0.2 - 1 EMME Project
Geyve Fault Emre et al. (2018) 3 - 5 Gasperini et al. (2011)
Geyve Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Gokce Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Gokova Fault Zone Emre et al. (2018) 0 - 0.2 inferred minimal
Gokova Fault Zone Emre et al. (2018) 0 inferred null
Gokova Fault Zone Emre et al. (2018) 3 - 5 EMME Project
Gole Fault Emre et al. (2018) 0 inferred null
Gorumlu Fault Emre et al. (2018) 0 - 0.2 inferred minimal
Gulbahce Fault Zone Emre et al. (2018) 0 - 0.2 inferred minimal
Gumuldur Fault Emre et al. (2018) 0 - 0.2 inferred minimal

277
Gumuldur Fault Emre et al. (2018) 0.2
-
1 EMME Project
Gumuskent Fault Emre et al. (2018) 0
- 0.2 Ozsayin et al. (2013)
Gunasan Fault Emre et al. (2018)
0 inferred null
Gundogan Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Gure Fault Zone Emre et al. (2018)
0 inferred null
Gurun Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Gurun Fault Emre et al. (2018) 0
- 0.2 inferred from Kaymakci et al. (2010)
Guzelhisar Fault Emre et al. (2018)
0 inferred null
Guzelhisar Fault Emre et al. (2018) 0.2
-
1 EMME Project
Hacli Lake Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Hacli Lake Fault Emre et al. (2018) 1
-
3 EMME Project
Hamur Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Harran Fault Zone Emre et al. (2018)
0 inferred null
Havran
-Bayla Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Havran
-Bayla Fault Zone Emre et al. (2018) 3
-
5 Sozbilir et al. (2016)
Havran
-Bayla Fault Zone Emre et al. (2018) 1
-
3 Sozbilir et al. (2016)
Heltepe Fault Emre et al. (2018) 1
-
3 EMME Project
Heltepe Fault Emre et al. (2018) 0.2
-
1 EMME Project
Horasan
-Senkaya Fault Emre et al. (2018) 1
-
3 EMME Project
Hotamis Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Igdir Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Ilica Fault Zone EMME + Ozmen et al. (2014) 0
- 0.2 Karaca (2004)
Incesu Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Incesu Fault Zone Emre et al. (2018)
0 inferred null
Incesu Fault Zone Emre et al. (2018) 1
-
3 EMME Project
Inegol Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Inegol Fault Emre et al. (2018) 0.2
-
1 EMME Project
Istiranca EMME + Glavcheva & Matova (2004) 0
- 0.2 inferred from Pondard et al. (2007)
Izmir Fault Emre et al. (2018)
0 inferred null
Izmir Fault Emre et al. (2018) 0.2
-
1 Uzel et al. (2013); Zhu et al. (2006)

278
Izmir Fault Emre et al. (2018) 0.2
-
1 Uzel et al. (2013); Zhu et al. (2006)
Izmir Fault Emre et al. (2018) 0.2
-
1 EMME Project
Iznik
-Mekece Fault Emre et al. (2018) 3
-
5 Gasperini et al. (2011)
Kahramanmaras Fault Emre et al. (2018)
0 inferred null
Kahramanmaras Fault Emre et al. (2018) 1
-
3 EMME Project
Kahramanmaras Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Kahramanmaras Fault Zone Emre et al. (2018)
0 inferred null
Kale Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Kale Fault Emre et al. (2018) 0.2
-
1 EMME Project
Kalloni Fault EMME + Pavlides et al. (2008) 0.2
-
1 EMME Project
Kamchia Fault Glavcheva & Matova (2004) 0
- 0.2 EMME Project
Kandilli Fault Emre et al. (2018) 1
-
3 EMME Project
Kapidag Peninsula Fault Kuscu et al. (2009) 0
- 0.2 inferred minimal
Karaadilli Fault Emre et al. (2018)
0 inferred null
Karabuk Fault Emre et al. (2018) 1
-
3 inferred from Kahraman et al. (2015)
Karacadag Fault Emre et al. (2018)
0 inferred null
Karacasu Fault Emre et al. (2018)
0 inferred null
Karakecili Fault Zone Emre et al. (2018) 0.2
-
1 EMME Project
Karatas Fault Emre et al. (2018) 1
-
3 EMME Project
Karayazi Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Kavakbasi Fault Emre et al. (2018) 1
-
3 EMME Project
Kaymaz Fault Emre et al. (2018) 1
-
3 EMME Project
Kazan Fault Zone EMME + Ozmen et al. (2014) 0
- 0.2 Karaca 2004
Kazankaya Fault Emre et al. (2018)
0 inferred null
Kazbel Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Kelekci Fault Emre et al. (2018)
0 inferred null
Keskin Fault Zone Emre et al. (2018) 0.2
-
1 EMME Project
Kestanbol Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Kiraz Fault Emre et al. (2018)
0 inferred null
Kiziloren Fault Emre et al. (2018)
0 inferred null

279
Kiziluzum Fault Emre et al. (2018)
0 inferred null
Kocbeyli Fault Emre et al. (2018) 0.2
-
1 EMME Project
Konya Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Koprubasi Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Koprubasi Fault Zone Emre et al. (2018)
0 inferred null
Kovada Fault Emre et al. (2018) 0.2
-
1 EMME Project
Kumdanli Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Kumdanli Fault Emre et al. (2018) 0.2
-
1 EMME Project
Kumdanli Fault Emre et al. (2018) 0.2
-
1 EMME Project
Kusadasi Fault Zone Emre et al. (2018) 1
-
3 Mozafari et al. (2019)
Kutahya Fault Emre et al. (2018)
0 inferred null
Kutahya Fault Emre et al. (2018) 0.2
-
1 EMME Project
Kuzburnu Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Leskeri Fault Zone Emre et al. (2018)
0 inferred null
Magiras Fault EMME + Pavlides et al. (2008) 0.2
-
1 EMME Project
Malatya Fault Emre et al. (2018) 1
-
3 Sancar et al. (2019)
Malatya Fault Emre et al. (2018) 0.2
-
1 EMME Project
Manyas Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Manyas Fault Zone Emre et al. (2018) 3
-
5 EMME Project
Manyas Fault Zone Emre et al. (2018) 3
-
5 EMME Project
Maronia EMME 0.2
-
1 EMME Project
Menemen Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Menemen Fault Zone Emre et al. (2018) 0.2
-
1 EMME Project
Merzifon
- Esencay Emre et al. (2018) 1
-
3 Emre et al. (2020), Peyret et al. (2013)
Milas Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Milas Fault Emre et al. (2018) 0.2
-
1 EMME Project
Mordogan Fault Emre et al. (2018)
0 inferred null
Mugla Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Mugla Fault Emre et al. (2018)
0 inferred null
Mugla Fault Emre et al. (2018) 0.2
-
1 EMME Project

280
Mus Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Mus Fault Zone Emre et al. (2018) 1
-
3 EMME Project
Mustafa Kemal Pasa Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Mustafa Kemal Pasa Fault Emre et al. (2018) 3
-
5 EMME Project
NAF Emre et al. (2018) 1
-
3 inferred from Duman et al. (2005)
NAF Emre et al. (2018) > 20 Kozaci et al. (2007)
NAF Emre et al. (2018)
0 inferred null
NAF Emre et al. (2018) 15
- 20 Pucci et al. (2008)
NAF Emre et al. (2018) 10
- 15 inferred minimal
NAF Emre et al. (2018) 7
- 10 Yilar (2014)
NAF
- Adalar Emre et al. (2018) 15
- 20 Rockwell et al. (2009); Armijo et al. (1999)
NAF
- Arifiye Emre et al. (2018) 10
- 15 EMME Project
NAF
- Arifiye Emre et al. (2018) 10
- 15 inferred minimal
NAF
- Avcilar Emre et al. (2018) 15
- 20 Rockwell et al. (2009); Armijo et al. (1999)
NAF
- Bolu Emre et al. (2018) > 20 Okomura et al. (1993); Kondo et al. (2010)
NAF
- Cinarcik Emre et al. (2018) 7
- 10 EMME Project
NAF
- Darica Emre et al. (2018) 10
- 15 inferred minimal
NAF
- Dokurcun Emre et al. (2018) 10
- 15 EMME Project
NAF
- Dokurcun Emre et al. (2018) 10
- 15 inferred minimal
NAF
- Duzce Emre et al. (2018) 15
- 20 Pucci et al. (2008)
NAF
- Erzincan Emre et al. (2018) > 20 Hubert
-Ferrari et al. (2002)
NAF
- Erzincan Emre et al. (2018) > 20 EMME Project
NAF
- Ezinepazar Emre et al. (2018) 1
-
3 Emre et al. (2020b)
NAF
- Ganos Emre et al. (2018) 15
- 20
inferred from Rockwell et al. (2009); Armijo et al.
(1999)
NAF
- Ganos Emre et al. (2018) 15
- 20 Rockwell et al. (2009); Armijo et al. (1999)
NAF
- Golcuk Emre et al. (2018) 10
- 15 inferred minimal
NAF
- Ismetpasa Emre et al. (2018) > 20 Okomura et al. (1993); Kondo et al. (2010)
NAF
- Karadere Emre et al. (2018) 10
- 15 EMME Project
NAF
- Karadere Emre et al. (2018) 10
- 15 inferred minimal

281
NAF
- Karadere Emre et al. (2018) 0
- 0.2 inferred minimal
NAF
- Karamursel Emre et al. (2018) 10
- 15 inferred minimal
NAF
- Kargapazari Emre et al. (2018) > 20 Hubert
-Ferrari et al. (2002)
NAF
- Kargi Emre et al. (2018) > 20 Kozaci et al. (2007)
NAF
- Karliova Emre et al. (2018) 0
- 0.2 inferred minimal
NAF
- Kumburgaz Emre et al. (2018) 15
- 20 inferred minimal
NAF
- Kumburgaz Emre et al. (2018) 15
- 20 Rockwell et al. (2009); Armijo et al. (1999)
NAF
- Lemnos Fault EMME 5
-
7 supposed
- minimal
NAF
- Marmara Saros EMME 10
- 15 EMME Project
NAF
- Niksar Emre et al. (2018) 7
- 10 Yilar (2014)
NAF
- Refahiye Emre et al. (2018) > 20 EMME Project
NAF
- Saros Emre et al. (2018) 10
- 15 Armijo et al. (1999)
NAF
- Saros Emre et al. (2018) 15
- 20 inferred minimal
NAF
- Saros Emre et al. (2018) 15
- 20 Rockwell et al. (2009); Armijo et al. (1999)
NAF
- Susehri Emre et al. (2018) > 20 EMME Project
NAF
- Tekirdag Emre et al. (2018) 15
- 20 Rockwell et al. (2009); Armijo et al. (1999)
NAF
- Tepetarla Emre et al. (2018) 10
- 15 EMME Project
NAF
- Tepetarla Emre et al. (2018) 10
- 15 supposed from EMME Project
NAF
- Yedisu Emre et al. (2018) > 20 Hubert
-Ferrari et al. (2002)
NAF
- Yenicaga Emre et al. (2018) > 20 Okomura et al. (1993); Kondo et al. (2010)
Nasa Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Nasa Fault Zone Emre et al. (2018)
0 inferred null
Nasuhpinar Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Nazik Lake Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Nazik Lake Fault Emre et al. (2018)
0 inferred null
Nazimiye Fault Emre et al. (2018) 3
-
5 EMME Project
Nazimiye Fault Emre et al. (2018) 1
-
3 EMME Project
Nemrut Acilma Catlagi Emre et al. (2018) 0
- 0.2 inferred minimal
Olu Deniz Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Olu Deniz Fault Zone Emre et al. (2018) 3
-
5 EMME Project

282
Orenkaya Fault Emre et al. (2018)
0 inferred null
Orhaneli Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Orhaneli Fault Emre et al. (2018)
0 inferred null
Orhangazi Fault Emre et al. (2018)
0 inferred null
Orhangazi Fault Emre et al. (2018) 0.2
-
1 supposed from EMME Project
Ovacik Fault Emre et al. (2018) 1
-
3 inferred from Zabci et al. (2017)
Ovacik Fault Emre et al. (2018) 1
-
3 Zabci et al. (2017)
Ovacik Fault Emre et al. (2018) 1
-
3 EMME Project
Ovalibag Fault Zone Emre et al. (2018)
0 inferred null
Oylat Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Oylat Fault Emre et al. (2018)
0 inferred null
Oylat Fault Emre et al. (2018) 0.2
-
1 EMME Project
Oylat Fault Emre et al. (2018) 0.2
-
1 EMME Project
Palandoken Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Palandoken Fault Emre et al. (2018) 1
-
3 EMME Project
Parmakoren Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Pazarkoy Fault Emre et al. (2018) 1
-
3 EMME Project
Plovdiv Fault Glavcheva & Matova (2004) 0.2
-
1 EMME Project
Pomorie Fault Glavcheva & Matova (2004) 0
- 0.2 EMME Project
Pulumur Fault Emre et al. (2018) 1
-
3 EMME Project
Pulumur Fault Emre et al. (2018) 0.2
-
1 EMME Project
Rahmanlar Fault Emre et al. (2018)
0 inferred null
Reyhanli Fault Emre et al. (2018) 1
-
3 EMME Project
Ruen Fault Glavcheva & Matova (2004) 0
- 0.2 EMME Project
Sahmelek Fault Emre et al. (2018) 0.2
-
1 EMME Project
Saimbeyli Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Samli Fault Emre et al. (2018)
0 inferred null
Samothraki Fault EMME 0.2
-
1 EMME Project
Sancak
-Uzunpinar Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Sancak
-Uzunpinar Fault Zone Emre et al. (2018) 1
-
3 EMME Project

283
Sandikli Fault Emre et al. (2018)
0 inferred null
Sarikoy Fault Emre et al. (2018) 3
-
5 EMME Project
Sariz Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Sariz Fault Emre et al. (2018) 0
- 0.2 inferred from Kaymakci et al. (2010)
Savrun Fault Emre et al. (2018) 1
-
3 EMME Project
SE Anatolian Overlap Emre et al. (2018) 0
- 0.2 inferred minimal
SE Anatolian Overlap Emre et al. (2018) 0
- 0.2 inferred minimal
SE Anatolian Overlap Emre et al. (2018)
0 inferred null
SE Anatolian Overlap Emre et al. (2018) 5
-
7 EMME Project
SE Anatolian Overlap Emre et al. (2018) 3
-
5 EMME Project
SE Anatolian Overlap Emre et al. (2018) 3
-
5 EMME Project
Seferihisar Fault Emre et al. (2018) 0.2
-
1 EMME Project
Selemiye Fault Emre et al. (2018)
0 inferred null
Selendi Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Selendi Fault Emre et al. (2018)
0 inferred null
Selimiye Fault Emre et al. (2018)
0 inferred null
Senirkent Fault Zone Emre et al. (2018)
0 inferred null
Senirkent Fault Zone Emre et al. (2018) 0.2
-
1 EMME Project
Seyithaci Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Seyitomer Fault Emre et al. (2018)
0 inferred null
Silven Fault Glavcheva & Matova (2004) 0
- 0.2 EMME Project
Simav Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Simav Fault Zone Emre et al. (2018)
0 inferred null
Simav Fault Zone Emre et al. (2018) 3
-
5 EMME Project
Sinekci Fault Emre et al. (2018) 3
-
5 EMME Project
Sivasli Fault Emre et al. (2018) 1
-
3 EMME Project
Sogukpinar Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Soma
-Kirkagac Fault Emre et al. (2018)
0 inferred null
Soma
-Kirkagac Fault Emre et al. (2018) 1
-
3 EMME Project
Soma
-Kirkagac Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal

284
Soma
-Kirkagac Fault Zone Emre et al. (2018) 0.2
-
1 EMME Project
Southern Shelf Fault Kuscu et al. (2009) 0
- 0.2 supposed minimal
Stara Gora Fault Glavcheva & Matova (2004) 0.2
-
1 EMME Project
Sudugunu Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Sudugunu Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Sudugunu Fault Emre et al. (2018) 3
-
5 EMME Project
Sudugunu Fault Emre et al. (2018) 3
-
5 EMME Project
Sudugunu Fault Emre et al. (2018) 1
-
3 EMME Project
Sungulare Fault Glavcheva & Matova (2004) 0
- 0.2 EMME Project
Sungurlu Fault Emre et al. (2018) 1
-
3 EMME Project
Suphan Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Surgu Fault Emre et al. (2018) 1
-
3 EMME Project
Susurluk Fault Emre et al. (2018)
0 inferred null
Taslica Fault Emre et al. (2018)
0 inferred null
Taslicay Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Tatarli Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Tatarli Fault Emre et al. (2018) 0.2
-
1 EMME Project
Tatarli Fault Emre et al. (2018) 0.2
-
1 EMME Project
Tavsanli Fault Emre et al. (2018)
0 inferred null
Tavsanli Fault Emre et al. (2018) 0.2
-
1 EMME Project
Taycilar Fault Emre et al. (2018) 0.2
-
1 EMME Project
Tekkekoy Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Tercan Fault Emre et al. (2018) 1
-
3 EMME Project
Thrace Pavlides et al. (2008) 0.2
-
1 EMME Project
Thrace EMME 0.2
-
1 EMME Project
Toprakkale Fault Emre et al. (2018)
0 inferred null
Toprakkale Fault Emre et al. (2018) 1
-
3 EMME Project
Tosya Fault EMME 1
-
3 Dhont et al. (2009)
Tosya Korgun Fault EMME 0.2
-
1 Dhont et al. (2009)
Troia Fault EMME 0.2
-
1 EMME Project

285
Turhal Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Tutak Fault Emre et al. (2018) 3
-
5 EMME Project
Tuz Lake Fault Emre et al. (2018) 0
- 0.2 Kurcer and Gokten (2012) and Ozsayin et al. (2013)
Tuzla Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Tuzla Fault Emre et al. (2018) 0.2
-
1 EMME Project
Ulubat Fault Emre et al. (2018) 3
-
5 EMME Project
Uluborlu Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Uluborlu Fault Emre et al. (2018) 0.2
-
1 EMME Project
unnamed Emre et al. (2018) 0
- 0.2 inferred minimal
unnamed Emre et al. (2018)
0 inferred null
unnamed Emre et al. (2018) 3
-
5 EMME Project
unnamed Emre et al. (2018) 3
-
5 EMME Project
unnamed Emre et al. (2018) 1
-
3 EMME Project
unnamed Emre et al. (2018) 1
-
3 EMME Project
unnamed Emre et al. (2018) 0.2
-
1 EMME Project
unnamed Emre et al. (2018) 0.2
-
1 EMME Project
unnamed Emre et al. (2018) 0
- 0.2 EMME Project
unnamed Emre et al. (2018) 0
- 0.2 EMME Project
Van Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Van Fault Zone Emre et al. (2018) 1
-
3 EMME Project
Van Fault Zone Emre et al. (2018) 1
-
3 EMME Project
Varto Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Varto Fault Zone Emre et al. (2018) 0
- 0.2 inferred minimal
Varto Fault Zone Emre et al. (2018) 10
- 15 EMME Project
Yagcilar Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Yalakdere Fault Emre et al. (2018) 0.2
-
1 supposed
Yalova Fault Emre et al. (2018) 0.2
-
1 supposed
Yarikkaya Fault Emre et al. (2018)
0 inferred null
Yatagan Fault Emre et al. (2018) 0.2
-
1 EMME Project
Yemliha Fault Emre et al. (2018) 1
-
3 EMME Project

286
Yenice
-Gonen Fault Emre et al. (2018) 3
-
5 EMME Project
Yenice
-Gonen Fault Emre et al. (2018) 3
-
5 EMME Project
Yenice
-Gonen Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Yenice
-Gonen Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Yeniceoba Fault Zone EMME + Ozmen et al. (2014) 0
- 0.2 Karaca (2004)
Yeniceoba Fault Zone EMME + Ozmen et al. (2014)
0 EMME Project
Yenifoca Fault Emre et al. (2018)
0 inferred null
Yenifoca Fault Emre et al. (2018) 0.2
-
1 EMME Project
Yenikosk Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Yesilhisar Fault Zone Emre et al. (2018)
0 inferred null
Yigilca Emre et al. (2018) 1
-
3 inferred from Duman et al. (2005)
Yorgancayir
-Kaynarca Fault Zone Emre et al. (2018)
0 inferred null
Yumurtalik Fault Emre et al. (2018) 0
- 0.2 inferred minimal
Yuvali Fault Emre et al. (2018) 1
-
3 EMME Project
Zeytinbagi Fault Emre et al. (2018) 3
-
5 Gasperini et al. (2011)
Zeytindag Fault Zone Emre et al. (2018) 1
-
3 EMME Project

287
Table A.3: Active fault database used for the Middle East (Dead Sea fault system). Geographic coordinates of end points of each fault segment are
given for sake of spatial understanding, as many of the faults are still unnamed. The end points of fault segments are referred as A and B, and the
coordinates are given in the WGS84/Pseudo-Mercator system. References for each fault trace and its related slip rate are given as well.
Fault trace Slip rate
Name References Latitude (A)
Longitude
(A)
Latitude (B)
Longitude
(B)
Slip rate
range
(mm/yr)
References
unnamed
AbdulWahe&Asfahani
(2018)
3995121.7 4087567.2 4059895.3 4185868.7 0 inferred inactive from Abdul-Wahe&Asfahani (2018)
unnamed
AbdulWahe&Asfahani
(2018)
3999949.0 4101873.6 3996789.3 4128730.9 0 inferred inactive from Abdul-Wahe&Asfahani (2018)
unnamed
AbdulWahe&Asfahani
(2018)
4029615.0 4127941.0 4065688.1 4153657.4 0 inferred inactive from Abdul-Wahe&Asfahani (2018)
unnamed
AbdulWahe&Asfahani
(2018)
4034047.3 4116969.9 4034968.9 4132153.9 0 inferred inactive from Abdul-Wahe&Asfahani (2018)
unnamed
AbdulWahe&Asfahani
(2018)
4036987.6 4063869.5 4030931.5 4102663.5 0 inferred inactive from Abdul-Wahe&Asfahani (2018)
unnamed
AbdulWahe&Asfahani
(2018)
4042429.3 4093096.7 4036812.0 4110123.9 0 inferred inactive from Abdul-Wahe&Asfahani (2018)
unnamed
AbdulWahe&Asfahani
(2018)
4045940.0 4126536.7 4046773.9 4140579.8 0 inferred inactive from Abdul-Wahe&Asfahani (2018)
unnamed
AbdulWahe&Asfahani
(2018)
4066127.0 4073611.9 4052610.5 4099065.0 0 inferred inactive from Abdul-Wahe&Asfahani (2018)
unnamed
AbdulWahe&Asfahani
(2018)
4069462.2 4131539.6 4071305.3 4160240.1 0 inferred inactive from Abdul-Wahe&Asfahani (2018)

288
unnamed
AbdulWahe&Asfahani
(2018)
4072797.4 4065186.1 4046993.3 4121358.3 0 inferred inactive from Abdul-Wahe&Asfahani (2018)
unnamed
AbdulWahe&Asfahani
(2018)
4076132.6 4135225.9 4078502.4 4151199.8 0 inferred inactive from Abdul-Wahe&Asfahani (2018)
unnamed Sbeinati et al. (2010) 4207802.4 4050086.7 4242564.6 4051466.2 0 inferred inactive from Sbeinati et al. (2010)
unnamed Sbeinati et al. (2010) 4221688.9 4034636.9 4353380.6 4050178.7 0 inferred inactive from Sbeinati et al. (2010)
unnamed Sbeinati et al. (2010) 4249737.7 4051098.3 4290661.5 4067192.0 0 inferred inactive from Sbeinati et al. (2010)
unnamed Sbeinati et al. (2010) 4301973.0 4069215.1 4331309.4 4096252.4 0 inferred inactive from Sbeinati et al. (2010)
unnamed Sbeinati et al. (2010) 4336735.2 4099471.1 4392097.2 4113633.5 0 inferred inactive from Sbeinati et al. (2010)
unnamed Sharon et al. (2020) 3575059.7 3912016.9 3570656.9 3910676.0 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3576244.2 3912933.2 3575417.3 3912396.9 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3576266.5 3905602.7 3572333.1 3910206.6 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3578222.1 3903803.6 3577272.2 3902105.0 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3580378.8 3906273.2 3579887.1 3905692.1 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3582636.1 3907882.3 3580736.4 3906496.7 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3575663.1 3900350.6 3573875.2 3898048.6 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3574277.4 3898026.3 3571930.8 3895634.9 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3565941.2 3887924.4 3564600.2 3885108.4 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3566276.4 3888617.2 3566097.6 3888214.9 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3566835.1 3887432.7 3565851.8 3885711.8 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3567103.3 3890069.9 3567081.0 3888594.9 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3567304.5 3890516.9 3566723.4 3889779.4 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3568310.2 3891902.6 3567483.3 3890852.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3568936.0 3892841.2 3568332.5 3892237.8 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3570612.2 3894383.3 3569494.7 3893064.7 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3554096.0 3849103.7 3554900.6 3846645.3 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3554431.3 3845550.2 3555437.0 3835403.6 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3555213.5 3859138.5 3554945.3 3860188.9 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3555593.4 3834911.9 3556263.9 3828453.0 0 inferred inactive from Sharon et al. (2020)

289
unnamed Sharon et al. (2020) 3555615.8 3859876.1 3553693.8 3850802.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3556062.8 3861306.4 3555861.6 3860703.0 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3556085.1 3862982.6 3555839.3 3862356.8 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3556174.5 3862178.0 3555638.1 3861060.6 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3557023.8 3863809.5 3556062.8 3859406.7 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3557783.7 3866692.6 3556331.0 3863161.4 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3557850.7 3836990.4 3556286.3 3816183.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3559258.7 3869799.1 3557917.8 3867094.9 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3560398.5 3871296.5 3559750.4 3869463.9 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3561136.1 3873308.0 3560152.7 3871430.6 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3567125.7 3886471.7 3560957.3 3873844.4 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3562499.4 3870380.2 3561717.1 3870603.7 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3562834.6 3868771.1 3561583.1 3870156.7 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3558454.2 3876369.8 3556219.2 3875766.4 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3559236.4 3877666.1 3556599.2 3876459.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3560644.4 3878627.1 3560175.0 3878269.5 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3547525.4 3833124.0 3551637.6 3866446.7 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3550696.2 3876364.2 3551958.9 3878554.5 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3550819.1 3874263.4 3550768.8 3874956.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3551020.2 3873704.7 3550791.1 3874095.8 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3551612.5 3873078.9 3551199.0 3873375.0 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3551704.7 3867229.0 3551637.6 3868860.5 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3551768.9 3879683.1 3551579.0 3880074.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3551883.5 3870871.9 3551391.8 3872235.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3551950.5 3869285.1 3552017.6 3870357.9 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3552025.9 3879141.1 3552003.6 3879750.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3548573.0 3898802.9 3547254.4 3897864.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3556238.8 3903853.9 3547053.2 3896702.1 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3556663.4 3904569.0 3556305.8 3904502.0 0 inferred inactive from Sharon et al. (2020)

290
unnamed Sharon et al. (2020) 3565334.9 3909418.8 3557400.9 3905194.8 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3552126.5 3886712.0 3551500.7 3890868.9 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3546069.9 3895204.7 3545220.6 3895227.0 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3551232.5 3893394.4 3545287.6 3895897.5 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3549399.9 3892835.7 3547656.7 3893528.5 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3544259.6 3899406.3 3548997.6 3917308.1 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3544751.3 3889081.0 3544326.6 3897126.7 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3545108.8 3885460.4 3545108.8 3886354.4 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3546941.5 3881124.6 3545220.6 3884857.0 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3547299.1 3879604.9 3547053.2 3880610.6 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3543991.4 3900680.3 3538784.0 3897953.6 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3539789.7 3908591.9 3538247.6 3907921.4 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3542114.0 3887136.6 3538448.8 3882845.5 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3530984.1 3891315.9 3527095.3 3887382.4 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3533219.0 3877816.9 3531788.7 3876565.4 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3521552.7 3866865.8 3518960.2 3865323.7 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3522312.6 3868877.2 3520971.6 3869950.0 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3522692.5 3867402.2 3522334.9 3866709.3 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3524994.5 3867513.9 3523541.8 3867357.5 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3528212.8 3890176.1 3527296.5 3890433.1 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3529162.6 3892947.4 3527777.0 3892098.1 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3525910.8 3890377.2 3525206.8 3890667.8 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3526838.3 3893115.0 3524871.6 3892422.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3524491.6 3894355.4 3521630.9 3895092.9 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3523999.9 3903116.3 3522547.2 3902490.5 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3508891.8 3892053.4 3508333.1 3892053.4 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3509540.0 3892075.8 3509115.3 3892098.1 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3516356.5 3891159.5 3510188.1 3892522.8 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3520155.9 3890623.1 3518233.8 3891114.8 0 inferred inactive from Sharon et al. (2020)

291
unnamed Sharon et al. (2020) 3506589.9 3895741.1 3503975.0 3894467.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3508109.6 3896098.7 3507617.9 3895852.8 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3508958.9 3896903.2 3508489.5 3896299.8 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3509718.8 3897752.5 3509294.1 3897663.1 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3515775.4 3901261.3 3514166.3 3901417.8 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3509696.4 3894042.5 3508958.9 3894221.3 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3496488.0 3885505.1 3491437.0 3881795.1 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3497694.8 3887024.8 3497337.3 3886667.3 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3500153.3 3888544.6 3499751.0 3888164.7 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3502969.3 3891159.5 3502164.7 3890466.6 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3505137.2 3892656.9 3504600.8 3892165.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3505964.1 3893349.7 3505494.7 3893126.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3496689.1 3880610.6 3494543.6 3880342.4 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3490677.2 3877437.0 3488710.4 3877884.0 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3488241.1 3877973.4 3485782.7 3877481.7 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3485223.9 3878018.1 3480262.4 3877235.9 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3479703.7 3877280.6 3477960.4 3879470.8 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3483100.8 3879917.8 3478809.7 3879806.0 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3472361.9 3881783.9 3467087.5 3881493.4 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3475133.2 3882722.6 3471937.3 3881225.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3477032.9 3889025.1 3476004.9 3889695.6 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3478329.2 3884711.7 3475870.8 3883527.2 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3482150.9 3887281.9 3478686.8 3884957.5 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3482642.6 3886924.3 3481234.6 3886477.3 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3461287.9 3882018.6 3460874.4 3882342.7 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3462729.4 3881325.8 3461779.5 3882007.4 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3463925.1 3880588.3 3463310.5 3881068.8 0 inferred inactive from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3464461.5 3880431.8 3460919.1 3881471.0 0 inferred inactive from Sharon et al. (2020)

292
Amman
Hallabat Fault
Al
-Awabdeh et al.
(2016)
3739416.1 3970714.0 3750646.6 3987878.2
0 inferred inactive from Al
-Awabdeh et al. (2016)
Amman
Hallabat Fault
Al
-Awabdeh et al.
(2016)
3750998.6 3986554.0 3755624.9 3995354.0
0 inferred inactive from Al
-Awabdeh et al. (2016)
Amman
Hallabat Fault
Al
-Awabdeh et al.
(2016)
3754434.8 3994465.7 3755792.5 3996661.5
0 inferred inactive from Al
-Awabdeh et al. (2016)
Amman
Hallabat Fault
Al
-Awabdeh et al.
(2016)
3755122.1 3997047.0 3770526.3 4025223.8
0 inferred inactive from Al
-Awabdeh et al. (2016)
Karama Fault
Al
-Awabdeh et al.
(2016)
3749775.0 3968317.0 3751044.7 3970357.8
0 inferred inactive from Al
-Awabdeh et al. (2016)
Karama Fault
Al
-Awabdeh et al.
(2016)
3752767.0 3963288.4 3749179.9 3966540.2
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3744344.1 3971032.4 3749892.3 3972306.3
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3745869.4 3965953.6 3750445.5 3972541.0
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3746053.8 3965970.3 3748853.1 3967361.6
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3748316.7 3965635.1 3749121.3 3966707.8
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3749473.3 3967864.4 3753110.6 3973027.1
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3750579.6 3972624.8 3753831.4 3974653.0
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3751602.0 3971267.1 3753244.7 3973345.6
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3752767.0 3973270.2 3758591.8 3977628.3
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3752842.4 3973245.0 3766285.5 3977770.7
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3757301.1 3975357.0 3758943.8 3976228.6
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3759484.0 3976503.6 3762706.6 3977750.3
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3762595.5 3977917.0 3766012.7 3985209.7
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3763213.9 3977364.3 3759861.5 3976559.7
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3763623.4 3981778.7 3769582.6 3990210.5
0 inferred inactive from Al
-Awabdeh et al. (2016)
Shueib Fault
Al
-Awabdeh et al.
(2016)
3764206.9 3979500.6 3766346.1 3981653.7
0 inferred inactive from Al
-Awabdeh et al. (2016)

293
Shueib Fault
Al-Awabdeh et al.
(2016)
3766061.3 3981181.4 3771881.6 3985320.9 0 inferred inactive from Al-Awabdeh et al. (2016)
Shueib Fault
Al-Awabdeh et al.
(2016)
3767575.4 3982653.8 3769686.8 3984390.2 0 inferred inactive from Al-Awabdeh et al. (2016)
Shueib Fault
Al-Awabdeh et al.
(2016)
3768735.3 3987668.4 3776014.1 3994336.1 0 inferred inactive from Al-Awabdeh et al. (2016)
Shueib Fault
Al-Awabdeh et al.
(2016)
3769520.1 3983904.0 3774548.6 3987057.2 0 inferred inactive from Al-Awabdeh et al. (2016)
Shueib Fault
Al-Awabdeh et al.
(2016)
3771319.0 3992113.5 3775555.7 3999350.7 0 inferred inactive from Al-Awabdeh et al. (2016)
Shueib Fault
Al-Awabdeh et al.
(2016)
3772062.2 3985390.3 3776298.9 3988821.4 0 inferred inactive from Al-Awabdeh et al. (2016)
Shueib Fault
Al-Awabdeh et al.
(2016)
3774173.6 3985807.0 3776569.8 3987133.6 0 inferred inactive from Al-Awabdeh et al. (2016)
unnamed
AbdulWahe&Asfahani
(2018)
3990031.1 4063869.5 4085699.5 4143212.9 0 - 0.2 inferred minimal from Abdul-Wahe&Asfahani (2018)
unnamed Sharon et al. (2020) 3935793.8 3984585.0 3926898.8 3977120.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3925379.0 3961408.8 3922250.1 3961185.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3914070.3 3969029.9 3910494.4 3968851.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3914562.0 3970572.0 3909555.7 3969231.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3914852.5 3969275.8 3914427.9 3969208.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3920641.0 3970158.6 3914863.7 3970728.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3923010.0 3970661.4 3915031.3 3969253.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3926518.9 3971980.0 3924999.1 3970393.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3909343.4 3969231.1 3908661.8 3969275.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3909634.0 3969834.5 3907991.3 3969029.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3918193.7 3975164.8 3918428.4 3974572.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3918864.2 3975231.9 3909891.0 3969879.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3909589.3 3968448.9 3901130.1 3968627.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3898532.0 3968337.1 3894822.0 3968292.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3900968.0 3969197.6 3899928.8 3969208.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3901504.4 3968102.4 3899370.1 3968515.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3902633.1 3969823.3 3901325.6 3969264.6 0 - 0.2 inferred from Sharon et al. (2020)

294
unnamed Sharon et al. (2020) 3904270.1 3969320.5 3887552.9 3972862.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3908170.1 3969398.7 3904013.1 3969521.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3899761.2 3967208.5 3892955.8 3968381.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3901772.6 3983333.5 3895984.1 3983489.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3899872.9 3982372.5 3893905.7 3983110.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3893726.9 3973633.9 3890396.8 3975041.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3891134.4 3973231.6 3889927.5 3973924.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3886865.6 3970952.0 3883267.4 3972158.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3890776.8 3969387.5 3887178.5 3970840.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3891201.4 3967063.2 3889458.2 3967890.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3889748.7 3967085.6 3888273.6 3968091.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3881457.1 3975500.1 3877847.7 3975343.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3882507.5 3975555.9 3882038.2 3975544.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3893056.4 3978226.7 3882742.2 3975623.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3888508.3 3977008.6 3877289.0 3968348.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3905180.9 3962157.5 3901850.8 3966113.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3904577.4 3962649.2 3896040.0 3966292.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3896218.8 3964861.8 3894408.5 3966113.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3897582.1 3963208.0 3896799.9 3964146.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3898811.3 3961598.8 3898230.3 3962224.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3894609.7 3964258.4 3893335.8 3965554.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3894095.6 3963632.6 3892821.7 3964459.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3892263.0 3964571.3 3889938.7 3965912.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3899727.7 3959319.2 3899079.5 3959632.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3898498.4 3960459.0 3898185.6 3960838.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3885211.8 3958469.9 3878797.6 3957810.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3886429.8 3958425.2 3885558.2 3958402.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3898721.9 3959967.3 3886787.4 3958447.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3898464.9 3956894.3 3896654.6 3958615.2 0 - 0.2 inferred from Sharon et al. (2020)

295
unnamed Sharon et al. (2020) 3920143.7 3958548.1 3916947.8 3959777.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3871914.0 3969074.6 3867645.3 3968314.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3861901.5 3967845.4 3860895.8 3967554.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3866773.6 3968515.9 3863577.7 3968158.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3862951.9 3948736.8 3852224.3 3956804.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3866907.7 3946256.0 3863600.0 3948692.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3867108.9 3957229.5 3866527.8 3957386.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3868136.9 3944021.1 3867153.6 3945742.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3853900.5 3953385.4 3853162.9 3954949.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3853118.2 3957676.5 3851754.9 3958503.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3851017.4 3958704.6 3848514.3 3958726.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3850928.0 3958615.2 3849899.9 3958525.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3849073.0 3958369.3 3847754.4 3958615.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3848581.3 3958414.0 3846480.5 3958257.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3851442.0 3955888.6 3848514.3 3957117.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3849810.5 3956492.0 3846726.3 3955307.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3848290.8 3955888.6 3847061.6 3956045.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3844781.9 3956022.7 3844290.3 3955866.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3847061.6 3956626.1 3845698.3 3956380.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3846480.5 3955955.6 3845810.0 3955776.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3844983.1 3955374.5 3844089.1 3955262.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3844536.1 3951910.4 3843865.6 3955084.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3862594.3 3947284.1 3862348.5 3949317.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3883490.9 3919571.0 3882105.2 3923213.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3888139.6 3931974.8 3886306.9 3935014.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3883066.3 3915726.9 3882798.1 3919749.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3884563.7 3910921.8 3884541.3 3912374.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3875892.1 3925828.8 3878596.4 3918095.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3877523.6 3920465.0 3875691.0 3922945.7 0 - 0.2 inferred from Sharon et al. (2020)

296
unnamed Sharon et al. (2020) 3871444.6 3910586.6 3867801.7 3924085.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3855733.1 3923191.6 3854235.7 3932064.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3849475.3 3946434.8 3848335.5 3949720.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3849140.1 3943797.6 3846167.6 3951597.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3844558.5 3948937.9 3843217.5 3956804.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3839842.8 3940333.5 3837317.3 3942277.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3864941.0 3898182.7 3862370.8 3904552.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3837719.6 3918520.6 3834031.9 3912799.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3828712.8 3953530.7 3826142.7 3953664.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3824522.3 3951765.1 3822499.7 3952491.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3806441.8 3958347.0 3805816.0 3958604.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3806531.2 3958168.2 3805927.7 3958090.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3807950.3 3957743.6 3806698.8 3958257.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3808822.0 3958078.8 3808084.4 3958090.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3810364.1 3956648.4 3808129.1 3957698.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3802519.5 3956950.2 3800027.5 3957955.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3763732.3 3951843.3 3763307.7 3951888.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3766637.7 3952156.2 3765274.4 3952178.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3766905.9 3951374.0 3766123.7 3951396.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3764358.1 3946792.4 3762056.1 3948289.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3754144.4 3941071.0 3742277.0 3945272.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3748132.5 3954525.3 3747573.8 3954726.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3744199.0 3956268.5 3743752.0 3956156.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3735460.4 3946144.3 3734901.7 3947194.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3735683.9 3947999.3 3735058.2 3947932.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3725917.3 3944848.0 3725291.5 3946054.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3722587.3 3946367.8 3721715.6 3946591.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3755217.2 3897333.4 3752758.8 3895210.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3760849.2 3902429.1 3755485.4 3897534.6 0 - 0.2 inferred from Sharon et al. (2020)

297
unnamed Sharon et al. (2020) 3763441.7 3906452.0 3761117.4 3902742.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3669686.5 3920487.3 3666959.9 3927594.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3623982.2 3919950.9 3623110.6 3920688.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3581272.8 3922588.1 3580557.6 3922007.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3586457.8 3923727.9 3580892.8 3922185.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3579618.9 3921671.8 3565091.9 3919883.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3481067.0 3897853.1 3480832.3 3897886.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3482273.8 3897841.9 3481815.7 3897819.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3482944.3 3898154.8 3482564.4 3898087.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3484173.5 3898646.5 3483346.6 3898423.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3484754.6 3898534.7 3484285.3 3898534.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3488844.5 3899071.1 3488174.0 3898981.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3492241.6 3903049.3 3489291.5 3899294.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3493247.3 3900389.7 3490856.0 3899831.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3495683.4 3899786.3 3495124.7 3900188.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3497963.0 3900501.5 3493672.0 3900434.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3499482.8 3902311.8 3498141.8 3901529.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3501293.1 3901060.2 3497225.5 3900993.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3502544.6 3902736.4 3499795.7 3901261.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3506165.2 3904479.6 3503505.7 3903406.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3512601.8 3907966.1 3506656.9 3905060.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3513898.1 3910826.8 3510769.2 3909195.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3515797.8 3909597.6 3511305.6 3909284.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3516892.9 3912234.8 3515998.9 3911407.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3518747.9 3912726.5 3517608.0 3912123.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3522234.4 3913665.2 3514367.4 3911251.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3522927.2 3913776.9 3522256.7 3913352.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3469858.8 3893450.3 3469098.9 3894076.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3470104.7 3891707.0 3469769.4 3890500.2 0 - 0.2 inferred from Sharon et al. (2020)

298
unnamed Sharon et al. (2020) 3473390.0 3897137.9 3470127.0 3892444.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3474529.8 3896914.4 3474775.7 3895729.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3474686.3 3895327.6 3473948.7 3892064.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3474306.3 3890768.3 3470819.8 3892109.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3440391.3 3889338.0 3436949.5 3887237.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3440681.8 3886656.1 3439072.7 3885404.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3442760.3 3890701.3 3442022.8 3890477.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3443073.2 3891349.4 3442514.5 3890969.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3443877.8 3889941.4 3441464.1 3887371.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3444525.9 3886924.3 3443296.7 3885717.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3445151.7 3887393.6 3444883.5 3887192.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3447464.8 3893260.3 3446213.3 3892869.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3447587.8 3888578.1 3445486.9 3887505.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3449252.8 3894165.4 3447934.2 3893472.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3449945.6 3894500.7 3449643.9 3894366.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3451990.6 3895618.1 3451364.8 3895059.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3900607.7 3950228.6 3901680.4 3943523.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3887097.5 3963895.2 3884572.0 3959559.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3888505.5 3964476.3 3883991.0 3959291.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3890561.7 3965303.2 3887432.8 3964923.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3892193.2 3962777.7 3889623.0 3964766.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3881309.1 3972812.6 3873933.8 3971650.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3865619.9 3927398.8 3850042.4 3951625.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3865999.8 3925901.4 3865619.9 3926884.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3867474.8 3923353.6 3866133.9 3925454.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3829302.3 3908156.1 3826933.3 3926259.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3801812.7 3948295.4 3800047.1 3951245.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3804963.9 3944540.7 3801969.1 3948071.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3801544.5 3946708.6 3797409.9 3949368.2 0 - 0.2 inferred from Sharon et al. (2020)

299
unnamed Sharon et al. (2020) 3802282.0 3945837.0 3802013.8 3946194.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3808495.1 3938707.6 3806863.6 3939959.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3808517.4 3938059.4 3802527.8 3945412.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3808383.4 3932673.2 3803019.5 3940026.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3794929.1 3944183.1 3792671.8 3945635.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3795845.4 3942574.0 3795465.5 3943110.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3796515.9 3941680.0 3796158.3 3942149.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3797007.6 3942574.0 3795264.3 3944093.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3797186.4 3940987.2 3796918.2 3941210.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3797566.3 3940272.0 3797253.4 3940719.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3799108.4 3939266.3 3797208.7 3942283.4 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3803421.8 3935131.7 3797923.9 3939869.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3805455.6 3929410.3 3803824.1 3934617.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3793230.5 3941031.9 3790638.0 3934640.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3793610.5 3936942.0 3793297.6 3937880.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3795554.9 3933097.9 3794862.0 3934863.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3796717.0 3931913.4 3793029.4 3940115.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3790883.9 3930058.4 3790526.3 3931846.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3792046.0 3930304.2 3788403.1 3937321.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3787576.2 3932449.8 3782011.2 3942149.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3786838.6 3931980.4 3784134.4 3940048.5 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3784469.6 3934885.8 3772758.6 3939333.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3785743.5 3927756.4 3779731.6 3936629.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3782257.0 3928337.5 3773585.5 3931846.3 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3779753.9 3930594.8 3771708.2 3935645.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3768132.3 3928426.9 3761137.0 3930863.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3768177.0 3927443.5 3760935.8 3930304.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3449532.1 3885963.3 3447654.8 3885058.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3450258.5 3886533.2 3449353.3 3885449.2 0 - 0.2 inferred from Sharon et al. (2020)

300
unnamed Sharon et al. (2020) 3455622.3 3889002.8 3454102.6 3888578.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3456561.0 3887706.5 3450549.0 3886320.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3461164.9 3885158.7 3449409.2 3886253.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3456236.9 3884555.2 3454661.3 3885449.2 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3457566.7 3884454.7 3452448.7 3886376.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3455454.7 3884745.2 3446548.5 3884599.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3456862.7 3882745.0 3454024.3 3882286.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3453756.2 3882253.3 3444961.7 3883750.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3450202.6 3882152.7 3447051.4 3883136.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3446179.8 3884722.9 3445162.9 3884734.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3442011.6 3884767.6 3439184.4 3884711.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3443129.1 3884778.7 3442548.0 3884778.7 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3444224.2 3884711.7 3443598.4 3884734.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3444369.5 3883840.1 3440827.1 3883873.6 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3440313.1 3883739.5 3435262.1 3883057.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3441933.4 3887438.3 3441687.5 3887404.8 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3442458.6 3887494.2 3442212.8 3887416.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3442793.8 3887695.3 3442581.5 3887550.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3444704.7 3888075.3 3443039.7 3887740.0 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3447476.0 3888790.4 3444984.1 3888052.9 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Sharon et al. (2020) 3447900.6 3889114.5 3447565.4 3888879.8 0 - 0.2 inferred from Sharon et al. (2020)
Batroun Fault Elias et al. (2007) 4054021.5 3976751.6 4056256.4 4005716.3 0 - 0.2 inferred minimal
Batroun Fault Elias et al. (2007) 4057284.5 3968571.8 4057642.1 3976528.1 0 - 0.2 inferred minimal
Bishri Fault
AbdulWahe&Asfahani
(2018)
4171537.7 4232035.2 4195762.0 4371237.1 0 - 0.2 inferred minimal
Chekka Fault Elias et al. (2007) 4065017.3 3969242.3 4066090.1 3999190.3 0 - 0.2 inferred minimal
Hasbaya Fault Sharon et al. (2020) 3962389.4 3983020.6 3933246.0 3963353.2 0 - 0.2 Nemer & Meghraoui (2020)
Ithrya Fault
AbdulWahe&Asfahani
(2018)
4185931.9 4106174.3 4230167.5 4222029.6 0 - 0.2 inferred minimal

301
Jhar Fault
AbdulWahe&Asfahani
(2018)
4116330.9 4115126.7 4145294.7 4285047.8 0 - 0.2 inferred minimal
Khnefice
Fault
AbdulWahe&Asfahani
(2018)
4058842.1 4197103.1 4086226.1 4240110.0 0 - 0.2 inferred minimal
Okeirbat Fault
AbdulWahe&Asfahani
(2018)
4122299.2 4124956.9 4192690.1 4213252.6 0 - 0.2 inferred minimal
Palmyra Fault
AbdulWahe&Asfahani
(2018)
4079731.2 4234492.8 4146786.8 4309096.6 0 - 0.2 inferred minimal
Tirza Fault Sharon et al. (2020) 3932754.3 3961744.1 3922093.7 3960872.5 0 - 0.2 inferred from Sharon et al. (2020)
Tirza Fault Sharon et al. (2020) 3937760.5 3963375.6 3932910.7 3961811.1 0 - 0.2 inferred from Sharon et al. (2020)
unnamed Elias et al. (2007) 3933022.5 3920610.2 3947683.6 3942601.9 0 - 0.2 inferred minimal
unnamed Elias et al. (2007) 4021257.5 3962179.9 4020989.3 3990161.2 0 - 0.2 inferred minimal
unnamed Elias et al. (2007) 4030822.9 3966470.9 4037259.5 3999279.7 0 - 0.2 inferred minimal
Zobeideh
Fault
AbdulWahe&Asfahani
(2018)
3980025.399 4091077.97 4010744.607 4131627.324 0 - 0.2 inferred minimal
Carmel Fault Sharon et al. (2020) 3852363.943 3908161.674 3838954.375 3910374.253 0.2 - 1 inferred from Sharon et al. (2020)
Carmel Fault Sharon et al. (2020) 3854945.284 3907804.085 3852419.816 3908139.325 0.2 - 1 inferred from Sharon et al. (2020)
Carmel Fault Sharon et al. (2020) 3872355.373 3896607.096 3855012.332 3907781.736 0.2 - 1 inferred from Sharon et al. (2020)
Carmel Fault Sharon et al. (2020) 3885340.305 3887488.59 3872556.517 3896428.302 0.2 - 1 inferred from Sharon et al. (2020)
Carmel-Tirza
Fault system
Sharon et al. (2020) 3843647.723 3925236.524 3840049.489 3931516.671 0.2 - 1 inferred from Sharon et al. (2020)
Carmel-Tirza
Fault system
Sharon et al. (2020) 3845223.348 3912642.704 3844284.678 3914430.647 0.2 - 1 inferred from Sharon et al. (2020)
Carmel-Tirza
Fault system
Sharon et al. (2020) 3848910.979 3910117.236 3845446.84 3912441.561 0.2 - 1 inferred from Sharon et al. (2020)
Carmel-Tirza
Fault system
Sharon et al. (2020) 3852263.371 3908396.341 3849201.519 3909670.25 0.2 - 1 inferred from Sharon et al. (2020)
Eilat Fault Sharon et al. (2020) 3443170.979 3891656.731 3393510.88 3871229.489 0.2 - 1 inferred from Sharon et al. (2020)
Eilat Fault Sharon et al. (2020) 3446456.324 3893243.53 3443327.424 3891679.08 0.2 - 1 inferred from Sharon et al. (2020)
Gilboa Fault Sharon et al. (2020) 3830584.569 3944110.49 3828930.723 3944557.476 0.2 - 1 inferred from Sharon et al. (2020)
Gilboa Fault Sharon et al. (2020) 3830886.285 3943093.598 3823242.831 3948658.569 0.2 - 1 inferred from Sharon et al. (2020)

302
Gilboa Fault Sharon et al. (2020) 3839960.092 3931650.767 3830953.333 3946133.1 0.2
-
1 inferred from Sharon et al. (2020)
Mt Lebanon
Thrust System
Elias et al. (2007) 3976692.986 3883331.624 3986169.08 3889768.217 0.2
-
1 inferred from Elias et al. (2007)
Mt Lebanon
Thrust System
Elias et al. (2007) 3984381.138 3903982.359 3994483.012 3918196.501 0.2
-
1 inferred from Elias et al. (2007)
Mt Lebanon
Thrust System
Elias et al. (2007) 3986437.272 3892897.116 3995913.366 3903356.579 0.2
-
1 inferred from Elias et al. (2007)
Mt Lebanon
Thrust System
Elias et al. (2007) 4004227.298 3913011.468 4021123.354 3922129.974 0.2
-
1 inferred from Elias et al. (2007)
Mt Lebanon
Thrust System
Elias et al. (2007) 4010842.685 3939562.412 4018262.646 3945373.225 0.2
-
1 inferred from Elias et al. (2007)
Mt Lebanon
Thrust System
Elias et al. (2007) 4019424.809 3917302.529 4029794.874 3923202.739 0.2
-
1 inferred from Elias et al. (2007)
Mt Lebanon
Thrust System
Elias et al. (2007) 4021927.928 3949038.507 4027917.535 3958246.41 0.2
-
1 inferred from Elias et al. (2007)
Mt Lebanon
Thrust System
Elias et al. (2007) 4030599.449 3914888.807 4041952.883 3922040.577 0.2
-
1 inferred from Elias et al. (2007)
Mt Lebanon
Thrust System
Elias et al. (2007) 4049462.241 3930801.494 4027738.741 3928655.963 0.2
-
1 inferred from Elias et al. (2007)
Mt Lebanon
Thrust System
Elias et al. (2007) 4086293.854 3957799.424 4075744.994 3952346.2 0.2
-
1 inferred from Elias et al. (2007)
Mt Lebanon
Thrust System
Elias et al. (2007) 4089422.753 3944568.651 4095591.154 3954044.745 0.2
-
1 inferred from Elias et al. (2007)
Mt Lebanon
Thrust System
Elias et al. (2007) 4092551.652 3958246.41 4081019.424 3947161.167 0.2
-
1 inferred from Elias et al. (2007)
Ovgos Fault
Zone
Harrison et al.
(2012)
4318793.411 4015522.027 4198107.3 3590170.534 0.2
-
1 Harrison et al. (2012)
Roum Fault Sharon et al. (2020) 3901197.119 3960168.448 3898839.27 3960000.828 0.2
-
1 inferred from Sharon et al. (2020)
Roum Fault Sharon et al. (2020) 3910650.864 3960403.115 3909578.099 3960783.053 0.2
-
1 inferred from Sharon et al. (2020)
Roum Fault Sharon et al. (2020) 3911176.072 3959911.431 3901521.183 3960213.146 0.2
-
1 inferred from Sharon et al. (2020)
Roum Fault Sharon et al. (2020) 3916796.916 3959956.13 3911544.835 3959911.431 0.2
-
1 inferred from Sharon et al. (2020)
Roum Fault Sharon et al. (2020) 3920819.786 3959956.13 3910919.055 3960224.321 0.2
-
1 inferred from Sharon et al. (2020)
Saida Fault Elias et al. (2007) 3993946.63 3924498.997 3970971.57 3927359.705 0.2
-
1 inferred from Elias et al. (2003)
Tayasir Fault Sharon et al. (2020) 3808771.672 3945529.67 3801206.441 3951530.451 0.2
-
1 inferred from Sharon et al. (2020)
Tayasir Fault Sharon et al. (2020) 3810604.313 3942814.232 3809062.213 3945149.732 0.2
-
1 inferred from Sharon et al. (2020)
Tayasir Fault Sharon et al. (2020) 3818214.243 3930522.128 3810682.536 3942657.787 0.2
-
1 inferred from Sharon et al. (2020)
Tirza Fault Sharon et al. (2020) 3783259.97 3949429.619 3782723.587 3950290.066 0.2
-
1 inferred from Sharon et al. (2020)

303
Tirza Fault Sharon et al. (2020) 3792613.143 3942825.407 3783673.431 3949083.205 0.2 - 1 inferred from Sharon et al. (2020)
Tirza Fault Sharon et al. (2020) 3794859.246 3938757.838 3792702.54 3942099.055 0.2 - 1 inferred from Sharon et al. (2020)
Tirza Fault Sharon et al. (2020) 3796211.377 3936768.752 3794970.992 3938634.917 0.2 - 1 inferred from Sharon et al. (2020)
Tirza Fault Sharon et al. (2020) 3798077.542 3932690.008 3796323.124 3936589.958 0.2 - 1 inferred from Sharon et al. (2020)
Tirza Fault Sharon et al. (2020) 3798368.083 3931896.609 3798088.717 3932477.69 0.2 - 1 inferred from Sharon et al. (2020)
Tirza Fault Sharon et al. (2020) 3799831.96 3929058.25 3798625.099 3931337.877 0.2 - 1 inferred from Sharon et al. (2020)
Aakkar Thrust Elias et al. (2007) 4092730.446 4000888.836 4098898.847 4010811.916 0.2 - 1 inferred from Elias et al. (2003)
Aakkar Thrust Elias et al. (2007) 4097826.082 4002319.19 4105693.029 4030211.091 0.2 - 1 inferred from Elias et al. (2003)
RankineAabdeh Fault
Elias et al. (2007) 4095144.168 3959095.682 4097200.302 3986629.995 0.2 - 1 inferred from Elias et al. (2003)
RankineAabdeh Fault
Elias et al. (2007) 4097379.096 3987702.761 4097647.288 4000844.137 0.2 - 1 inferred from Elias et al. (2003)
Tripoli Thrust Elias et al. (2007) 4082270.983 3980193.403 4090495.518 3992977.191 0.2 - 1 inferred from Elias et al. (2003)
Tripoli Thrust Elias et al. (2007) 4086293.854 3991993.822 4109090.119 4041966.812 0.2 - 1 inferred from Elias et al. (2003)
AMZ Sharon et al. (2020) 3627083.203 3937159.864 3620311.371 3945384.399 0.2 - 1 inferred from Sharon et al. (2020)
AMZ Sharon et al. (2020) 3634816.053 3934746.142 3627261.997 3937159.864 0.2 - 1 inferred from Sharon et al. (2020)
Dead Sea East
Fault
Sharon et al. (2020) 3739500.08 3969722.765 3707875.849 3958190.537 0.2 - 1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3627072.028 3929706.38 3625742.246 3929326.442 0.2 - 1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3635710.025 3931583.719 3627284.346 3929706.38 0.2 - 1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3642157.792 3933438.709 3639844.641 3933326.963 0.2 - 1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3649074.894 3934444.427 3647543.968 3934142.712 0.2 - 1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3650427.025 3933338.138 3642437.158 3933427.535 0.2 - 1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3653466.527 3935919.479 3651320.996 3935260.176 0.2 - 1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3655410.915 3936210.02 3653790.592 3936142.972 0.2 - 1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3655422.089 3936031.226 3654505.769 3935662.463 0.2 - 1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3657880.51 3936444.687 3655522.661 3936601.132 0.2 - 1 inferred from Sharon et al. (2020)

304
Dead Sea
West Fault
Sharon et al. (2020) 3659210.292 3936444.687 3658361.02 3936422.338 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3660216.01 3936310.592 3656271.362 3936031.226 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3664562.945 3936534.085 3659646.103 3936489.386 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3664864.66 3935059.032 3661177.029 3936198.845 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3667412.478 3937204.563 3665356.344 3936768.752 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3668194.703 3936846.974 3665780.98 3936433.513 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3674094.913 3936880.498 3670552.552 3937182.214 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3675748.759 3936355.29 3674821.264 3936601.132 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3681291.381 3935863.606 3676095.173 3936277.068 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3683325.165 3936489.386 3681637.794 3935941.829 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3684073.866 3935628.939 3668541.117 3936869.324 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3684688.471 3937159.864 3683772.151 3936724.053 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3686990.447 3937930.915 3684532.026 3936187.671 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3687929.117 3938087.359 3686834.002 3936936.372 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3697516.958 3941026.29 3696153.652 3940635.177 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3705450.952 3940769.273 3699170.804 3940713.4 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3708892.741 3940657.527 3705808.541 3940825.146 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3717575.436 3942724.835 3709473.822 3940422.859 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3724481.364 3946568.911 3723140.407 3945943.131 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3725431.208 3946256.021 3715809.843 3942132.579 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3727185.627 3946881.801 3724749.555 3946680.658 0.2
-
1 inferred from Sharon et al. (2020)

305
Dead Sea
West Fault
Sharon et al. (2020) 3732214.215 3947820.471 3730716.813 3947462.882 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3733845.712 3947440.533 3726157.56 3946144.275 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3736304.133 3946725.356 3732482.406 3947865.169 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3738203.822 3946345.418 3736929.913 3946904.15 0.2
-
1 inferred from Sharon et al. (2020)
Dead Sea
West Fault
Sharon et al. (2020) 3742159.644 3945317.351 3738404.965 3946166.624 0.2
-
1 inferred from Sharon et al. (2020)
Rachaiya
Fault
Sharon et al. (2020) 3940040.167 3979768.766 3920953.882 3969029.937 0.2
-
1 inferred from Sharon et al. (2020)
Rachaiya
Fault
Sharon et al. (2020) 3990326.046 4010845.44 3936508.981 3978528.381 0.2
-
1 inferred from Sharon et al. (2020)
Rachaiya
Fault
Sharon et al. (2020) 3920730.389 3968940.54 3899096.286 3966750.311 0.2
-
1 inferred from Sharon et al. (2020)
Roum Fault Sharon et al. (2020) 3971418.556 3955916.497 3920886.834 3959956.13 0.2
-
1 inferred from Sharon et al. (2020)
Serghaya
Fault
Sharon et al. (2020) 4047106.409 4049672.622 3928691.797 3982547.231 1
-
3 inferred from Sharon et al. (2020)
Mount
Lebanon
Thrust
Elias et al. (2007) 3995823.969 3924677.792 4012898.819 3935673.637 1
-
3 Elias et al. (2007)
Mount
Lebanon
Thrust
Elias et al. (2007) 4014150.379 3936210.02 4023626.473 3940143.493 1
-
3 Elias et al. (2007)
Mount
Lebanon
Thrust
Elias et al. (2007) 4031538.118 3953150.774 4024028.76 3948144.535 1
-
3 Elias et al. (2007)
Mount
Lebanon
Thrust
Elias et al. (2007) 4032476.788 3954983.415 4052054.757 3957039.549 1
-
3 Elias et al. (2007)
Mount
Lebanon
Thrust
Elias et al. (2007) 4032744.979 3931829.561 4007445.595 3921548.892 1
-
3 Elias et al. (2007)
Mount
Lebanon
Thrust
Elias et al. (2007) 4048389.475 3936031.226 4054826.068 3954178.841 1
-
3 Elias et al. (2007)
Mount
Lebanon
Thrust
Elias et al. (2007) 4056971.599 3957844.123 4075387.405 3972952.236 1
-
3 Elias et al. (2007)

306
Mount
Lebanon
Thrust
Elias et al. (2007) 4057418.584 3944345.158 4063586.985 3954983.415 1
-
3 Elias et al. (2007)
Mount
Lebanon
Thrust
Elias et al. (2007) 4060905.072 3939607.111 4076907.156 3963118.553 1
-
3 Elias et al. (2007)
Bassimeh
Fault
Abou Romieh et al.
(2012)
3985457.872 4034483.076 4019354.718 4082256.444 1
-
3 Abou Romieh et al. (2012)
Cypriot Arc
Harrison et al.
(2012)
4076063.406 3757781.285 4101625.46 3574749.531 1
-
3 Harrison et al. (2012)
Cypriot Arc
Harrison et al.
(2012)
4114501.124 3555349.707 4256304.825 3538543.698 1
-
3 Harrison et al. (2012)
Larnaca Ridge
Harrison et al.
(2012)
4193279.856 4026249.681 4080170.151 3764629.012 1
-
3 Harrison et al. (2012)
Dead Sea East
Fault
Sharon et al. (2020) 3638961.845 3950088.923 3625865.167 3945484.971 3
-
5 inferred from Sharon et al. (2020)
Dead Sea East
Fault
Sharon et al. (2020) 3649331.911 3951966.262 3642403.634 3950893.497 3
-
5 inferred from Sharon et al. (2020)
Dead Sea East
Fault
Sharon et al. (2020) 3659836.072 3954469.382 3651164.552 3953217.822 3
-
5 inferred from Sharon et al. (2020)
Dead Sea East
Fault
Sharon et al. (2020) 3707305.942 3957687.678 3661713.412 3954737.573 3
-
5 inferred from Sharon et al. (2020)
Jordan Gorge
Fault
Sharon et al. (2020) 3919333.559 3960749.529 3898928.667 3966582.691 3
-
5 inferred from Sharon et al. (2020)
Damascus
Fault
Abou Romieh et al.
(2012)
3953997.361 4020818.41 3982703.753 4068803.633 3
-
5 Abou Romieh et al. (2012)
Jordan Gorge
Fault
Sharon et al. (2020) 3898738.698 3966526.818 3877015.198 3965632.847 3
-
5 inferred from Sharon et al. (2020)
Arava Fault Sharon et al. (2020) 3620143.751 3945216.78 3479522.083 3902976.641 3
-
5 inferred from Sharon et al. (2020)
Evrona Fault Sharon et al. (2020) 3348644.701 3877140.874 3265807.095 3843225.842 3
-
5 inferred from Sharon et al. (2020)
Evrona Fault Sharon et al. (2020) 3420743.477 3891042.126 3328485.65 3856266.646 3
-
5 inferred from Sharon et al. (2020)
Jericho Fault Sharon et al. (2020) 3710066.078 3948412.727 3632525.252 3935785.384 3
-
5 inferred from Sharon et al. (2020)
Jericho Fault Sharon et al. (2020) 3733130.535 3953083.726 3711038.272 3948099.837 3
-
5 inferred from Sharon et al. (2020)
Jericho Fault Sharon et al. (2020) 3768598.842 3957698.852 3733242.281 3953094.901 3
-
5 inferred from Sharon et al. (2020)
Jordan Valley
Fault
Sharon et al. (2020) 3798233.987 3960984.197 3764620.67 3958548.125 3
-
5 inferred from Sharon et al. (2020)
Jordan Valley
Fault
Sharon et al. (2020) 3825477.759 3960939.498 3813632.641 3961274.737 3
-
5 inferred from Sharon et al. (2020)
Jordan Valley
Fault
Sharon et al. (2020) 3853906.043 3966012.785 3797429.413 3960336.067 3
-
5 inferred from Sharon et al. (2020)

307
Jordan Valley
Fault
Sharon et al. (2020) 3859225.171 3966035.134 3855828.081 3965722.244 3 - 5 inferred from Sharon et al. (2020)
Jordan Valley
Fault
Sharon et al. (2020) 3875137.859 3967599.583 3861147.209 3966571.517 3 - 5 inferred from Sharon et al. (2020)
Yammouneh
Fault
Sharon et al. (2020) 4041416.5 4009755.912 3930921.661 3958883.364 5 - 7 Daëron et al. (2004)
Yammouneh
Fault
Sharon et al. (2020) 4056099.977 4017835.177 4042019.931 4008817.243 5 - 7 Daëron et al. (2004)
Yammouneh
Fault
Sharon et al. (2020) 4113761.119 4043447.452 4056435.216 4017231.746 5 - 7 Daëron et al. (2004)
Evrona Fault Sharon et al. (2020) 3484036.638 3906865.416 3433259.074 3889701.169 5 - 7 inferred from Sharon et al. (2020)
Missyaf Fault
Meghraoui et al.
(2003); Sbeinati et
al. (2010)
4107383.727 4045321.242 4129568.633 4044607.901 5 - 7 Meghraoui et al. (2003); Sbeinati et al. (2010)
Missyaf Fault
Meghraoui et al.
(2003); Sbeinati et
al. (2010)
4115658.483 4040969.862 4198334.709 4047033.26 5 - 7 Meghraoui et al. (2003); Sbeinati et al. (2010)

308
Table A.4: Coefficients of complexity (CoCo) values for all study sites of the four strike-slip plate boundary systems, for different values of radii.
Fault
system
Study site
CoCo values (mm/yr/km) for different radii r
r=50 km r=60 km r=70 km r=80 km r=90 km r=100 km r=120 km r=150 km r=200 km
AlpineMFS
Saxton River 0.329 0.330 0.295 0.262 0.236 0.217 0.186 0.151 0.122
Branch RiverDunbeath
0.060 0.077 0.099 0.138 0.192 0.195 0.182 0.153 0.129
Tophouse road 0.334 0.291 0.257 0.240 0.222 0.206 0.178 0.143 0.100
Hossack station 0.157 0.146 0.130 0.130 0.125 0.123 0.122 0.105 0.080
Hokuri Creek 0.034 0.031 0.027 0.023 0.021 0.020 0.018 0.019 0.020
San
Andreas
Wrightwood 0.084 0.102 0.107 0.131 0.151 0.184 0.188 0.158 0.124
Quincy 0.366 0.333 0.284 0.264 0.262 0.237 0.211 0.169 0.139
Van MatreWallace Creek
0.003 0.013 0.019 0.023 0.030 0.042 0.056 0.055 0.046
North
Anatolian
Güzelköy 0.019 0.043 0.059 0.062 0.058 0.060 0.057 0.058 0.039
Düzce 0.034 0.030 0.027 0.027 0.025 0.022 0.021 0.019 0.019
Demir Tepe 0.012 0.013 0.018 0.015 0.013 0.012 0.017 0.008 0.015
Dead Sea
Northern Wadi
Araba Valley
0.003 0.003 0.003 0.002 0.002 0.002 0.002 0.001 0.001
Beteiha 0.010 0.012 0.013 0.012 0.011 0.012 0.012 0.013 0.011

309
Table A.5: Plate motion rates and slip-rate variabilities for all sites. The slip-rate variability is the ratio between the fastest and slowest incremental
slip rates (in mm/yr) from the available slip-rate records. The uncertainties are given in terms of 1-σ confidence values.
Fault
system
Study site
References for
slip rate record
Plate
motion
rate
(mm/y
r)
Reference for
plate motion
rate
Fastest
increment
al slip
rate of the
record
+ σ - σ
Slowest
increment
al slip
rate of the
record
+ σ - σ
Slip rate
variabilit
y
(Fastest
SR/Slowe
st SR)
+ σ - σ Comments
AlpineMFS
Saxton River Zinke et al. (2017) 39
DeMets et al.
(1994)
16.80 28.20 7.60 1.40 0.50 0.40 12.00
20.5
9
6.4
2
Branch RiverDunbeath
Zinke et al. (2021) 39 15.21 23.10 6.20 3.85 0.20 0.30 3.95 6.00
1.6
4
Tophouse road Zinke et al. (2019) 39 9.60 5.00 2.50 2.00 2.20 1.20 4.80 5.84
3.1
4
Hossack station Hatem et al. (2020) 39 32.70
124.9
0
10.1
0
8.20 2.70 1.50 3.99
15.2
9
1.4
3
Hokuri Creek
Berryman et al.
(2012)
39 26.96 5.18 5.27 19.77 2.88 2.65 1.36 0.33
0.3
2
Slip-rate record rebuilt under the assumption that each event
has the same amount of horizontal slip (7.5 m)
SAF
Wrightwood Weldon et al. (2004) 49
DeMets et al.
(1994)
87.96 16.98
17.2
4
13.06 3.31 4.37 6.74 2.14
2.6
1
Reassessment of incremental slip rates based on Dolan et al.
(2016) data
Quincy
Onderdonk et al.
(2015)
49 16.07 2.45 2.45 12.16 0.79 0.79 1.32 0.22
0.2
2
The fastest rate calculated via Monte Carlo simulation (Zinke
et al., 2017) is based on the preferred three-event, 9.5 m
displacement occurring in the past ca. 600 years (i.e., we
ignore the likely two-event displacement recorded at this site
as being based on too-few earthquakes)
Van Matre-Wallace
Creek
Sieh & Jahns
(1984); Noriega et
al. (2006); GrantLudwig et al.
(2019); Salisbury et
al. (2018)
39 (for
r=100km
and 150
km)
42 (for r
= 200
km)
39.14 0.64 0.67 30.50 0.82 0.82 1.28 0.04
0.0
4
Slip-rate record taken as unique from all the cited sources
(constant slip rate). Plate motion rate reduced at WallaceCreek for radii of 100 and 150 km, as they do not include the
entire ECSZ.

310
NAF
Güzelköy
Meghraoui et al.
(2011)
27
DeVries et al.
(2017)
24.57 1.12 1.12 10.32
1.67
8
1.67
8
2.38 0.40
0.4
0
Calculated from very short record (see Table A.6)
Düzce Pucci et al. (2008) 21 15.34 1.95 1.95 13.02 1.16 1.16 1.18 0.18
0.1
8
Demir Tepe Kondo et al. (2010) 21 - - 1.00
inferred minimal from very short record (see Table A.6) that
highlights very low COVs in both earthquake time recurrence
and seismic slip
DSF
Wadi Araba Valley 1 Niemi et al. (2001) 7
DeMets et al.
(1994)
5.41 1.23 2.09 2.50 4.42 1.08 2.16 3.85
1.2
6
Only right-lateral
component of the plate
motion rate is retained
Slip rates recalculated with Monte
Carlo simulation (based on Zinke
et al., 2017, 2019 method)
Wadi Araba Valley 2 Klinger et al. (2000) 7 4.80 0.60 0.60 1.77 0.34 0.34 2.71 0.62
0.6
2
Beteiha
Wechsler et al.
(2018)
7 8.86 1.98 2.13 3.10 0.20 0.22 2.86 0.67
0.7
2

311
Table A.6: Notable specificities for slip-rate records available for all sites, and implications for slip-rate variability.
Fault
system
Site
References for slip rate
record
Specific timelength of record
Total
displacement
(m)
Youngest rate is…
Slip rate
variability
would ...… with
time passing slowest of the
record
fastest of the
record none
AlpineMFS
Saxton River Zinke et al. (2017) 12.9 ky 72.5 ×
Branch River -
Dunbeath Zinke et al. (2021) 11.9 ky 58.8 ×
Tophouse road Zinke et al. (2019) 11.2 ky 47.0 × get even higher
Hossack station Hatem et al. (2020) 13.7 ky 210 × get even higher
Hokuri Creek Berryman et al. (2012) 7.9 ky 180 ×
SAF
Wrightwood
Weldon et al. (2004);
Dolan et al. (2016)
quite short (~1.6
ky) 45.7 ×
Quincy
Onderdonk et al.
(2015)
quite short (~1.8
ky)
25.7 ×
get lower
Van Matre-Wallace
Creek
Sieh & Jahns (1984);
Noriega et al. (2006);
Grant-Ludwig et al.
13.3 ky 130 × get higher

312
(2019); Salisbury et al.
(2018)
NAF
Güzelköy
Meghraoui et al.
(2011)
quite short (~2
ky) 35.4 × get lower
Düzce Pucci et al. (2008) 60.2 ky 890 × get higher
Demir Tepe Kondo et al. (2010) very short (~1ky) 19.9 × get higher
DSF
Wadi Araba Valley
1 Niemi et al. (2001) 13.8 ky 54.0 ×
Wadi Araba Valley
2 Klinger et al. (2000) 120 ky 500 × get lower
Beteiha Wechsler et al. (2018) ~3.7 ka 14.3 × get higher

313
A.3. Fault databases used in this study
A.3.1. New Zealand fault database
The active fault database generated by Litchfield et al. (2014) provides slip-rate data and fault trace
maps for New Zealand. This database was compiled at a scale of 1:250,000 and is considered to be a
simplified fault database by its authors. Another fault database, the New Zealand Active Fault Database
(NZAFD), generated by Langridge et al. (2016), has a higher level of detail for fault traces, but could not
be used in our study because, unlike the Litchfield et al. (2014) database, it does not include offshore faults.
To determine whether our use of the simplified fault representations we used from the Litchfield et al.
(2014) database had a significant impact on our CoCo calculations, we compared the two databases for the
onshore faults common to both databases. This comparison demonstrated that using one fault database
versus the other resulted in only minor differences in the inputs to our CoCo analysis. Specifically,
comparison of the results based on these two data bases led to only an ~3% difference of fault lengths, with
the more detailed NZAFD fault database leading to longer fault lengths than the Litchfield et al. (2014)
database). This minor difference was taken into account in our final calculations of the CoCo values. This
minor difference likely reflects the relative linearity of the surface traces of the mainly strike-slip faults we
used for the Alpine-Marlborough fault system and the other plate boundaries discussed in this study, which
will minimize any variations in fault trace dependent on the scale of the map compilation.
A.3.2. California fault database
The United States Geological Survey’s Quaternary fault database was used for the California fault
traces (Schmitt, 2017; https://www.usgs.gov/natural-hazards/earthquake-hazards/faults), whereas
geological slip rates for the faults used in this study were mainly gathered from the UCERF-3 compilation
(Field et al., 2015). In addition, a few slip-rate values, as detailed in Table A.1, were taken from the Southern
California Earthquake (SCEC) Data Center website at Caltech (https://scedc.caltech.edu/). Faults for which

314
(a) no geological slip-rate data were provided within the UCERF-3 compilation, or (b) which were not
found in the literature, or (c) that were denoted as having a Middle to Latest Quaternary age within the
USGS California fault database were assigned to our minimum slip rate range of ]0 – 0.2[ mm/yr, whereas
those specified in the USGS database as “undifferentiated Quaternary” were assigned a zero slip-rate value.
Finally, the blind thrust faults that comprise the Great Valley Thrust fault system, which are not included
in the USGS’s Quaternary fault database, were added using available data from the SCEC Community Fault
Model (https://www.scec.org/research/cfm-viewer/) (Plesch et al., 2007). For all blind thrust faults in our
analysis, it is the vertical projection of the up-dip limit of the thrust ramp to the surface that is shown in
Figure 2.1c and Figure 2.3, and it is the length of that projection that is used in the computation of the
Coefficient of Complexity.
A.3.3. North Anatolian fault system database
For our CoCo analysis of the North Anatolian fault system, we digitized the fault traces published in
Emre et al. (2018) for all faults within our area of analysis within Turkey. Slip rates used in our analysis
were based on our compilation of a large number of published studies (Finetti et al., 1988; Okumura et al.,
1993; Bozkurt and Koçyiðit, 1996; Altunel et al., 1999; Armijo et al., 1999; Koçyı̇ğı̇t et al., 2001; Amit et
al., 2002; Hubert-Ferrari et al., 2002; Cetin et al., 2003; Karaca, 2004; Duman et al., 2005; Vanneste et al.,
2006; Zhu et al., 2006; Kozacı et al., 2007; Seyrek et al., 2007; Kürçer et al., 2008; Pucci et al., 2008; Kuşçu
et al., 2009; Rockwell et al., 2009; Kaymakci et al., 2010; Kondo et al., 2010; Gasperini et al., 2011;
Özkaymak et al., 2011; Zabci et al., 2011; Akyuz et al., 2012; Kürçer and Gökten, 2012; Duman and Emre,
2013; Peyret et al., 2013; Özsayın et al., 2013; Uzel et al., 2013; Özmen et al., 2014; Yılar, 2014; Kahraman
et al., 2015; Sarıkaya et al., 2015; Sözbilir et al., 2016; Topal et al., 2016b; Mozafari et al., 2019; Sançar et
al., 2019; Emre et al., 2020).

315
For faults for which no slip-rate studies have been published, we used the Earthquake Model for the
Middle East fault database (EMME) created by Danciu et al. (2018) and Erdik et al. (2012) to assign sliprate ranges to these faults (see Table A.2).
Slip rates were assigned to the faults taken from the Emre et al. (2018) database using the following decision
tree:
• if slip-rate data were found in the literature, we assigned the published slip-rate range to the given
fault
• if no slip rate could be found in the literature, we used the information contained in the explanation
given in the published fault database of Emre et al. (2018):
 if the fault is mapped as a Holocene fault:
- we assigned the lowest value of the geodetic strain accumulation range from the EMME
database as a slip rate, if the fault traces matched between the EMME and Emre et al. (2018)
databases (i.e., if the fault traces exist at the same location in both databases).
- we assigned a minimum slip-rate range of 0-0.2 mm/yr if there was no match of fault traces
between the EMME and the Emre et al. (2018) databases (range of ]0 - 0.2 mm/yr[). These are
noted as “inferred minimal” in Table A.2.
 if the fault is mapped as a Quaternary fault:
- we assigned the lowest value of the geodetic strain accumulation range from the EMME
database as a fault slip rate, if the fault traces in Emre et al. (2018) and in the EMME database
match.
- we assigned a null slip rate if no match was found with the EMME database. Mentioned as
“inferred null” in Table A.2.

316
Because the values from the EMME data base are mostly derived from geodetic measurements, we
considered them as maximum estimates for the slip rates of individual faults in Turkey, hence the decision
node 2.a.i.
For the faults located in Bulgaria, we only considered those that are included within the largest
circles of observation used for the CoCo analysis. We used the fault traces from Glavcheva & Matova
(2014) and slip rates were assigned using the following decision tree:
• if slip-rate data were found in the literature, we assigned the related slip-rate range to the given
fault, as noted in Figure 2.2,Figure 2.3,Figure 2.4, Figure 2.5.
• if no slip rate could be found in the literature, we assigned the lowest value of the geodetic elastic
strain accumulation range from the EMME database as a slip rate.
A.3.4. Dead Sea fault system database
Most of the fault traces of the Dead Sea Fault system for faults located in Israel come from the fault
database of Sharon et al. (2020). We digitized the fault traces from the Sharon et al. (2020) database, which
was built by compiling 1:50,000 scale geological maps. For the other faults in the DSF system that we used
in our CoCo analysis, we compiled data from published studies in Syria and Lebanon (Meghraoui et al.,
2003; Elias et al., 2007; Sbeinati et al., 2010; Abou Romieh et al., 2012; Harrison et al., 2012; Al-Awabdeh
et al., 2016; Abdul-Wahe and Asfahani, 2018). The slip rate ranges were assigned to faults by using
published studies (Meghraoui et al., 2003; Daëron et al., 2004a; Elias et al., 2007; Sbeinati et al., 2010;
Abou Romieh et al., 2012; Harrison et al., 2012; Al-Awabdeh et al., 2016; Abdul-Wahe and Asfahani,
2018; Nemer and Meghraoui, 2020) or were inferred from Sharon et al. (2020) study (see Table A.3).
Specifically, in Table A.3 we used the following terms to denote which method was used for which faults:

317
- Inferred minimal: if (i) published studies indicate that the fault is active, but that no recent activity
has been observed, suggesting that the fault is probably slipping at slow rates, or if (ii) there are no
data available, but we suspect that the fault might be active with a very slow slip rate based on
geomorphic expression.
- Inferred from [citation]: if published studies suggested slip-rate ranges for faults (e.g., > 1 mm/yr
or ~0.5-1 mm/yr in Sharon et al. (2020))
- Inferred inactive: if there is no evidence of tectonic activity on the fault, or if a paper has
specifically indicated that the fault was likely inactive.
Finally, we note that the relatively few comprehensive studies that have been conducted in Syria suggest
that fault traces and slip-rate data in this region may not be complete.
A.4. Additional details about how slip-rate data were used in the computation of
CoCo
As we highlight in the discussion section of the main text, slip rates can be variable through time, which
makes the choice of the “correct” slip-rate value difficult to establish for probabilistic seismic hazard
analysis (PSHA). This could apply as well to the CoCo computation, since we use fault slip-rate data as
one of the parameters that make up the CoCo mathematical formula.
As described in the main text, CoCo is calculated with the following equation:
CoCo site (r) =
r²
v L
Nf 

 (1)
Where Nf is the number of faults within the circle of specified radius r, v is the velocity or slip rate (in
mm/yr) of the fault section of length L (in km) within the circle. This sum is scaled by the πr² area (in km²)

318
of the circle of radius r. The unit of this coefficient is thus mm/yr/km. The unit of this coefficient thus
effectively describes a density of fault activity within a given circle of observation. We use the following
values for the radius r: 50, 60, 70, 80, 90, 100, 120, 150 and 200 km.
Because of the large uncertainty intervals that accompany some of the slip rate values we use, and the
different ranges that might be found from one study to another, we assign a slip rate range to each fault.
Specifically, in mm/yr these ranges are: 0; ]0 – 0.2[; [0.2 – 1.0[; [1.0 – 3.0[; [3.0 – 5.0[; [5.0 – 7.0[; [7.0 –
10[; [10 – 15[; [15 – 20[; and ≥20 (where open brackets refer to the exclusion of the value in the interval,
and closed brackets include that value). For each slip-rate range, a median value for the pertinent slip-rate
bin is then used for the calculation of CoCo, as follows:
- For the slip-rate bin ]0 – 0.2[, v = 0.1 mm/yr in equation (1)
- For the slip-rate bin [0.2 – 1.0[, v = 0.5 mm/yr in equation (1)
- For the slip-rate bin [1.0 – 3.0[, v = 2 mm/yr in equation (1)
- For the slip-rate bin [3.0 – 5.0[, v = 4 mm/yr in equation (1)
- For the slip-rate bin [5.0 – 7.0[, v = 6 mm/yr in equation (1)
- For the slip-rate bin [7.0 – 10[, v = 8 mm/yr in equation (1)
- For the slip-rate bin [10 – 15[, v = 12 mm/yr in equation (1)
- For the slip-rate bin [15 – 20[, v = 17 mm/yr in equation (1)
- For slip rates ≥ 20, v = 24 mm/yr in equation (1)
The available slip-rate values are not homogeneous nor equal in terms of their levels of certainty, yet
CoCo is a single number. For this reason, we need to assign one value of slip rate for its computation. This
is why slip-rate ranges were first assigned to a fault (or fault segment) and then a median value was used
for the final computation of CoCo.

319
Slip-rate values that are derived for faults that have non-constant incremental slip rates are averaged
over the entire studied time range (i.e., the longest-possible time span that was documented in each study).
A.5. A note about the potential importance of the scale used in fault trace
compilations when computing the CoCo metric
In this initial study exploring the calculation and utility of the CoCo metric, we intentionally focused
on strike-slip fault-dominated plate-boundary fault systems to obviate as much kinematic complexity as
possible. This is important because different fault-trace databases can be compiled at widely different
scales. In the case of the strike-slip faults we discuss in this paper, the scale of the fault trace compilations
will have relatively little effect on the resulting CoCo calculations. This is because strike-slip faults are
typically relatively linear such that even relatively coarse-grained fault compilations are likely to capture
near-complete fault-trace lengths within any CoCo measurement radius. In contrast, the scale of the fault
trace compilation could have a more significant impact on the calculation of the CoCo values for dip-slipdominated fault networks. This is because along dipping faults, the true fault-trace length will be controlled
be both the fault dip and the intersection of the fault plane with topography at the Earth’s surface. Thus, the
measured length of an irregular fault trace will typically increase with finer- and finer-grained detail of the
map compilation scale. The net result in such situations would likely be longer fault traces that would be
included in any CoCo measurement radius for more detailed fault compilations relative to maps compiled
at coarser-grained scales. We bring this up not in relation to this study but as a cautionary note for future
studies that may employ the CoCo metric in dip-slip fault dominated regions.

320
Appendix B. Supplements for CHAPTER 3
This appendix includes additional information on the methods used in CHAPTER 3, as well as
supplementary figures.
B.1. Calculation of CoCo values
Values of the Coefficient of Complexity (CoCo) for the Haiyuan, Altyn Tagh and Kunlun faults were
calculated using the Himalayan fault system database from Mohadjer et al. (2016).
The CoCo value for the Denali fault was calculated thanks to the use of the Alaskan fault system
database (Koehler et al., 2011).
The CoCo calculation follows the one detailed in CHAPTER 2 and Appendix A. One slight change has
been brought: Instead of using slip-rate bins up to 20 mm/yr, we further slice the possible ranges into the
following ones, for slip rates that are faster than 20 mm/yr: [15 – 20[; [20 – 25[, [25 – 30[ and >30, for
which we assign a median value for the CoCo calculation: 17, 22, 27, and 35 mm/yr respectively (see
CHAPTER 2 and Appendix A for complete methodology). This only aims at assigning values to fastslipping sections that are closer to the actual slip rate, which applies to the CoCo calculation of the sites
located on the Calico the Garlock and the San Jacinto faults only.
B.2. Remarks on the behavior of faults with intermediate CoCo values
Some faults are neither truly low-Coco nor high-CoCo, but rather fall into an intermediate area among
the whole range of CoCo values. For example, the Central Denali (16) and the Altyn Tagh (18) faults exhibit
intermediate CoCo values that help us define an approximate boundary low- and high-CoCo faults. As
noted by Dolan & Meade (2017), the central Denali fault’s long-term/large-displacement slip rate, 12.1 ±
1.7 mm/yr, averaged over 12 ky and 144 m (Matmon et al., 2006) is faster than its geodetic slip-deficit rate

321
of 7.0 ± 0.3 mm/yr inferred from block model analysis (Elliott and Freymueller, 2020) (Table 1). The Denali
fault, according to its relatively low CoCo value, would be thought to behave in a relatively constant
manner, since the only major faults it might interact with are the Totschunda-Duke River fault (slipping at
~ 6 mm/yr during the Holocene, Matmon et al., 2006) and the Susitna Glacier fault (a slow-slipping thrust
fault). Elsewhere along the Denali fault, the geodetic rates fall within a range of 6 to 8 mm/yr. Other sliprate sites located more to the west exhibit slower geologic slip rate values, such as 9.4 ± 1.6 mm/yr (data
point 17 in Figure 3.2; Matmon et al., 2006), which still is faster than the elastic strain accumulation rate,
which at that location is 7.8 ± 0.3 mm/yr (Elliott and Freymueller, 2020). This geologic/geodetic rate ratio
<1 for a low-CoCo fault might be explained by possible long-term post-seismic effects of the 1964 Mw 9.2
Alaska earthquake, which might add up to the ones of the 2002 Denali earthquake. Alternatively, given that
the Aleutian megathrust is characterized by a flat and shallow slab (Jadamec et al., 2013), and that it might
be located less than 100 km below the Denali fault (Martin-Short et al., 2018), the interaction between the
slab and the Denali fault would need to be accounted for in the CoCo calculation. This, however, is
speculative since it would require three-dimensional considerations, whereas the CoCo analysis, as initially
designed and used here, considers the two-dimensional (ground surface) relationships among fault systems.
On the other hand, the Altyn Tagh fault exhibits a geologic slip rate behavior that is constant, with a
slip rate of 9.4 mm/yr averaged both over 54 m and 156 m displacements (Cowgill, 2007; Cowgill et al.,
2009), very close to the collocated slip-deficit geodetic rate of 9 ± 4 mm/yr (Bendick et al., 2000). Several
active reverse faults parallel to the Altyn Tagh fault (Yun et al., 2020) participate in the CoCo value, which
places the Altyn Tagh fault’s behavior between the CoCo values of the central Denali fault (16) and the
Kunlun fault (19).
B.3. Comparison of geodetic rates with geologic rates
Figure B.1 displays the slope of each geodetic rate/geologic rate comparison, with geologic rates
differentiated by the displacement over which they are averaged.

322
As mentioned in the main text, assuming a linear relationship between geologic slip rates and geodetic
rates going through the origin, we find a scaling line with best-fit slope and 1σ confidence of 0.945 ± 0.028
for low-CoCo faults using the large-displacement geologic rates, and a scaling line with best-fit slope of
1.103 ± 0.050 for the small-displacement average geologic rates.
For the high-CoCo faults, we find a scaling line with best-fit slope of 0.696 ± 0.140 using the largedisplacement geologic rates and a scaling line with best-fit slope of 0.751 ± 0.162 using the smalldisplacement geologic rates.

323
Figure B.1: Geodetic rate and geological slip rate comparisons for selected strike-slip faults. (a) and (b)
for low-CoCo faults, (c) and (d) for high-CoCo faults. The dark line and the two faded lines show the linear
fits with 67% confidence intervals with slopes indicated on each plot.
B.4. Most recent events and recurrence intervals
The following table summarizes the available information on the studied strike-slip faults regarding
their most recent event and average recurrence interval.

324
Table B.1: Date of most recent earthquakes that occurred on the studied strike-slip faults.
Fault Fault section MRE References for MRE Cluster of events?
Mean
recurrence
interval
(years)
References for
recurrence interval
Elapsed time
since MRE
(years)
Garlock central 1545 C.E. Dawson et al. (2003) yes (500 yrs rec) 1000 478
San Andreas Mojave 1857 C.E. ~100 Scharer et al. (2017) 166
San Andreas Carrizo plain 1858 C.E. 88 ± 41 Akçiz et al. (2010) 166
San Jacinto Claremont 1744-1850 C.E. Onderdonk et al. (2015a)
most recent cluster
occurring between
A.D. 1400 and A.D.
1850
156-195
(~164)
Onderdonk et al.
(2015a)
226
Owens Valley northern 1872 C.E. Beanland and Clark (1993) 3000-4100 Lee et al. (2001) 151
Calico fault northern 0.6-2.0 ka* Ganev et al. (2010) ~1500-2000 Ganev et al. (2010) ~600-2000
Hope Taramakau 1800-1840 C.E. Vermeer et al. (2022) 203
Hope Conway ~1840 C.E. Hatem et al. (2019) ~291 Hatem et al. (2019) 183
Hope Hurunui 1888 C.E. Khajavi et al. (2016) 298±88 Khajavi et al. (2016) 135
Wairau 268-1048 C.E. Nicol and Dissen (2018)
onshore section could
have experienced a
period of increased
earthquake frequency
since 5600 yr BP
~1000 1365
Clarence 110-310 C.E. Van Dissen and Nicol (2009) ~1700 1813
Awatere 1848 C.E. Mason et al. (2006) 820–950 Mason et al. (2006) 175
Alpine 1717 C.E.
(Berryman et al., 2012b; De
Pascale and Langridge, 2012)
329 306

325
Table B.1 – continued
Fault Fault section MRE References for MRE Cluster of events?
Mean
recurrence
interval
(years)
References for
recurrence interval
Elapsed
time since
MRE
(years)
Dead Sea Wadi Araba 1458 C.E. Klinger et al. (2015) seismic lull ~280 Marco et al. (1996) 565
Dead Sea Beteiha 1202 C.E. Wechsler et al. (2018) past 1200 years=lull 190 Wechsler et al. (2018) 264
Yammouneh
Lebanese restraining
bend
1202 C.E. Daëron et al. (2007)
1127±13
5
Daëron et al. (2007) 821
Yammouneh Missyaf (Syria) 1170 C.E. Meghraoui et al. (2003) 550
Meghraoui et al.
(2003)
853
Rachaiya-Sergaya ~1759 C.E. Nemer et al. (2008) 1300 Gomez et al. (2003) 264
Roum 84-239 C.E.
Nemer and Meghraoui,
(2006)
1861.5
Fairweather 1958 C.E. Witter et al. (2021) 65
Queen Charlotte 2013 C.E. Brothers et al. (2020) 10
Denali Central 2002 C.E. Matmon et al. (2006) 21
Denali Western
Altyn Tagh Pingding (Xorxol) 1491–1741 C.E. Yuan et al. (2018) 620±410 Yuan et al. (2018) 407
Kunlun west 2001 C.E. Klinger et al. (2015) 300±50 Li et al. (2005) 22
Haiyuan Lenglongling 1540 C.E. Jolivet et al. (2012)
1430±14
0
Jiang et al. (2017) 483
North Anatolian Erzincan (east) 1939 C.E. Kozacı et al. (2011) 685 Hartleb et al. (2006) 84
North Anatolian Gerede (Demir Tepe) 1944 C.E. Hubert-Ferrari et al. (2002) 250-300 Kondo et al. (2010) 79
North Anatolian Tahtaköprü 1943 C.E. Kozaci et al. (2011) 250-623
Okomura et al. (2003);
Kondo et al. (2004)
80
North Anatolian
Ganos
(Güzelköy+Cinerçik)
1912 C.E. Meghraoui et al. (2012) 323±142
Meghraoui et al.
(2012)
111
East Anatolian Pazarcık 2023 C.E. Barbot et al. (2023) ~772 Güvercin et al. (2022) 0.8
* a small segment of the northern section of the Calico fault ruptured in an aftershock (magnitude 5) of the Landers earthquake
in 1995

326
B.5. Choice of geodetic rates
For all geodetic slip-deficit rates used in this study, we used published values collocated with geological
slip-rate data.
For the North Anatolian fault, we use DeVries et al. (2017) and choose the results from their viscoelastic
model by averaging the values given by the model with ηM = 1019.0 Pa·s, ηK = 1019.0 Pa·s on the one hand,
and ηM = 1018.6 Pa·s, ηK = 1018.0 Pa·s on the other hand. Only the first model is given for the Ganos segment
(Meghraoui et al., 2012), so we use this one only for this site.
B.6. Measure of the dispersion in Figure 3.3b
We measure the dispersion of the data points in order to illustrate the difference between data plotting
in the low-CoCo region and the data plotting in the high-CoCo region in Figure 3.3b of the main text.
This measure accounts for the distance between each plotted datum (the ratio between the geodetic slipdeficit rate to the geologic slip rate) and the 1:1 relationship line. We want to consider a ratio a/b (where a
and b are real numbers) equally as we would consider ratio b/a. To do this, we take the inverse of all ratios
that are less than 1 (case A in Figure B.2).
We then take the distance of each point to the 1:1 ratio line. Since it is a vertical distance, it simply is
the subtraction of 1 to the ratio (Euclidian distance).
This measurement accounts for the two data points for a single fault when there are two geologic sliprate estimates (i.e., one averaged over a small displacement, and one averaged over a large displacement).
In these cases, the plotted distance on Figure 3b is the sum of the two calculated distances (case B in Figure
B.2).

327
To account for a potential lack of one of the geologic slip-rate estimates (i.e., whether the smalldisplacement geologic slip rate or the large-displacement geologic slip rate is missing), we multiply the
available single distance by two (case C in Figure B.2).
Figure B.2: Illustration of the measurement of data dispersion shown in Figure 3.3b (CHAPTER 3).
B.7. Dispersion of data points in Figure 3.2c
For each high-CoCo fault plotted in Figure 3.2c, we measured a distance from the respective data point
(x = geodetic slip-deficit rate; y = small-displacement slip rate values) to the 1:1 line, which is the shortest
distance from the point to the 1:1 line.
We defined fast-slipping faults as those characterized by a small-displacement slip rate that is at least
8 mm/yr, and slow-slipping faults as these characterized by a small-displacement slip rate that is less than
8 mm/yr. Using this rule, we have the following faults that fall in the fast-slipping category: the Garlock

328
fault (1), the Mojave section of the San Andreas fault (2), the San Jacinto fault (4), the Hope fault (7) and
the northern North Anatolian fault (23).
We found that for all high-CoCo faults, the average distance from the data points to the 1:1 line is 3.65
(arbitrary units). The average distance for fast-slipping high-CoCo faults only is 6.14, whereas the average
distance for slow-slipping high-CoCo faults is 1.87.

329
Appendix C. Supplements for CHAPTER 4
This appendix includes:
- Supplementary figures (Figure C.1 to Figure C.6) which entail:
o Uninterpreted hillshade map of Bluff Station
o Restorations of the two cumulative offsets (with minimum, preferred, maximum
restorations)
o Photographs of sampling pits
- The methodology for the correlation of T7 north and south of the Kekerengu fault
- Descriptions of the probability density functions used as inputs into the RISeR code
C.1. Map and restoration figures supporting Chapter 4

330
Figure C.1: Hillshade map (1-m resolution lidar data; sourced from the LINZ Data Service and licensed for reuse under the CC BY 4.0 licence) of
the Bluff Station site.

331
Figure C.2: Co-seismic dextral measurements (in meters) at the Bluff Station site from Kearse et al. (2018) on top of Digital Terrain Model
(acquired after the earthquake by Zekkos, 2018; processed by GNS Science). The locations of the measurements are marked by red arrows. The
displaced features are indicated as well as the uncertainties on the displacement measurements (2σ). For the coseismic displacement at Bluff Station,
we use the average of the measurements from the two offset farm tracks and the offset hedge row. We did not use the measurement from the cottage
foundation, which likely does not directly inform on the actual displacement at this location.

332
Figure C.3: Restorations of T3B/T6 riser. White shaded areas hide the fault zone to allow better visualization of restoration.

333
Figure C.4: Restorations of T6/T7A riser.

334
Figure C.5: (a) Context for the location where charcoal samples were found at pit 23-02 (eastern edge of
terrace T6B). (b) Close photograph showing the sampling location for the charcoal samples, prior to
sampling. (c) and (d) are photographs taken from a microscope, showing the size and shape of the charcoal
bits.

335
Figure C.6: Photographs of each sampling pit at the Bluff Station site, with location of each IRSL sample.
C.2. Method for correlating T7 surface upstream and downstream of the fault
Here we display the steps we followed to determine if the surface located down and east of T6 north of
the fault relates to the same surface as the one down of T6 south of the fault, both north of the riser that
parallels the fault trace, and south of T6. We assumed that the depth of the gravels is approximately constant
along the gradient of the river.

336
- We calculated the elevation of the top of the river gravels at sample locations 23-01, 23-02 and 23-07,
given the elevation of the sample location and the depth of the gravels.
- We measured the amount of vertical displacement across the Kekerengu fault due to the Kaikōura
earthquake, along profile AA’ (Figure C.7). We determined a vertical displacement of ~0.9 m.
- We traced a profile along the base of the riser down to T6 terrace north of the fault, and along the base
of the riser down to T6 south of the fault (profile BB’; Figure C.7). We measured a vertical difference
between the surfaces of ~0.9 m, which corresponds to the fault vertical displacement. We concluded
that the profile was traced on the same fluvial surface across the fault.
- We projected the gravels elevation at sample location 23-07 to sample location 23-01, to see if the
elevation relates to the one of the terrace sampled at 23-01. To do that, we determined the current river
gradient (of 0.011). We then measured the distance between 23-07 and a point that projects from
location 23-01 following the river flow trend. We then calculated the projected elevation of the gravels
at that location and obtained 39.3 m.
- That elevation is very similar to the elevation of the gravels at location 23-01. We therefore conclude
that the terrace sampled at 23-01 and the terrace sampled at 23-07 correspond to the same surface,
which we refer to as T7.

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Figure C.7: Maps illustrating the steps for the correlation of terrace T7 across the fault.
C.3. Definition of input probability density functions (PDFs) in RISeR
We used the RISeR code from Zinke et al. (2017, 2019a) to calculate allowable single-event
displacements between consecutive event, for the six scenarios described in the main text.
RISeR uses Markov chain Monte Carlo analyses that take into account the PDFs of the displacements
and their respective ages (in this case, the paleoearthquake ages). For a given scenario, and for each
displacement, the Monte Carlo sampling scheme picks a random age and displacement value according to
its likelihood for the respective PDFs of those measurements. If any sample values result in a negative (leftlateral) slip history, the sampled path is rejected and a new set of values is chosen. This is done iteratively
until the desired number of sample paths is reached. In this study, we have used 10,000 sample paths. We
chose that number to facilitate posterior analysis of the slip-per-event coefficients of variation, and reduce
the computing time. A higher number would not have changed the results of the analysis, but would have
taken much more time to complete. We therefore chose a number that is a good balance between precision

338
of the results (enough sample paths) and computing time (not too many sample paths). Here we describe
how we entered the PDFs of both displacements and ages in the RISeR code.
C.3.1. Definition of paleoearthquake age PDFs
The paleoearthquake ages from Morris et al. (2022) were recalculated using the OxCal code available
in their supplementary materials, using the OxCal software (Bronk Ramsey, 2017). We obtained PDFs for
all the four paleoearthquake ages E1 to E4 in a format giving the probability distribution according to time
in years C.E. We recalculated the time in years before present (present being fixed at year 2024).
The Kaikōura earthquake (E0) was given the date of November 2016, with minimal uncertainty of one
month, since RISeR requires non-zero uncertainties, and with a boxcar probability distribution centered
around the date of April 2024.
C.3.2. Definition of PDFs for the displacements
In RISeR, the definition of the displacements is made with cumulative offsets, listed in ascending order
from the smallest offset to the largest offset (equivalently, from youngest to oldest).
In the six scenarios presented in CHAPTER 4, we defined the PDFs of the known cumulative
displacements as a gaussian distribution for the 20 ± 4 m (2σ) and a triangular distribution for the 33+3/-4 m
(which refers to a 100% confidence interval). For the Kaikōura coseismic slip, we used a gaussian
distribution defined as displayed in the main text: 9.7± 0.3 m (2σ).
For the other intermediate displacements, we initially assumed that the minimal lateral slip that could
have been recorded in the paleoseismic trenches studied by Morris et al. (2022) is 1 m. We used trapezoidal
PDFs, taking into account the median value of the two adjacent offsets of the record, and the extreme values
of the two adjacent offsets of the record.

339
For instance, for Scenario C (Figure C.10), we defined the displacement of earthquake E1 as a
trapezoidal distribution, whose bounds are 10.55 m (which is the centered value of the E0 displacement
subtracted by 1σ of the E0 displacement, and added by 1 m) and 21.0 m (which is the maximal value of the
E3 displacement subtracted by 1 m), and whose middle values are 10.7 m (centered value of E0
displacement added by 1 m) and 19.0 (centered value of E2 displacement subtracted by 1 m). The choice
of the trapezoidal distribution allows for the most objective interpretation of allowable range of slips, with
a flat distribution between the preferred values.
The outputs for these simulations inevitably result in some very small displacements smaller than 1 m,
as shown on Figure 4.6 of the main text. The next figures illustrate the PDFs used for the six scenarios
described in the main text.

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Figure C.8: Probability density functions of cumulative displacements entered as inputs in RISeR code,
for Scenario A.

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Figure C.9: Probability density functions of cumulative displacements entered as inputs in RISeR code,
for Scenario B.

342
Figure C.10: Probability density functions of cumulative displacements entered as inputs in RISeR code,
for Scenario C.

343
Figure C.11: Probability density functions of cumulative displacements entered as inputs in RISeR code,
for Scenario D.

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Figure C.12: Probability density functions of cumulative displacements entered as inputs in RISeR code,
for Scenario E.

345
Figure C.13: Probability density functions of cumulative displacements entered as inputs in RISeR code,
for Scenario F.

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Appendix D. Supplements for CHAPTER 5
This appendix includes:
- Figure of restored 9-13 km offset of Clarence River
- Restorations of cumulative offsets (with minimum, preferred, maximum restorations – Figure D.1
to Figure D.12)
- Justification for the definition of the T3ML/T4ML riser based on observation of road cut disturbance
(Figure D.13)

347
Figure D.1: Large restoration offset of the Clarence River path along the Kekerengu fault. The offset is 9 to 13 km. Map background is from Google
Earth. Inset map shows a slope map of the Marlborough fault system and highlights the two “elbows” formed by the Clarence river (highlighted by
the white arrows). Active faults are indicated in black (Langridge et al., 2016). AM: Awatere Mountains; IKR: Inland Kaikoura Range; SKR:
Seaward Kaikoura Range; HH: Hundalee Hills. The main geomorphic features restored by this configuration are indicated by white arrows.

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Figure D.2: Restorations of Chaffey/Kulnine riser. Background maps include lidar and topographic map (contour lines every meter).

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Figure D.3: Restorations of Kulnine abandoned channel. Background maps include lidar and topographic map (contour lines every meter). The gray
area masks fault-related topography to aid visualization of the offset geomorphic features.

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Figure D.4: Restorations of Kulnine/Winterholme terrace riser. Background maps include lidar and topographic map (contour lines every meter).
The gray boxes mask fault-related topography to aid visualization of the offset geomorphic features.

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Figure D.5: Restorations of Winterholme (T1
BS
)/T3
BS
terrace riser. Background maps include lidar and topographic map (contour lines every meter).

352
Figure D.6: Restorations for the offset contact bedrock/T3SB
. Background maps include lidar, topographic
map (contour lines every 50 cm) and slope map highlighting the steep areas (i.e., risers), and smooth areas
(i.e., terrace treads).

353
Figure D.7: Restorations for T3SB/T4SB riser. Background maps include lidar, topographic map (contour
lines every 50 cm) and slope map highlighting the steep areas (i.e., risers), and smooth areas (i.e., terrace
treads). The black boxes mask fault-related topography to aid visualization of the offset geomorphic
features.

354
Figure D.8: Restorations for T4SB/T5SB
. Background maps include lidar, topographic map (contour lines
every 30 cm) and slope map highlighting the steep areas (i.e., risers), and smooth areas (i.e., terrace treads).

355
Figure D.9: Restorations for the Black Hut offset. Background maps include lidar, topographic map
(contour lines every 2 m) and slope map highlighting the steep areas (i.e., risers), and smooth areas (i.e.,
terrace treads).

356
Figure D.10: Restorations for the double offset at McLean Stream. Background maps include lidar,
topographic map (contour lines every meter) and slope map highlighting the steep areas (i.e., risers), and
smooth areas (i.e., terrace treads).

357
Figure D.11: Restorations for the T2ML/T3ML offset at McLean Stream. Background maps include lidar,
topographic map (contour lines every 50 cm) and slope map highlighting the steep areas (i.e., risers), and
smooth areas (i.e., terrace treads).

358
Figure D.12: Restorations for T3ML/T4ML and T4ML/T6ML offset at McLean Stream. Background maps
include lidar, topographic map (contour lines every 50 cm) and slope map highlighting the steep areas (i.e.,
risers), and smooth areas (i.e., terrace treads).

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Figure D.13: Determination of the T3ML/T4ML riser displacement prior to the Kaikōura earthquake.
Geomorphic evidence based on air photos taken with Google Earth at different times: before the
construction of the dirt road that crosses the T3ML/T4ML riser near the fault trace, after the construction of
that road, and after the 2016 Kaikōura earthquake. This set of photos provides the evidence for an offset
that displaces both T3ML/T4ML riser and T4ML/T5
ML riser, prior the Kaikōura earthquake. It also shows that
the construction of the road did not change the aspect of the T3ML/T4ML riser directly north of the fault.

360
Appendix E. Supplements for CHAPTER 6
This appendix includes:
- Supplementary figures (E.1 to E.10) which entail:
o Restorations of lateral offsets, with minimum, preferred, maximum restorations and
different map backgrounds
o Photographs of sampling pits
- Methods for calculating vertical offsets
E.1. Offset restorations

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Figure E.1: Restorations of T1/T2 offset, including minimum, preferred and maximum versions.
Background maps include lidar, topographic map (contour lines every 10 cm) and interpreted landscape
with topography (contour lines every 50 cm). The gray areas mask fault-related topography to aid
visualization of the offset geomorphic features.

362
Figure E.2: Restorations of T2/T3 riser offset, including minimum, preferred and maximum versions. Background maps include lidar, topographic
map (contour lines every 10 cm) and interpreted landscape with topography (contour lines every 50 cm). The gray areas mask fault-related
topography to aid visualization of the offset geomorphic features.

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Figure E.3: Restorations of channel 1 offset, including minimum, preferred and maximum versions. Background maps include lidar, topographic
map (contour lines every 10 cm) and slope map highlighting the steep areas (i.e., risers), and smooth areas (i.e., terrace treads). The gray areas mask
fault-related topography to aid visualization of the offset geomorphic features.

364
Figure E.4: Restorations of T3/T4 riser offset, including minimum, preferred and maximum versions. Background maps include lidar, topographic
map (contour lines every 10 cm) and slope map highlighting the steep areas (i.e., risers), and smooth areas (i.e., terrace treads). We use a different
color scheme for the slope map than for Figure E.3, to better highlight the morphology of riser T3/T4. The gray areas mask fault-related topography
to aid visualization of the offset geomorphic features.

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Figure E.5: Restorations of T4/T8 riser offset, including minimum, preferred and maximum versions. Background maps include lidar, topographic
map (contour lines every 10 cm) and slope map highlighting the steep areas (i.e., risers), and smooth areas (i.e., terrace treads). The gray areas mask
fault-related topography to aid visualization of the offset geomorphic features.

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Figure E.6: Restorations of T8/T9 riser offset, including minimum, preferred and maximum versions, using
the upper edge of the riser as a marker. Background maps include lidar, topographic map (contour lines
every 10 cm), interpreted landscape with topography (contour lines every 50 cm), aspect map (which
enhances the various directions of the slopes derived from the DTM), and slope map. The black boxes mask
fault-related topography to aid visualization of the offset geomorphic features.

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Figure E.7: Restorations of Ch2 offset, including minimum, preferred and maximum versions. Background
maps include lidar, topographic map (contour lines every 10 cm), interpreted landscape with topography
(contour lines every 50 cm), aspect map (which enhances the various directions of the slopes derived from
the DTM), and Strahler order map (which highlights the location of drainages and helps determine the
preferential direction of water, and therefore the exact position of the deepest part of potential thalwegs,
such as for the characterization of Channels 1 and 2). The black boxes mask fault-related topography to aid
visualization of the offset geomorphic features.

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Figure E.8: Three elevation profiles traced on terrace T1 (Waiohine surface) used to obtain the vertical component of the offset whose age would
be defined by the abandonment age of T1. The red sections are used to determine the trend of the gradient north and south of the fault, to then
determine the range of vertical component of offset. North of the fault, the gradient of the paleo-floodplain does not take into account a channel
incised into T1. Profile SS’ is located 30 m away from the T1/T2 riser edge. Profile TT’ is located 20 m away from the T1/T2 riser edge. Profile
UU’ is located 10 m away from the T1/T2 riser edge. Averaging the three vertical measurements, we obtain 18.80 ± 0.10 m.

369
E.2. Method for measuring vertical displacements
For measuring the vertical displacements at Waiohine River, presented in section 6.5.2 of CHAPTER 6, we
followed the following steps:
C.3.1. We traced profiles across the fault with the topographic tool available in QGIS.
C.3.2. We exported the elevation values from QGIS.
C.3.3. We defined a northern section of the profile that is representative of the paleo-floodplain
gradient north of the fault, and a southern section of the profile that is representative of the paleofloodplain gradient south of the fault. These sections were highlighted in red in Figure 6.4 of the
main text, and displayed in Figure E.9 in a schematic explanation of the method.
C.3.4. We calculated the slope and y-intercept that define the two gradients on each side of the
fault for each gradient, including the 2σ uncertainties on both the slope and the y-intercept.
C.3.5. We calculated the distance between the two slopes at the x location on the southernmost
side of the northern section of the profile, and the distance between the two slopes at the x location
on the northernmost side of the southern section of the profile.
C.3.6. The latter two distances are reported in Figure 6.4, along with their 2σ uncertainties.
C.3.7. We calculated the average of both distances (d1 and d2, in Figure E.9) and used it as the
final vertical offset of the feature of interest.
We note that for the estimate of distances d1 and d2 for profiles GG’ and HH’, we obtained 2σ uncertainties
that were larger than the mean itself. We chose to ignore the values of the 2σ range that would end up giving
a negative value for the distances. We applied a 2σ value that is equal to the mean itself, to circumvent that
problem.

370
Figure E.9: Schematic explanation of calculation of vertical displacements
E.3. Field photographs

371
Figure E.10: Photographs of sample pits and corresponding sample numbers.

372
Appendix F. Supplements for CHAPTER 7
This appendix includes:
- Supplementary figures for the Glen Eden study site on the northern Elsinore fault (Figure F.1 to
Figure F.5), which entail:
o Uninterpreted Digital Elevation Model of Glen Eden site
o Restorations of the two offsets (with minimum, preferred, maximum restorations)
- Photographs of sampling locations at the Glen Eden site

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Figure F.1: Air photographs of Glen Eden taken at three different periods (1953, 1967, 1981), highlighting
the progressive land development.

374
Figure F.2: Digital elevation models (DTM) from 2019 lidar data (Anon, 2019) and from stereoscopic
analysis of 1981 air photos.

375
Figure F.3: Offset restorations for terrace riser Qf5/Qf3.

376
Figure F.4: Offset restorations for terrace riser Qf10+/Qf5.

377
Figure F.5: Photographs of each sampling location at the Glen Eden site, with location of each IRSL
sample.

378
Appendix G. Supplements for CHAPTER 8
G.1. Explored parameter spaces
As explained in CHAPTER 8, we explore two parameter spaces {Ru; μ0} and {Ru; Rb}. Figure G.1
illustrates the parameter spaces and the fixed parameters for each of the spaces.
Figure G.1: Illustration of the explored parameter spaces, in a 3D diagram.
G.2. Slip per event and slip rate at Hokuri Creek
This record is based on the observation of the 16-m-thick, well-preserved cyclic stratigraphy at the
Hokuri Creek site, which has been documented to show relatively regular earthquake recurrence through
time (Berryman et al., 2012b). The alternating deposition of peat and silt is explained by the recurrence of
surface-rupturing earthquakes: Layers of silts are evidence for shallow-water sedimentation occurring
shortly after an earthquake, as the mountain-side-up fault scarp dams the creek, whereas each layer of peat
records the time when the creek had reestablished normal drainage across the fault trace.

379
The use of 7.5 m of average slip per event is supported by several pieces of information. The HC record
has a total of 24 earthquakes, named Ha1, Ha2, and Hk1 to Hk22 (Figure 8.2). The 22 ground-rupturing
events Hk1 to Hk22 were recognized directly at Hokuri Creek and have occurred when the site drained
across the fault through a now-abandoned gorge (Clark et al., 2013). The two most recent events are inferred
from other nearby sites to the north of Hokuri Creek (Haast and Okuru River sites) (Berryman et al., 2012a),
where total slip for those events is so large that it suggests both events ruptured a broad area of the
southwestern Alpine fault, down to Hokuri Creek. In addition, the most recent event (Ha1) occurred in 1717
C.E. and is recognized by coseismic displacement in the spillway area at Hokuri Creek (Berryman et al.,
2012a; Biasi et al., 2015; De Pascale and Langridge, 2012).
The magnitudes of all the other recorded events are assumed to be on the same order of this most recent
(Mw = 8.3) and ante-penultimate (Mw = 7.6) Alpine fault earthquakes (Sutherland et al., 2007). Additionally,
the horizontal displacement in the most recent event was recognized at Hokuri Creek on the basis of five
channel landforms that were offset dextrally by 7.5 ± 0.5 m, with 1.0 ± 0.5 m vertical component. Although
earthquake magnitude cannot directly be measured for each of the 22 other events recognized at the Hokuri
Creek site, we assume similar large horizontal single-event displacements of 7.5 m based on the consistent
vertical displacements observed across the fault scarp at Hokuri Creek and from evidence of characteristic
displacement profile through the late Holocene on the Alpine fault (De Pascale et al., 2014). The
combination of 24 events, each of them assumed to have laterally displaced 7.5 m on average, with the age
of the oldest recognized earthquake (ca. 7.9 ka) agrees with the long-term slip-rate estimate of ~23 mm/yr
in the southwestern section of the Alpine fault (Sutherland et al., 2006).
The most-recent event at Hokuri Creek has displaced three abandoned channels that are described as
being probably part of the floodplain of the former Hokuri Creek (Berryman et al., 2012a). Two edges of
the former channel of Hokuri Creek are also dextrally displaced by 7.5 ± 0.5 m. In our study, we ignore the
vertical component of each displacement for computation purposes; however, the vertical displacement of

380
the MRE is estimated to be 1.0 ± 0.5 m for the same channels where the dextral displacement was recorded.
The lateral displacement of older events cannot be recognized at Hokuri Creek. However, Berryman et al.
(2012b) have identified the displacement of the three most-recent events at nearby sites, located to the north
of Hokuri Creek (Haast, Okuru and Turnbull rivers - see Figure 8.1). They measure a cumulative
displacement of 25 ± 3 m for the past three events. Specifically, they show that a 9.3 m lateral displacement
occurred at Haast during the last earthquake, and that the third recognized event may have involved a similar
displacement of 9 m. At the Okuru river, much closer to the Hokuri Creek site, they recognized three offsets
that they relate to the three most-recent events: a lateral displacement of 7.5 m in the most-recent (1717
C.E.) earthquake, a cumulative displacement of 16 m that records slip in the last two earthquakes, and
another cumulative displacement of 23 m that records the last three earthquakes. We assume that this site
is more representative of what occurs at Hokuri Creek, and that a 7.5 m of slip per event is supported by
the data found at the Okuru River.
The average slip rate we use to characterize the slip behavior of the Alpine fault at Hokuri Creek is 23
mm/yr. This slip rate value was documented by Sutherland et al. (2006), who studied offset glacial
landforms in the southwestern portion of the Alpine fault, and is supported by the study of Cooper and
Norris (1995) who reported on the age and displacement of fluvial terraces on the north side of the Haast
River and obtained a minimum dextral slip rate of 23.5 mm/yr for the past 4 ky. Finally, combining the
earthquake ages obtained at Hokuri Creek (Berryman et al., 2012a) with an average slip per event of 7.5 m
yields a slip rate value of 23 mm/yr.
G.3. Earthquake recurrence patterns for different effective normal stress values
In the main text, we focus on the results obtained for ̅ = 13 MPa, at a point referring Hokuri Creek in
the architecture of the model. We explored other values of ̅, i.e., 14 MPa and 16.4 MPa, illustrated
hereafter. The differing ̅ values, i.e., 16.4, 14 and 13 MPa, have minimal effects on the distribution of

381
periodicity in the {Ru; μ0} parameter space. Clusters of type of periodicity, however, seem to be
recognizable for the three different values of effective normal stress.
Figure G.2: Overview of the periodicity styles obtained for different values of ̅ at the sampling location
(HC), and on the entire fault (left side). The first two rows show graphs of ̅=13 MPa presented in the main
text for HC, with the first row presenting the results for the parameter space {Ru; Rb} with a fixed value of
μ0=0.50, and the second presenting the results for the parameter space {Ru; μ0} with fixed value of
Rb=0.286. The two last rows show Ru versus μ0 at ̅=14 MPa and 16.4 MPa. This figure emphasizes the
importance of sampling of the fault and the various periodicity style of recurrence patterns according to
different values of the non-dimensional parameters.
We defined the best ̅ value in order to explore the other set of parameter space {Ru; Rb} by looking at
the mean recurrence intervals obtained for complex behaviors (period-n, with n>2) for ̅ = 16.4 and 14
MPa. These were mostly too long and we thus fixed ̅=13 MPa to both show our results and to more
effectively explore the {Ru; Rb} parameter space.

382
Figure G.3: Bifurcation diagrams showing periodicity of earthquake recurrence for all the simulations we
ran for ̅=13 MPa, as presented in Figure 3 in the main text. We show the different values of recurrence
time intervals obtained at each value of Ru numbers with fixed Rb=0.286 and varying μ0 (from 0.30 to 0.60
with 0.05 increments) in the first two rows, and fixed μ0=0.50 and varying Rb (from 0.35 to 0.70 with 0.05
increments) in the two bottom rows. The size of the markers refers to the relative size of the events, using
the seismic moment.
As an additional way of looking at these data, we plot bifurcation diagrams for simulations at ̅=13
MPa (Figure G.2) for all the {Ru; μ0} and {Ru; Rb} parameter spaces. This figure emphasizes the types of
periods obtained for each simulation. If we were to plot the periodicity considering both the recurrence time
intervals and the amount of slip (or equivalently, the size of each earthquake), we would obtain different
results. For instance, for Ru=0.25, μ0=0.50 and Rb=0.286, the recurrence time interval shows a period-1
behavior, but considering the sizes of earthquakes, it would be characterized by a period-2 behavior.

383
G.4. Fitting earthquake simulations to the Hokuri Creek data
In this section, we detail the logic of the Python code we used to perform the calculation of fit or misfit
between the simulations and the Hokuri Creek data.
G.4.1. Trimming of the events
If entire simulation (20,000 years) contains less than 23 recurrence intervals, we assign an arbitrary
value to the measure of misfit between the simulation and the HC data (see paragraph G.4.3). We first
remove the first three events from the simulations to get rid of the direct initiation effects.
When it comes to a paleoseismic record, there are several issues that need to be taken into account. The
first issue involves the timing of earthquakes. If two or more successive surface-breaching earthquakes
occur within a brief amount of time, they will not be distinguishable if no expositional event occurs between
them (e.g., Williams, 2019). Accumulating decimeters of silts and peat at Hokuri Creek and transitioning
from deposition of a silt layer to a layer of peat necessitates some unknown amount of time. Assuming a
continuously flowing river at Hokuri Creek, we may assume that below the time scale of a few decades, the
deposition will not be sufficient enough to enable the distinction of two (or more) events that occurred
within a short time frame. In that regard, we choose to ignore the simulated events that occurred within less
than 20 years, as done by Biasi et al. (2015) for the same study site, and by others in similar studies (e.g.,
Biasi et al., 2002). The second issue relates to the amount of displacement that would be recorded in the
stratigraphy, and recognized as a single event. In this study, we infer an average horizontal displacement of
7.5 m for each event, which we infer would correspond to a vertical component of displacement of 1.5 m
in the stratigraphy at Hokuri Creek (Berryman et al., 2012a), according to the recorded slip of the MRE.
Using that ratio of the vertical to horizontal components of displacement, we assume that an event that
would lead to less than 15 cm of vertical displacement would not be recognized by paleoseismologists. This
converts to a threshold of 1 m of horizontal displacement, equivalent to a Mw 6.9 earthquake (Wells and

384
Coppersmith, 1994). We therefore remove the events that have horizontal displacements of less than 1 m
from the simulated records.
In other words, when we trim the events from the simulations according to what would be observed on
a paleoseismic site, we specifically follow those steps:
• If two events occur within a time interval less than 20 years:
o We remove the event that follows this time interval,
o We add the slip of this ignored event to the slip of the next event,
o We add the time interval between the preceding event to the ignored event to the time
interval between the ignored event and the following event.
• If horizontal slip is less than 1 m for a given event:
o We remove this event from the simulation,
o We add the slip of this ignored event to the slip of the next event,
o We add the time interval between the preceding event to the ignored event to the time
interval between the ignored event and the following event.
G.4.2. Browsing all possible intervals of 23 recurrence intervals
The Hokuri Creek record contains a total of 24 events, which are separated by 23 recurrence time
intervals. We can thus use all possible intervals that contain 23 recurrence time intervals within a 20,000-
year-long simulation. We analyze all possible intervals in a given simulation that contain 23 values of time
intervals (time since last event). The ranking of those intervals is done by using one of the three criteria,
developed in the next paragraph. The ranked intervals are stored in the form of a list; the best interval is
then selected, and we know which interval it refers to (e.g., interval 0 is the first possible interval of 23
recurrence times of a given simulation; interval 1 starts with the second event of the whole simulation and
finishes with the 23rd event). Once an interval has been selected for each simulation, we rank the simulations

385
themselves. We store the ranked simulations in the form of a list and the ranking is displayed according to
the ranking criterion that is used.
G.4.3. Ways of ranking
We used three ways of ranking both the possible intervals contained within one simulation and the
selected intervals of all simulations. Only point (3) was used in our final results presented in the main
manuscript, since it appears to us to be the most meaningful criterion for selecting the best model.
(1) The Root Mean Square Error (RMSE) weighted by the standard deviation of the Hokuri Creek data
= √
1

∑
(
−

)
2

2

Where N is the number of recurrence time intervals (N=23), i refers to the index of each recurrence time
interval Tr. Tr data refers to the mean recurrence time intervals for the Hokuri Creek record whereas Tr simu
refers to the recurrence time intervals for the simulation. σ
i
centered
2
is the standard deviation of the Hokuri
Creek recurrence time intervals recentered to enable the RMSE computation. Weighting the mean squared
errors by the relevant standard deviation allows to account for the range of uncertainty: the smaller the
standard deviation, the larger the error if the points are far from each other, while the larger the standard
deviation, the more it allows the simulation point to be far from the actual mean value of the data point.
In the case where the entire simulation contains fewer than 23 recurrence intervals, we assign an arbitrary
value of 6.5 to the RMSE. We fixed this value based on observations that all RMSE misfits for any other
simulation were systematically below 6 (see Figure G.4, Figure G.5).
(2) The number of simulation points that fall within 95 % confidence intervals

386
For each possible interval of a simulation, we analyze each recurrence time interval and verify whether
it lies within the related to 95 % confidence interval of the HC data point. When this condition is verified,
we add one to the number that stores this information. We then select the best interval by maximizing this
number. In the case where the entire simulation contains fewer than 23 recurrence intervals, it is still
possible to count the number of simulation points that fall within the 95% confidence interval of the HC
data point, which we do. Obviously, the shorter the simulated record, the less likely it is to have a high
number of data points that fall within the 95 % confidence intervals.
(3) The number of consecutive points that fall within 95 % confidence intervals
For each possible interval of a simulation, we analyze each recurrence time interval and count all the
possible consecutive points that lie within the related 95% confidence interval of the HC data point. We
retain the maximum number for each 23-recurrence-time sequence and select the best interval by
maximizing this number. In the case where the entire simulation contains fewer than 23 recurrence intervals,
it is still possible to count the number of consecutive simulation points that fall within the 95% confidence
interval of the HC data point, which we do.
G.5. Ranking simulations
Overall, the right-hand sections of the colored matrices on Figure G.4 and Figure G.5 seem to provide
the best fits to the HC data, i.e., for Ru numbers above ~50.

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Figure G.4: Results of fitting of best sequences of 23 time recurrence intervals to the Hokuri Creek
paleoseismic data, for ̅=13 MPa, at the sampling location (HC), for different values of Ru and μ0, at fixed
Rb=0.286. The recurrence interval time sequences obtained in each simulation are filtered to suit
paleoseismic hypotheses explained above. Best 23-event sequences of each filtered simulation are selected
based on three different ways of ranking the simulations, as explained above. For (a), (d) and (g), we use
color gradients that refer to the value of the RMSE, the number of events within 95% confidence intervals,
and the number of successive events that fall within 95% confidence intervals, respectively. (b) and (c)
show the results in light of the HC data for one of the best fitting simulations according to the first way of
ranking the results (RMSE) for μ0 versus Ru. The 21
st interval of the simulation for {Ru=60, μ0=0.60} gives
one of the lowest possible RMSE, a total of seventeen events within the 95% confidence interval, and a
maximum of four events in a row that fall within the 95% confidence intervals. The earthquake recurrence
behavior is period-11. Insets (e) and (f) present the results in light of the HC data for the best fitting
simulation using the second way of ranking the results. The maximum number of events that fall within the
95% confidence intervals is 19 and is reached for {Ru=95, μ0=0.50}, one of our two best fitting models (see
CHAPTER 8), which refers to a chaotic behavior. Insets (h) and (i) show the results for one of the best
fitting simulations using the third way of ranking, for simulation {Ru=80, μ0=0.50}, characterized by a
period-2 recurrence behavior.

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Figure G.5: Results of fitting of best sequences of 23 recurrence intervals to the Hokuri Creek paleoseismic
data, for ̅=13 MPa, at sampling point HC, for varying values of Rb at fixed μ0=0.50. For (a), (d) and (g),
we use color gradients that refer to the value of the RMSE, the number of events within the 95% confidence
intervals, and the number of successive events that fall within the 95% confidence intervals, respectively.
Insets (b) and (c) show the best result based on the ranking criterion of the lowest RMSE for {Ru=70,
Rb=0.35}. The earthquake recurrence behavior is period-22. Sixteen events in total and eight successive
recurrence time intervals fall within the 95% error bars of the data. Insets (e) and (f) show the second-best
result for the second way of ranking: for {Ru=50, Rb=0.50}, 18 recurrence times in total and 13 successive
recurrence times fall within the 95% confidence intervals. The earthquake recurrence behavior is period-2
in this case. The best result for this ranking criterion is already shown in Figure G.4. Insets (h) and (i) show
one good result for the third way of ranking, for which 11 successive recurrence times and 15 recurrence
times in total fall within the 95% confidence intervals. The earthquake recurrence behavior is chaotic in
this case.
Figure G.5 illustrates the method used to rank all the models in the different parameter spaces for ̅ =
13 MPa. The graphs on the right-hand side are shown as examples, to illustrate the periodicity of some of
the models that rank best according to our three ranking methods. Figure G.4 highlights that the model
{Ru=95, μ0=0.50, Rb=0.286}, one of the two best models referred to in the main text, also bears a sequence

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of events that contains 19 recurrence intervals in total out of 23 that fit the HC data within the 95%
confidence intervals.
G.6. Recurrence time intervals, average slip per event and CoV for selected best
fits
Figure G.6: Colored matrices showing average slip per event in parameter space {Ru, μ0} of the best-fitting
23-time recurrence intervals for the three ranking methods, at the location representing Hokuri Creek. (a)
is for the best sequences using RMSE, (b) is for best sequences using number of events within the 95%
confidence intervals, and (c) is for best sequences using maximum number of successive events within the
95% confidence intervals. Black stars refer to average values of slip corresponding to 7.5 ± 2.5 m (arbitrary
uncertainty used in Figure 8.1 to build the slip deficit plot of the Hokuri Creek record).

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Figure G.7: Colored matrices showing average earthquake repeat time in parameter space {Ru, μ0} of the
best-fitting 23-time recurrence intervals for the three ranking methods, at the location representing Hokuri
Creek. (a) is for the best sequences using RMSE, (b) is for best sequences using number of events within
the 95% confidence intervals, and (c) is for best sequences using maximum number of successive events
within the 95% confidence intervals. Black stars refer to average values of recurrence time interval between
261 and 397 years (the 1σ interval of the HC mean recurrence interval, according to (Berryman et al.,
2012b).

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Figure G.8: Colored matrices showing CoV of best-fitting 23-time recurrence intervals in parameter space
{Ru, μ0} for the three ranking methods, at the location representing Hokuri Creek. (a) is for the best
sequences using RMSE, (b) is for best sequences using number of events within the 95% confidence
intervals, and (c) is for best sequences using maximum number of successive events within the 95%
confidence intervals. Black stars refer to values of CoV between 0.2 and 0.4.
On Figure G.9 and Figure G.10, the energy trade-off between the Rb and Ru numbers, as discussed in
the main text, is clearly identifiable.

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Figure G.9: Colored matrices showing average slip per event in parameter space {Ru, Rb} of the best-fitting
23-time recurrence intervals for the three ranking methods, at the location representing Hokuri Creek. (a)
is for the best sequences using RMSE, (b) is for best sequences using number of events within the 95%
confidence intervals, and (c) is for best sequences using maximum number of successive events within the
95% confidence intervals. Black stars refer to average values of slip corresponding to 7.5 ± 2.5 m.

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Figure G.10: Colored matrices showing average earthquake repeat time in parameter space {Ru, Rb} of the
best-fitting 23-time recurrence intervals for the three ranking methods, at the location representing Hokuri
Creek. (a) is for the best sequences using RMSE, (b) is for best sequences using number of events within
the 95% confidence intervals, and (c) is for best sequences using maximum number of successive events
within the 95% confidence intervals. Black stars refer to average values of recurrence time interval between
261 and 397 years.

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Figure G.11: Colored matrices showing CoV of best-fitting 23-time recurrence intervals in parameter space
{Ru, Rb} for the for the three ranking methods, at the location representing Hokuri Creek. (a) is for the best
sequences using RMSE, (b) is for best sequences using number of events within the 95% confidence
intervals, and (c) is for best sequences using maximum number of successive events within the 95%
confidence intervals. Black tars refer to values of CoV between 0.2 and 0.4.
G.7. Seismic simulations for best obtained results
The analogous plot of Figure 8.2 is displayed here (Figure G.12) for the period-5 model {Ru=90,
Rb=0.286, μ0=0.60}. It shows full, unilateral ruptures that break the entire fault as well as partial ruptures,
that break less than 100 km. The evolution of the slip velocity for the best chaotic model fit found at the
sampling point representing Hokuri Creek, for {Ru=95, Rb=0.286, μ0=0.50}, is displayed in Figure G.13. It
shows full, unilateral ruptures as well as partial ruptures.

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Figure G.12: Representation of the Alpine fault zone model with reference map in inset (a) and plot of the
cumulative for the following parameters: ̅=13 MPa, Ru=90, Rb=0.286 and μ0=0.60, in inset (b). The
cumulative slip is plotted for a total of 25 events in this period-5 simulation, between years 1,689 and 3,866
(within a whole 20,000-year-long simulation). Location of Hokuri Creek (HC) is shown as the black vertical
line. The orange isochrons feature cumulative coseismic slip every 20 seconds. The gray contours show
slip isochrons every 10 years.

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Figure G.13: Seismic cycle simulation (20,000 years) represented by the slip velocity for the following
parameters: ̅=13 MPa, Ru=95, Rb=0.286 and μ0=0.50. The x- and y-axes represent distance along the fault
and adaptive time steps, respectively. The area between the blue dashed lines is the velocity-weakening
domain. The white vertical line refers to the proxy of Hokuri Creek's location. This highlights two kinds of
rupture styles: full, unilateral ruptures (break the entire fault) and partial ruptures (break less than ~100
km).

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Similarly, the evolution of the slip velocity for the period-5 model fit found at the sampling point
representing Hokuri Creek, for {Ru=90, Rb=0.286, μ0=0.60}, is displayed in Figure G.14. It shows full,
unilateral ruptures as well as partial ruptures. It also illustrates a result displayed in Figure G.2: the
earthquake repeat time behavior at the sampling point (Hokuri Creek location) is not necessarily equivalent
to the overall earthquake recurrence behavior of the entire fault.
The related distributions of seismic moments for the two best simulations are displayed in Figure G.15.
They display the seismic moments observed on the entire fault, for the entire 20,000-year simulations.

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Figure G.14: Seismic cycle simulation (20,000 years) represented by the slip velocity for the following
parameters: ̅=13 MPa, Ru=90, Rb=0.286 and μ0=0.60. The x- and y-axes represent distance along the fault
and adaptive time steps, respectively. The area between the blue dashed lines is the velocity-weakening
domain. The white vertical line refers to the proxy of Hokuri Creek's location. This highlights two kinds of
rupture styles: full, unilateral ruptures (break the entire fault) and partial ruptures (break less than ~100
km).

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Figure G.15: Cumulative frequency distribution (Nc) of seismic moments for the simulations using
following parameters: (a) ̅=13 MPa, Ru=90, Rb=0.286 and μ0=0.60, and (b) ̅=13 MPa, Ru=95, Rb=0.286
and μ0=0.50.

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Appendix H. How plate-like are the Nazca and the Pacific plates at the 10,000-
year time scale? Preliminary steps towards characterization of variable midocean ridge spreading rates.
H.1. Introduction
The purpose of this study is to reveal how “plate-like” the Nazca and the Pacific plates are, and how
the spacing between abyssal hills is an indicator for the plate-like behavior of these plates. A truly platelike behavior could be viewed as a double conveyor-belt that is moving through time, extending apart from
the ridge axis, at which the new oceanic crust is created.
Abyssal hills are geomorphic features i