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Characterizing fault behavior and earthquake surface expression on timescales of single events to multiple millennia

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Characterizing fault behavior and earthquake surface expression on timescales of single events to multiple millennia

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Content i

CHARACTERIZING FAULT BEHAVIOR AND EARTHQUAKE SURFACE EXPRESSION
ON TIMESCALES OF SINGLE EVENTS TO MULTIPLE MILLENNIA

By
Robert Wayne Zinke

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)

December 2018

ii

iii

ACKNOWLEDGEMENTS
There are many people I would like to thank for their help and support. First and foremost,
I thank my parents, Paul and Jeanie, for their endless support, wisdom, and encouragement; my
sister, Laura, who endured the doctoral process alongside me; and my brother, Mathew, for
reminding everyone how to enjoy life properly.
I give enormous thanks those who helped me academically, both professionally and
personally. A huge thank you to my advisor, James Dolan, with whom I have enjoyed working,
and who taught me to speak up and dream big. Many thanks to Russ Van Dissen, Ed Rhodes,
James Hollingsworth, Alex Hatem, Chris Milliner, and Kenny Befus. All of these incredible
scientists have contributed to the work I present in this thesis, or helped me greatly in my career.
Furthermore, I would like to thank the members of my qualifying and dissertation committees
whom I have not yet thanked: Charlie Sammis, Josh West, and Steve Nutt.
Special thanks to my friends in the 2013 cohort and throughout the department. Without
them, L.A. would have seemed a very dull place. And thanks to Dylan and Jayme for years of
support. I also thank the USC Earth Science Department office staff – Cindy, John McRaney, John
Yu, Karen, Vardui, and Barbara – for keeping everything running smoothly, even when I did not
make it easy on them.
This dissertation includes parts of the following manuscripts:
1) Zinke, R., Hollingsworth, J., and Dolan, J. F., 2014, Surface slip and off-fault
deformation patterns in the 2013 MW 7.7 Balochistan, Pakistan earthquake: Implications
for controls on the distribution of near-surface coseismic slip: Geochemistry, Geophysics,
Geosystems, v. 15, p. 5034–5050, doi:10.1002/2014GC005538.
2) Zinke, R., Dolan, J. F., Van Dissen, R., Grenader, J. R., Rhodes, E. J., McGuire, C. P.,
Langridge, R. M., Nicol, A., and Hatem, A. E., 2015, Evolution and progressive
geomorphic manifestation of surface faulting: A comparison of the Wairau and Awatere
faults, South Island, New Zealand: Geology, v. 43, p. 1019–1022, doi:10.1130/G37065.1.
iv

3) Zinke, R., Dolan, J. F., Van Dissen, R., Grenader, J. R., Rhodes, E. J., McGuire, C. P.,
Langridge, R. M., Nicol, A., and Hatem, A. E., 2016, Evolution and progressive
geomorphic manifestation of surface faulting: A comparison of the Wairau and Awatere
faults, South Island, New Zealand: Reply: Geology, v. 44, p. e392–e393,
doi:10.1130/G38188Y.1.
4) Zinke, R., Dolan, J. F., Rhodes, E. J., Van Dissen, R., and McGuire, C. P., 2017, Highly
variable latest Pleistocene–Holocene incremental slip rates on the Awatere fault at Saxton
River, South Island, New Zealand, revealed by lidar mapping and luminescence dating:
Geophysical Research Letters, v. 44, doi:10.1002/2017GL075048.
5) Zinke, R., Hollingsworth, J., Dolan, J. F., and Van Dissen, R., in review, 3D surface
deformation in the 2016 MW 7.8 Kaikōura, New Zealand earthquake from optical image
correlation: Implications for strain localization and long-term evolution of the Pacific-
Australian plate boundary: Geochemistry, Geophysics, Geosystems, manuscript
2018GC007951.
6) Zinke, R., Dolan, J. F., Rhodes, E. J., Van Dissen, R., McGuire, C. P., Hatem, A. E., and
Brown, N. D., in review, Multi-millennial incremental slip rate variability of the Clarence
fault at the Tophouse Road site, Marlborough fault system, New Zealand: Geophysical
Research Letters, manuscript 2018GL080688.

v

TABLE OF CONTENTS
ACKNOWLEDGEMENTS ........................................................................................................... iii
ABSTRACT ................................................................................................................................... ix
CHAPTER 1: Introduction ............................................................................................................. 1
1.1 Introduction ........................................................................................................................... 1
1.2 Quantifying Earthquake Surface Expression ........................................................................ 3
1.3 Characterizing Trends in Incremental Fault Slip Rate Behavior .......................................... 6
CHAPTER 2: Surface slip and off-fault deformation patterns in the 2013 MW 7.7 Balochistan,
Pakistan earthquake: Implications for controls on the distribution of near-surface coseismic slip 8
2.1 Abstract ................................................................................................................................. 8
2.2 Introduction ........................................................................................................................... 9
2.3 Background ......................................................................................................................... 10
2.4 Methods ............................................................................................................................... 11
2.4.1 Optical Image Correlation ............................................................................................ 11
2.4.2 Mapping the Surface Rupture Trace and Width of Deformation Using High-Resolution
Satellite Imagery .................................................................................................................... 13
2.4.3 Measuring Offsets......................................................................................................... 14
2.5 Results ................................................................................................................................. 17
2.5.1 COSI-Corr Results ........................................................................................................ 17
2.5.2 Surface Rupture Displacements.................................................................................... 18
2.5.3 Structural Style and Width of Deformation Zone ........................................................ 19
2.6 Discussion ........................................................................................................................... 20
2.6.1 Off-Fault Deformation of the 2013 Rupture ................................................................. 20
2.6.2 Surface Material Control on Strain Localization .......................................................... 25
2.6.3 Implications for the Use of Fault Slip Rate Studies Based on Geomorphic Offsets .... 30
2.6.4 Implications for Seismic Hazard .................................................................................. 31
2.7 Conclusions ......................................................................................................................... 33
2.8 Figure Captions ................................................................................................................... 34
2.8.1 Data Repository Figure Captions ................................................................................. 38
CHAPTER 3: 3D surface deformation in the 2016 MW 7.8 Kaikōura, New Zealand earthquake
from optical image correlation: Implications for strain localization and long-term evolution of the
Pacific-Australian plate boundary................................................................................................. 40
3.1 Abstract ............................................................................................................................... 40
3.2 Introduction ......................................................................................................................... 41
vi

3.2.1 Tectonic Setting ............................................................................................................ 43
3.3 Methods and Observations .................................................................................................. 44
3.3.1 Determinations of 3D Deformation from Optical Image Correlation .......................... 44
3.3.2 Fault Trace Mapping .................................................................................................... 48
3.3.3 Identification and Mapping of the Snowflake Spur Fault ............................................ 50
3.3.4 Measuring Fault Offsets and Fault Zone Widths.......................................................... 50
3.3.5 Offset Measurement and Fault Zone Width Analysis .................................................. 52
3.3.6 Kinematic Analysis....................................................................................................... 53
3.4 Discussion and Implications................................................................................................ 54
3.4.1 Consistency of Rupture Patterns with Long-term Kinematics: Observations .............. 54
3.4.2 Consistency of Rupture Patterns with Long-term Kinematics: Implications ............... 58
3.4.3 The Snowflake Spur Fault and Controls on Rupture Propagation ............................... 60
3.4.4 Rupture Propagation Between Tectonic Domains ........................................................ 61
3.4.5 Implications for the Evolution of the Pacific-Australian Plate Boundary in Northern
South Island, New Zealand .................................................................................................... 63
3.4.6 Off-Fault Deformation: Analysis.................................................................................. 64
3.4.7 Off-Fault Deformation: Implications............................................................................ 68
3.5 Conclusions ......................................................................................................................... 73
3.6 Figure Captions ................................................................................................................... 74
3.6.1 Captions for Supplementary Materials ......................................................................... 76
CHAPTER 4: Evolution and progressive geomorphic manifestation of surface faulting: A
comparison of the Wairau and Awatere faults, South Island, New Zealand ................................ 78
4.1 Abstract ............................................................................................................................... 78
4.2 Introduction ......................................................................................................................... 79
4.3 Comparison of Surface Deformation Patterns Along Faults with Different Structural
Maturities .................................................................................................................................. 80
4.4 Progressive Geomorphic Manifestation of OFD with Increasing Fault Slip ...................... 83
4.5 Further Evidence for the Structural Evolution and Progressive Geomorphic Manifestation
of OFD at the Branch River and Saxton River sites ................................................................. 87
4.6 Figure Captions ................................................................................................................... 92
4.6.1 Captions for Supplementary Materials ......................................................................... 93
CHAPTER 5: Highly variable latest Pleistocene–Holocene incremental slip rates on the Awatere
fault at Saxton River, South Island, New Zealand, revealed by lidar mapping and luminescence
dating............................................................................................................................................. 97
5.1 Abstract ............................................................................................................................... 97
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5.2 Introduction ......................................................................................................................... 98
5.3 The Saxton River Site ......................................................................................................... 98
5.4 Offset Determinations ......................................................................................................... 99
5.5 Age Determinations........................................................................................................... 101
5.6 Slip Rate Determinations .................................................................................................. 102
5.7 Discussion and Conclusions .............................................................................................. 105
5.8 Figure Captions ................................................................................................................. 109
5.8.1 Captions for Supplementary Materials ....................................................................... 110
CHAPTER 6: Multi-millennial incremental slip rate variability of the Clarence fault at the
Tophouse Road site, Marlborough fault system, New Zealand .................................................. 123
6.1 Abstract ............................................................................................................................. 123
6.2 Introduction ....................................................................................................................... 124
6.3 The Tophouse Road site, Clarence fault ........................................................................... 125
6.4 Offset Measurements......................................................................................................... 126
6.5 Age Determinations........................................................................................................... 128
6.6 Slip Rate Determinations .................................................................................................. 129
6.7 Discussion and Conclusions .............................................................................................. 131
6.8 Figure Captions ................................................................................................................. 135
6.8.1 Captions for Supplementary Materials ....................................................................... 136
CHAPTER 7: Conclusions ......................................................................................................... 139
References ................................................................................................................................... 141
Chapter 2 Figures ........................................................................................................................ 161
Chapter 3 Figures ........................................................................................................................ 171
Chapter 4 Figures ........................................................................................................................ 176
Chapter 5 Figures ........................................................................................................................ 180
Chapter 6 Figures ........................................................................................................................ 184
Appendices .................................................................................................................................. 188
Appendix A: Surface slip and off-fault deformation patterns in the 2013 MW 7.7 Balochistan,
Pakistan earthquake: Implications for controls on the distribution of near-surface coseismic
slip ........................................................................................................................................... 188
Appendix B: 3D surface deformation in the 2016 MW 7.8 Kaikōura, New Zealand earthquake
from optical image correlation: Implications for strain localization and long-term evolution of
the Pacific-Australian plate boundary ..................................................................................... 207
Appendix C: Evolution and progressive geomorphic manifestation of surface faulting: A
comparison of the Wairau and Awatere faults, South Island, New Zealand .......................... 309
viii

Appendix D: Highly variable latest Pleistocene–Holocene incremental slip rates on the
Awatere fault at Saxton River, South Island, New Zealand, revealed by lidar mapping and
luminescence dating ................................................................................................................ 320
Appendix E: Multi-millennial incremental slip rate variability of the Clarence fault at the
Tophouse Road site, Marlborough fault system, New Zealand .............................................. 363

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ABSTRACT
Observations of surface fault slip over multiple spatial and temporal scales are critical for
understanding the processes governing plate boundary behavior and earthquake occurrence. In this
thesis, I document spatial patterns of earthquake surface deformation using geologic and geodetic
methods, and temporal patterns of fault slip over Holocene and latest Pleistocene time averaged
timescales of 10
2–4
years. I discuss observations from single-earthquake ground deformation
patterns in light of the insights they provide into fault mechanics and the processes controlling
rupture propagation. I then use observations of distributed surface strain in recent earthquake
ruptures to inform interpretations of prehistoric earthquake activity preserved in the landscape.
Finally, I analyze records of multiple slip-versus-time markers preserved in the landscape to
determine longer-term spatiotemporal patterns of fault behavior (incremental slip rates) to
investigate potential coordinated behavior between faults in a mechanically linked fault network.
This thesis comprises five studies examining the surface characteristics of earthquake
deformation and long-term fault behavior. In Chapters 2 and 3, I analyze the spatial patterns of
surface deformation resulting from two large fault ruptures: (1) the 2013 moment magnitude (MW)
7.7 Balochistan, Pakistan earthquake; and (2) the 2016 MW 7.8 Kaikōura, New Zealand
earthquake. In each study, I use optical satellite imagery acquired before and after each earthquake
to generate correlation maps of surface deformation. Using these maps, and visual observations
from the raw satellite imagery, I quantify the along-fault distributions of fault displacement (in 2D
for the Balochistan earthquake, and 3D for the Kaikōura earthquake). I then compare sets of short-
aperture measurements of fault-parallel slip (gathered from direct measurement of the satellite
imagery or published field data), which capture the discrete, on-fault component of slip, to image
correlation-derived measurements of total surface strain, projected from the far-field, which
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capture the total surface deformation result from the rupture. The difference between these two
data sets (termed off-fault deformation, or OFD) constitutes the amount of total earthquake slip
that was unaccounted for by the short-aperture “field” measurements. I find that the percent OFD
along the Hoshab fault resulting from the 2013 Balochistan earthquake was ~45% on average, and
was notably higher where the fault passed through thick accumulations of sediment. Comparison
of published field measurements along the Kekerengu fault with our correlation-derived
measurements reveals that ~36% of surface displacement was accommodated as distributed off-
fault deformation when considering only field measurements of discrete slip. Comparatively, field
measurements that project previously linear features (e.g., fence lines) into the fault over apertures
> 5–100 m capture nearly all (~90%) of the surface deformation.
In Chapter 4, I examine detailed patterns of distributed fault deformation preserved in the
microtopography at two well-known fluvial terrace flights: At Branch River on the Wairau fault;
and at Saxton River on the Awatere fault in northern South Island, New Zealand. From
observations at these sites, I draw two primary sets of conclusions: (1) The fault expression of
Wairau fault (which has accommodated > 150 km of slip) is geometrically simpler than that of the
Awatere fault (which has accommodated < 20 km of slip); and (2) distributed deformation
associated with the Awatere fault may not be discernable in sedimentary deposits until those
deposits have accumulated sufficient displacement for strain to localize.
In Chapters 5 and 6, I examine the temporal patterns of earthquake behavior in the past by
quantifying incremental slip rate variability of two mechanically linked faults during Holocene
and latest Pleistocene time. In Chapter 5, I use geomorphic mapping on high-resolution lidar
imagery and luminescence dating to reveal highly variable incremental Holocene-latest
Pleistocene slip rates at the well-known Saxton River site along the Awatere fault, a dextral strike-
xi

slip fault in the Marlborough fault system (MFS), northeastern South Island, New Zealand. These
analyses revealed factor of 10 variations in incremental slip rate spanning thousands of years and
meters of slip. In Chapter 6, I use similar methods to reveal factor of 4–5 variations in incremental
slip rate on the neighboring Clarence fault, hinting at potentially coordinated behavior of faults
within the MFS. These observations 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
1.1 Introduction
Spatiotemporal patterns of earthquake occurrence along plate boundary faults reflect the
complex interactions of numerous physical processes within the earth. Moreover, identification of
such patterns can potentially lead to more effective seismic hazard mitigation strategies. Geologic
and geodetic observations of surface deformation provide important means by which to quantify
patterns of strain release over time and space, which can in turn allow us to draw inferences about
the mechanisms controlling those patterns. For single earthquakes, field-based measurements of
coseismic slip along discrete fault planes provide insights into the spatial distribution and
kinematics of fault displacement. Such field measurements are often limited, however, by the
availability of previously linear cultural or geomorphic features and may not capture the full extent
of coseismic surface deformation (e.g., Shelef and Oskin, 2010; Dolan and Haravitch, 2014).
Analyses of remotely sensed data (e.g., correlation of pre- and post-event optical imagery) can
provide maps of spatially dense and continuous measurements of surface displacements over areas
spanning > 10
3
km
2
(e.g., Avouac and Leprince, 2015). Because the spatially continuous
correlation maps capture both on- and off-fault deformation, comparison of correlation-derived
fault offsets with short-aperture field measurements can reveal the amounts and spatial patterns of
distributed, off-fault deformation (OFD; Dolan and Haravitch, 2014; Zinke et al., 2014b; Milliner
et al., 2015, 2016b). With further study, improved constraints on expected surface deformation
patterns based on studies such as these could aid in the potential development of earthquake hazard
microzonation maps detailing the expected magnitude and width of OFD (e.g., Dolan and
Haravitch, 2014).
2

Furthermore, geologic and geomorphologic evidence of fault slip preserved in the
landscape and sediments near a fault are often the only means by which to quantify longer-term
patterns of strain release in the pre-instrumental era (before c. late 19
th
century). It is therefore
critically important to understand how earthquake slip is manifested at the surface over timescales
of many large earthquakes. Along some faults, natural circumstances have provided multiple
markers of progressive fault slip ranging in age from younger than a few hundred years, to older
than ten thousand years. By measuring the offset accrued by these markers, and by dating the time
elapsed since offset accrual began, one can reconstruct the displacement-time history (i.e.,
incremental slip rate history) of the fault at that location (e.g., Friedrich et al., 2003; Mason et al.,
2006b; Gold and Cowgill, 2011; Dolan et al., 2016; Zinke et al., 2017). Such data sets facilitate
testing of common assumptions, such as whether fault slip rate is constant over timescales of
hundreds to thousands of years (e.g., Shimazaki and Nakata, 1980). Interestingly, emerging
evidence based the few such incremental slip rate studies available has shown that some faults
indeed exhibit relatively constant slip rates over time (e.g., Van der Woerd et al., 2002; Kozacı et
al, 2009; Gold and Cowgill, 2011; Salisbury et al., 2018), others appear to undergo periods of
transiently accelerated strain release spanning multiple earthquake cycles (e.g., Wallace, 1987;
Weldon et al., 2004; Mason et al., 2006b; Gold and Cowgill, 2011; Ninis et al., 2013; Dolan et al.,
2016; Zinke et al., 2017). Comparing trends in the incremental slip rate records with mechanical
models of fault behavior or observations of potentially linked natural phenomena (e.g., sea level
rise and fall) can provide insights into the processes controlling spatiotemporal patterns of strain
release during earthquakes.
Though conducted as standalone investigations (with coauthorship described in the
Acknowledgements and References sections of this thesis), the studies presented in the following
3

chapters fall broadly into two categories: (1) Chapters 2, 3, and 4 examine aspects of the
distribution of surface deformation resulting from one or more earthquakes; (2) Chapters 5 and 6
use markers of progressive fault slip preserved in the landscape, and stratigraphically informed
geochronologic dating methods, to determine the temporal consistency of fault slip since latest
Pleistocene time for two potentially mechanically linked faults. These two broad fields of study
are introduced in greater detail below. I note that Chapter 2 is based on the study by Zinke et al.
(2014b); Chapter 3 is based on R. Zinke et al., 3D surface deformation in the 2016 MW 7.8
Kaikōura, New Zealand earthquake from optical image correlation: Implications for strain
localization and long-term evolution of the Pacific-Australian plate boundary, submitted to
Geochemistry, Geophysics, Geosystems, 2018 (manuscript 2018GC007951; referred to herein as
Zinke et al. 2018a); Chapter 4 is based on Zinke et al. (2015) and Geology Forum Reply, Zinke et
al. (2016a); Chapter 5 is based on Zinke et al. (2017); and Chapter 6 is based on R. Zinke et al.,
Multi-millennial incremental slip rate variability of the Clarence fault at the Tophouse Road site,
Marlborough fault system, New Zealand, submitted to Geophyscial Research Letters (manuscript
2018GL080688 ; referred to herein as Zinke et al. 2018b).

1.2 Quantifying Earthquake Surface Expression
Fault slip resulting from earthquake rupture at depth reaches the surface in the form of
discrete ground-surface breakage along a primary fault strand, and also as distributed, off-fault
deformation (OFD) which includes secondary faulting, warping, rotation, and distributed granular
flow, that occur outside the fault core (e.g., Ben-Zion and Sammis, 2003; Shelef and Oskin, 2010;
Dolan and Haravitch, 2014; Zinke et al., 2014b; Milliner et al., 2015, 2016a, 2016b; Teran et al.,
2015; Zinke et al., 2015). Quantifying the proportions of on- versus off-fault deformation in large
4

earthquakes, and determining what factors exert primary controls on those proportions, is essential
to the proper interpretation of field data, and can eventually lead to the creation of seismic hazard
microzonation maps (e.g., Dolan and Haravitch, 2014).
In the Chapters 2 and 3, I examine the spatial patterns of slip resulting from large, single
events using optical image correlation (OIC) methods. Specifically, I compare the correlation-
derived measurements, projected into the fault from several kilometers distance, to on-fault
measurements of surface slip to determine patterns of distributed, off-fault deformation. In Chapter
2, I collected 398 measurements of slip in the 2013 MW 7.7 Balochistan, Pakistan earthquake by
visual analysis of WorldView high-resolution satellite imagery (Zinke et al., 2014b). To determine
the amount of off-fault deformation associated with the earthquake, I compared those visual
measurements with horizontal surface deformation maps produced by COSI-Corr subpixel image
correlation of Landsat-8 imagery. In Chapter 3, I used very high resolution (~0.5 m) stereo pre-
and post-earthquake WorldView optical satellite imagery and an advanced workflow for OIC to
retrieve a homogeneous 3D ground-surface displacement field in the vicinity of the 2016 Kaikōura
earthquake. This study represents the first comprehensive application of this methodology to
studying earthquake deformation. I determined OFD on the Kekerengu fault by comparing my
correlation-derived measurements of total fault ground surface displacement with published field
measurements of fault slip.
From these measurements of OFD, I tested for the potential influence of several geologic
factors on controlling OFD. Specifically, previous studies have shown that the structural maturity
of the fault exerts a primary control on the total percentage of OFD. However, large along-strike
variations in the percentage of strain localization observed in the 2013 Balochistan rupture imply
the influence of important secondary controls. We therefore tested for the influence of near-surface
5

material through which the rupture propagated by comparing the percentage OFD to the type of
material (bedrock, old alluvium, and young alluvium) at the surface and the distance of the fault
to the nearest bedrock outcrop (a proxy for sediment thickness along this hybrid strike slip/reverse
slip fault), and found significantly more off-fault deformation in younger and/or thicker sediments.
In the 2016 Kaikōura earthquake, we compared the percent OFD to several potential factors,
including: (1) fault structural maturity (cumulative displacement); (2) local topographic slope; and
(3) the distance over which the field measurements were projected.
In Chapter 4, I relate how on- and off-fault deformation accommodated in earthquakes is
manifested in the landscape. Specifically, I compare surface faulting patterns in flights of fluvial
terraces at the Branch River and Saxton River sites along the Wairau and Awatere faults in the
Marlborough fault system of South Island, New Zealand (Fig. 1). The comparison is applied at
two different scales of time and cumulative displacement: First, I compare the surface expression
of faulting between the Wairau fault (which has accommodated > 150 km of cumulative
displacement), and the Awatere fault (which has accommodated only < 20 km of cumulative
displacement). As faults accumulate greater amounts of displacement, strain progressively
localizes into a relatively narrow, structurally simple zone. This process, known as structural
maturation, decreases the proportion of total shear accommodated as off-fault deformation (OFD),
which includes secondary faulting, warping, rotation, and distributed granular flow, that occur
outside the fault core. Second, I examine fault-perpendicular width of OFD discernable in high-
resolution aerial lidar scans of the Saxton River site, as a function of the cumulative displacement
experienced by different deposits (ranging between 0 and ~70 m of slip). At this scale, the width
and expression of OFD in geologic and geomorphic features will increase as deformation
accumulates over multiple earthquakes, especially along structurally immature faults. In this way,
6

the style and structural complexity of surface deformation recorded in recent deposits along a fault
will reflect both the structural maturity of the underlying fault as well as the cumulative
deformation resulting from earthquakes experienced by the deposit. Understanding how these
processes become manifest in the landscape is essential to the proper geomorphic interpretation of
surface faulting patterns. These observations provide key insights into the interplay between
overall fault structural maturity and the progressive geomorphic manifestation of OFD with
increasing fault offset in young deposits.

1.3 Characterizing Trends in Incremental Fault Slip Rate Behavior
In the final two chapters, Chapters 5 and 6, I use progressively displaced markers at the
Saxton River site on the Awatere fault, and the Tophouse Road site on the Clarence fault (~20 km
to the north) to determine the incremental slip rate history of the faults at their respective sites
(Zinke et al., 2017). The Saxton River and Tophouse Road sites comprise suites of river terraces
representing various ages, from modern floodplains active at the present day, to higher, climate-
controlled surfaces deposited c. 11–14 ka. The different-aged terraces record different amounts of
fault strike-slip, with younger, less offset surfaces representing ~0–10 m of strike-slip, and older
surfaces recording ~47–70 m of strike slip. Fault offsets are recorded by the abandoned river banks
within the higher terraces, and channels within the terraces surfaces. To measure the offsets, I
produced geomorphic maps based on field studies and high-resolution aerial lidar
microtopographic data (Dolan and Rhodes, 2016). The terrace and channel deposits were dated
using an infrared stimulated luminescence technique (Rhodes, 2015). I then determined the ages
of the offset features based on observations of river bedload (a proxy for streampower) and
geomorphic indices preserved in the microtopography (e.g., the offset of ephemeral channels
7

within a terrace surface; for reference, see Cowgill, 2007). Once the offset-age history of each site
was determined, I calculated the incremental slip rates represented by each offset-age pair using a
Markov chain Monte Carlo sampling method which accommodates the assumption that the faults
slipped uni-directionally (see documentation in Chapter 6 S5; originally proposed by Gold and
Cowgill, 2011).
Determining the incremental slip rates of the Awatere and Clarence faults at these sites will
contribute to a larger, ongoing study of incremental fault slip rates along all four major dextral
faults in the Marlborough fault system (MFS) of South Island, New Zealand. Comparing the
incremental slip rate records spanning Holocene and latest Pleistocene time for all four faults will
allow for testing of whether the faults behaved in any coordinated fashion over this time.
8

CHAPTER 2:
Surface slip and off-fault deformation patterns in the 2013 MW 7.7 Balochistan, Pakistan
earthquake: Implications for controls on the distribution of near-surface coseismic slip

The following work is based on R. Zinke, J. Hollingsworth, and J. F. Dolan (2014),
“Surface slip and off-fault deformation patterns in the 2013 MW 7.7 Balochistan, Pakistan
earthquake: Implications for controls on the distribution of near-surface coseismic slip”
published in Geochemistry, Geophysics, Geosystems (doi: 10.1002/2014GC005538).

2.1 Abstract
Comparison of 398 fault offsets measured by visual analysis of WorldView high-resolution
satellite imagery with deformation maps produced by COSI-Corr subpixel image correlation of
Landsat-8 and SPOT5 imagery reveals significant complexity and distributed deformation along
the 2013 MW 7.7 Balochistan, Pakistan earthquake. Average slip along the main trace of the fault
was 4.2 m, with local maximum offsets up to 11.4 m. Comparison of slip measured from offset
geomorphic features, which record localized slip along the main strand of the fault, to the total
displacement across the entire width of the surface deformation zone from COSI-Corr reveals
~45% off-fault deformation. While previous studies have shown that the structural maturity of the
fault exerts a primary control on the total percentage of off-fault surface deformation, large along-
strike variations in the percentage of strain localization observed in the 2013 rupture imply the
influence of important secondary controls. One such possible secondary control is the type of near-
surface material through which the rupture propagated. We therefore compared the percentage off-
fault deformation to the type of material (bedrock, old alluvium, and young alluvium) at the surface
9

and the distance of the fault to the nearest bedrock outcrop (a proxy for sediment thickness along
this hybrid strike slip/reverse slip fault). We find significantly more off-fault deformation in
younger and/or thicker sediments. Accounting for and predicting such off-fault deformation
patterns has important implications for the interpretation of geologic slip rates, especially for their
use in probabilistic seismic hazard assessments, the behavior of near-surface materials during
coseismic deformation, and the future development of microzonation protocols for the built
environment.

2.2 Introduction
Numerous recent studies of large historical surface ruptures indicate significant variations
in the percentage of off-fault deformation, ranging from highly localized to completely distributed
(e.g., Akyuz et al., 2002; Hartleb et al., 2002; Rockwell et al., 2002; Treiman et al., 2002; Duman
et al., 2003; Liu et al., 2003; Haeussler et al., 2004a, 2004b; Klinger et al., 2005; Xu et al., 2006;
Van Dissen et al., 2011; Oskin et al., 2012; Teran et al., 2015). These observations raise basic
questions as to what controls the localization of fault slip near the surface. Dolan and Haravitch
(2014) suggested that the structural maturity of the fault is the primary control, with slip on faults
with large cumulative displacements remaining generally more localized at the fault trace than
along ruptures on less mature faults. Significant variations, however, in the percentage of off-fault
deformation within a single rupture on a fault or faults with the same structural maturity suggest
that other factors also act to control the degree of slip localization at the surface.

10

2.3 Background
The 2013 MW 7.7 Balochistan earthquake ruptured ~230 km of the Hoshab fault in
southeastern Balochistan, Pakistan (U.S. Department of the Interior, U.S. Geological Survey,
2013; Avouac et al., 2014; Jolivet et al., 2014). The Hoshab fault is located in the Makran
accretionary complex and is part of a mechanical transition zone between N-S convergence in the
Makran convergence zone to the west and N-S left-lateral strike slip on the Chaman transform
system to the north and east (Lawrence et al., 1981; Fig. 1). The Makran convergence zone
accommodates ~3 cm/yr of convergence between Arabia and Eurasia (Reilinger et al., 2006),
whereas the Chaman transform system accommodates ~3 cm/yr of left-lateral strike-slip motion
between India and Eurasia (Lawrence et al., 1981; Ambraseys and Bilham, 2003; Mohadjer et al.,
2010; Ader et al., 2012; Szeliga et al., 2012). The close association of the Hoshab fault with other
reverse faults in the Makran region and its position at the boundary between a bedrock range front
to the northwest and a sedimentary basin to the southeast suggest that either: (a) the Hoshab fault
initiated as a thrust fault and later developed into a strike-slip fault; or (b) the fault alternates in its
kinematics (Avouac et al., 2014). Kinematic indicators observed in field studies of faults in the
western Makran ranges show a bimodal distribution associated with early stage thrusting, and
highly variable slip vectors associated with reverse faulting contemporaneous with sinistral strike-
slip faulting (Platt et al., 1988), lending support to this latter possibility. Inversion of teleseismic
waveforms shows that the Hoshab fault is subvertical in the northeast, but exhibits a gentler (~50°)
northward dip along its southwestern section (Avouac et al., 2014; Jolivet et al., 2014). Despite
this along-strike variation in fault dip, the 2013 rupture involved almost pure left-lateral strike slip,
with only minor oblique reverse motion (Avouac et al., 2014; Jolivet et al., 2014). Inverse fault
slip modeling of the Landsat-8-derived displacement field indicates that the majority of slip
11

occurred at relatively shallow depths (< ~10 km), with the largest slip values occurring near the
surface (Avouac et al., 2014; Jolivet et al., 2014). This is in marked contrast to many other recent
strike-slip ruptures that exhibit an apparent deficit in slip near the surface (e.g., Fialko et al., 2005;
Sudhaus and Jonsson, 2011; Elliott et al., 2012), although the more strike-slip-dominated northern
section of the 2008 MW 7.9 Wenchuan rupture also exhibited higher slip nearer the surface (Tong
et al., 2010).

2.4 Methods
We use two independent methods to analyze and measure patterns of surface deformation
and displacements, as well as the structural style of the surface rupture and surrounding damage
zone: (1) optical image correlation, using recently acquired Landsat-8 satellite images, and (2)
visual inspection of high-resolution WorldView satellite images.

2.4.1 Optical Image Correlation
Optical image correlation is used to determine the horizontal deformation produced by the
earthquake at the surface. This technique compares two Landsat-8 satellite images of the region
affected by the earthquake and measures any pixel shifts between them with subpixel precision.
The short time period between the two image acquisitions (16 days; pre-earthquake: 10 September
2013, post-earthquake: 26 September 2013), coupled with minimal agriculture and vegetation in
this desert environment, resulted in an excellent correlation between the two
images (Fig. 2A). The pre- and post-earthquake images were obtained from the USGS
EarthExplorer website (http://earthexplorer.usgs.gov/), and correlated using the COSI-Corr
software package (COSI-Corr, available for free download from www.tectonics.caltech.
12

edu/slip_history/spot_coseis/index.html). Usually, COSI-Corr requires the user to orthorectify
and coregister the two images before correlation. The USGS, however, distributes Landsat-8
images as an orthorectified product, thereby obviating several processing steps. Subpixel
correlation of the two images using COSI-Corr takes advantage of an iterative, unbiased processor
that estimates the phase plane in the Fourier domain (Leprince et al., 2007a,
2007b, 2008; Ayoub et al., 2009a). This process produces two correlation images, each
representing one of the horizontal ground displacement components in the East-West and North-
South direction. In practice, this technique can resolve displacements as small as one tenth of the
input pixel size (15 m for Landsat-8). Due to the excellent optical qualities of the Landsat-8
platform, however, and the minimal topographic residuals present in the correlations (a result of
the nadir-looking Landsat-8 sensor), we were able to resolve displacements as small as 1/15 of a
pixel (i.e., ~1 m). Stacking of fault-perpendicular displacement profiles can further increase the
signal-to-noise ratio, thus allowing very well-resolved estimates of displacement along-strike (Fig.
2b).
Due to the large displacements that occurred in this earthquake, coupled with local areas
of higher noise resulting from topography or low image contrast, we used a multiscale window in
our correlations, varying between 128 pixels and 32 pixels in dimension. Patches with the largest
window size were correlated first, and if the correlation succeeded, smaller patches (decreasing by
power of two) were correlated, while accounting for the previously found displacement. The
process was iterated until the minimum window size was reached, or until the correlation failed,
in which case the measurement found from the previous larger size was kept and the process moved
on to the next area (see the COSI-Corr user manual, available with download of the COSI-Corr
software). Displacement measurements were made horizontally and vertically every 16 pixels,
13

resulting in correlation map of 240 m resolution. Additional noise was removed from the resulting
EW and NS displacement maps by stripping out unlikely values (i.e., displacement of < 28 m and
> 18 m, and values with a low signal-to-noise ratio). Due to a slight overlap of the charge coupled
device (CCD) arrays on the Landsat-8 sensor, the correlations contain a striping artifact, which
was removed by stacking and subtracting the average artifact signal from the correlations; this was
done using the destriping tool in COSI-Corr with a manual rotation of ˗11.75°. Finally, we used a
3×3 median filter to further reduce noise, while preserving edges. Visual comparison of the filtered
and raw displacement maps indicates our post processing steps do not artificially reduce our fault
offset interpretation.

2.4.2 Mapping the Surface Rupture Trace and Width of Deformation Using High-
Resolution Satellite Imagery
We used the panchromatic band of WorldView-1 and WorldView-2 high-resolution
satellite images (with a nominal pixel resolution of < 0.5 m [DigitalGlobe, 2014]) to visually map
the surface trace of the 2013 rupture and measure the offset of geomorphic features. To account
for any effects due to viewing geometry, we georeferenced and orthorectified the WorldView
images using post-earthquake Landsat-8 images and an ASTER (Advanced Spaceborne Thermal
Emission and Reflection Radiometer) GDEM (digital elevation model) with 30 m resolution
(ASTER GDEM is a product of METI and NASA and was acquired through the USGS
EarthExplorer website). After orthorectification, the pixel sizes for the WorldView images were
between 0.505 and 0.594 m, providing a detailed view of the rupture trace. In all, we used 21
WorldView images for our visual analysis of the rupture. Of those, 17 were taken within 16 days
of the earthquake, and all of the images were captured within 4 months of the event, so the effects
14

of erosion of the fault scarp and any associated features are minimal. More information on all
satellite images used in this study is available in the supplementary material as Table S1.
We mapped the major features of the rupture trace where it is visible in the WorldView
images. To represent the width and style of surface deformation, we also mapped out zones of
distributed deformation, including secondary fault strands, cracks, rotations, and obvious warping
of the ground surface (Fig. 4). Identification of such features is subject to the roughness of the
terrain, image resolution, and the angle of the sun in each image. As such, the full extent of
continuous deformation may not be represented in our mapping. Offsets of about 1 m or greater
on discrete planes were resolvable with the 0.5–0.6 m pixel sizes of the WorldView images.
We also noted whether or not each offset is on the ‘‘main’’ fault strand of the rupture trace.
We define the main fault strand as the strand that accommodates more deformation and is more
continuous along strike than surrounding strands. Where subparallel strands are approximately
equal in both of these characteristics, neither was considered the main strand. We note that in some
locations where no main strand can be established, we consider all deformation to be distributed,
off-fault deformation, and we therefore assign a value of 0 m for our discrete main strand fault
offset at those sites.

2.4.3 Measuring Offsets
We identified geomorphic features (e.g., channel thalwegs and edges, small ridges, gullies,
and rills) along the surface trace of the 2013 rupture for which we could reliably measure offsets.
We also measured several offset roads or footpaths, but such features are rare in this sparsely
populated region. To measure each offset, we progressively back slipped one side relative to the
other along the trace of the fault. In this way, we visually identified the maximum and minimum
15

amounts of offset, identified as the points at which the restorations become sedimentologically
implausible. These estimates are used as the error bounds shown in Figure 6. We also identified a
preferred offset value for each feature, representing what we believe is the most likely amount of
slip expressed on the fault strand at that location.
When measured, each offset was assigned a confidence rating of ‘‘A,’’ ‘‘B,’’ or ‘‘C’’ (Fig.
3 and Table S4). A confidence of ‘‘A’’ indicates that the feature is sharply defined in the image
and correlates unambiguously across the fault. A confidence level of ‘‘B’’ indicates that the
geometry of the feature and the offset across the fault are less well constrained, and a confidence
of ‘‘C’’ indicates that there is some ambiguity in reconstructing the feature across the fault,
especially where the feature is warped or bent near the fault, and/or where the pre-offset geometry
is difficult to determine. Figure 3 shows examples of offsets assigned each of these ratings.
Our analysis revealed a wide range of surface deformation patterns. In some locations, fault
strands occur as discrete (< 1 m wide) fractures that are laterally continuous for up to 2 km. In
contrast, at many locations, slip is not localized across a discrete fault plane, but rather is manifest
as broader zones of diffuse deformation up to several tens of meters wide. Where there is a
geomorphic feature that is highly linear for hundreds of meters on either side of the fault strand,
we consider any clear bending over a relatively narrow fault-perpendicular aperture reproducing
the scale at which geomorphic offsets are typically measured by field geologists (≤ ~5 m) to be
discrete and measurable fault slip. We measured the offset of such features by
restoring them to their original geometry, as described above. Bending of geomorphic features
over wider fault-perpendicular apertures (i.e., > 5 m) could not be consistently recognized and
measured. Therefore, any such measurements were not included in our analyses below. If a feature
was deformed across a sufficiently wide fault-perpendicular aperture (typically > ~10 m) such that
16

two distinctly offset sides of the feature cannot be identified, the faulting is considered to be truly
‘‘distributed’’ and 0 m of fault offset was recorded in those instances. Thus, 0 m offset
measurements do not necessarily preclude the presence of surface deformation. Rather, these
‘‘zero’’ measurements may represent locations where any surface deformation must be truly
distributed and accommodated by secondary faulting and/or folding and other distributed
deformation that cannot be readily identified at the scale of field measurements. For example, if a
geomorphic feature (e.g., a small channel or bar) is deformed over a very wide zone such that the
offset of the feature cannot be unequivocally distinguished from a geometry related solely to
fluvial processes, the ‘‘offset’’ was recorded as 0 m.
To gain a better understanding of the pre-offset geometry of each of the geomorphic
features crossing the fault, we referred to pre-earthquake color images available in Google Earth.
These pre-earthquake images were not quantitatively differenced with the post-earthquake images,
but instead were used to give a better sense of what the feature looked like before it was offset,
reducing uncertainty in restoring the geomorphic features to their original geometry.
In addition to measuring the offsets and assigning quality rankings, we made a qualitative
assessment of the type and relative age of the surface material through which the rupture
propagated at each measurement location. We classified the types of surface materials into five
categories: Active wash sediments, young alluvial fans, older alluvial fans, old and probably well-
consolidated alluvium, and bedrock. Active wash sediments are defined as loose sediments that
are found near active or recently active rivers and that appear to have been deposited recently.
Young alluvial fans exhibit surfaces that do not show significant desert varnish and are thus tan to
light brown in color (appearing white to pale gray in the panchromatic images). Young fans have
a distinct distributary drainage network and are uncut by any younger fan material. Older alluvial
17

fans, in contrast, have significant desert varnish and are brown to dark brown in color (medium
gray to black in panchromatic images). These older fans may have remnant distributary or post-
fan tributary drainage networks and may be cut by younger alluvial fans. Old, compacted alluvium
is brown (gray in panchromatic). It is distinct from ‘‘older fans’’ in that the overall geometries of
the deposits are typically highly eroded and do have any obvious remnant fan morphology.
Drainage patterns and topographic relief suggest that the old alluvium is more cohesive than old
alluvial fans, though visual analysis provides no evidence of lithification (e.g., prominent, erosion-
resistant bedding, and suggestions of cementation). Bedrock is identified by its relief, texture,
color, and bedding characteristics, where present. Bedrock can range from white to dark brown in
color. Examples of the different material types are shown in Figure 4.

2.5 Results
2.5.1 COSI-Corr Results
The EW and NS displacement fields from correlation of pre- and post-earthquake Landsat-
8 images are shown in Figure 2a. Along-strike left-lateral displacement was measured from the
EW/NS displacement field by stacking fault-perpendicular profiles (Fig. 2b). These data are
provided in Table S2. Stack width is 25 pixels, equivalent to 6 km on the ground. Error estimates
represent the standard deviation (1-sigma) calculated from a trend that visually best fits the
displacement on each side of the fault (calculated using the stacking tool in COSI-Corr). These
error estimates do not take into account errors in the interpretation of the fault offset, which can
be challenging in some areas. The location of highest slip occurs ~50 km SW of the main shock
epicenter, with slip reaching up to 10.5 ± 0.3 m (Fig. 2b). The average slip determined from COSI-
Corr measurements is 6.7 +0.3/-0.4 m.
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A number of aftershocks occurred after the September 24 main shock, the largest of which
was an MW 6.8 event that ruptured the same fault plane as the main shock on 28 September 2014
(Avouac et al., 2014; Jolivet et al., 2014). To determine whether any additional surface
deformation occurred during the 28 September aftershock, we also correlated two SPOT5 satellite
images from 19 February 2004 and 29 September 2013, 1 day after the aftershock (image available
in the supplementary material Fig. S3). The displacement profile measured from this SPOT5
correlation indicates that no additional surface deformation occurred during the 28 September
aftershock (Fig. 2b). Therefore, slip in this aftershock likely occurred at depth. Consequently, we
can be confident that our fault offset measurements determined from visual inspection of
WorldView data do not include additional surface slip from the 28 September aftershock.

2.5.2 Surface Rupture Displacements
From visual analysis of WorldView imagery, we measured 398 displacements along ~205
km of the rupture trace. Of the measurements collected, 279 were located on the main trace of the
rupture, as defined above. A complete list of all measurements and their associated characteristics
is provided in the supporting information as Table S4. Of these measurements, we observed a
maximum displacement of 11.4 +1.3/-2.1 m. This is comparable to the 10.5 ± 0.3 m maximum
surface slip measured by our COSI-Corr measurement near that location, and similar to other
COSI-Corr analyses of this earthquake (i.e., Avouac et al., 2014; Jolivet et al., 2014).
In places, slip along the rupture trace cannot be identified, or is less than the resolvable limit using
these images (i.e., ≤ ~1 m), as described above. At the northeastern end of the rupture (north of
27°08’N), the rupture trace is locally difficult to identify and measure. This is not surprising, as
coseismic slip along this stretch was < 1–2 m (Avouac et al., 2014; Jolivet et al., 2014, this study)
19

and is therefore at or below the lower limits of resolution of the WorldView imagery. Our
measurements of offset geomorphic features yield an average slip of 4.2 +0.9/-1.0 m.

2.5.3 Structural Style and Width of Deformation Zone
Our field-scale satellite observations of the 2013 rupture trace reveal a great deal of
complexity not captured in the COSI-Corr analysis. COSI-Corr correlations of Landsat-8 images
show a relatively simple rupture trace with little structural complexity along a single fault (Avouac
et al., 2014; Jolivet et al., 2014, this study). Our visual analysis of WorldView imagery, however,
reveals many steps, bends, and complexities at scales ranging from meters to kilometers.
Although the rupture trace is single-stranded at some locations (particularly between the
southwestern end of the surface rupture and 64°23’E, and near the middle of the rupture between
26°40’N, 65°14’E and 26°56’30”N, 65°25’E), it is multistranded along much of its length. In some
instances, the multistranded nature of the rupture is clearly due to broad-scale structural control,
as in the case of an 8-km-wide restraining step at 26°28’00”N, 64°58’30”E, and a 6-km-wide
releasing bend at 26°36’30”N, 65°10’30”E. For most of the rupture length, however,
there is no clear association between the broad-scale structure of the locality and the style of
faulting.
The total width of deformation along the fault that is resolvable in the WorldView images
ranges from narrow and highly localized to very wide and highly distributed (Fig. 5). Where
deformation is extremely localized, all visible offset is localized on a single strand that is less than
a meter wide. At its widest, however, visible deformation locally spans widths of up to nearly 800
m, measured perpendicular to the main fault trace. These broad zones of deformation typically
have one or more fault strands within them, which occur as discrete or meters-wide zones of more
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highly concentrated shear. The transition from localized to highly distributed slip can occur in tens
to hundreds of meters distance along strike. We note that where it is widely distributed,
deformation is mostly observed in the hanging wall of the northwest-dipping fault.
The fault strands themselves exhibit a variety of characteristics. At their most localized, strands
can occur as discrete planes of shear, less than a meter wide. Other strands occur as meters-wide
zones of concentrated deformation within the broader fault zone. The wider types of fault strands
are more common in unlithified, poorly consolidated sediments (i.e., active washes, young alluvial
fans, and older alluvial fans). Riedel shears are locally observed in active wash sediments and
across young alluvial fans. In some active river sediments, the fault trace is visible on either side
of a river, but is not visible within the river bed itself, even where the rupture trace does not appear
to have been washed away by the river. Presumably, deformation is so highly
distributed in those sediments that it is not manifest as a distinguishable rupture trace.

2.6 Discussion
2.6.1 Off-Fault Deformation of the 2013 Rupture
Comparison of our field-scale measurements of offset geomorphic features, which record
the component of slip localized along the main strand of the fault, to the total surface displacement
across the entire width of the zone of surface deformation obtained from COSI-Corr analysis
allows us to quantify the percentage of off-fault deformation associated with the rupture. We
observe significant differences between the field-scale measurements taken along the main strand
of the fault and the total slip measured using COSI-Corr (Fig. 6). Although we would expect
relatively minor discrepancies between the two data sets due to the smoothing effect of the
correlation window used in COSI-Corr, there are several large stretches of the rupture where the
21

main trace field-scale measurements are significantly lower than the corresponding COSI-Corr
measurements (most notably the region from 35 to 90 km along strike, measured from the
southwest end of the rupture; Fig. 6). We attribute this ‘‘missing fault slip’’ to deformation that is
not localized on the main trace of the fault, but rather is distributed throughout the zone of surface
deformation.
Recent studies suggest that the ratio of slip at the surface to slip at depth is primarily
determined by the structural maturity of the fault. Dolan and Haravitch (2014), for example,
compared the ratio of surface slip to slip at depth as a function of cumulative fault displacement
for six large (MW 7.1–7.9) strike-slip earthquakes. They found that as the cumulative strike slip
(i.e., structural maturity) along a fault increases, the ratio of slip along the fault trace relative to
geodetically inferred slip at depth (referred to as the ‘‘surface slip ratio,’’ or SSR) also increases.
The SSR is thought to be a function of the structural maturity of the fault, that is, as a fault
accumulates more slip, the number of discontinuities decreases and the fault structurally matures,
gradually becoming smoother (Wesnousky, 1988; Stirling et al., 1996).
To the best of our knowledge, there are no estimates of cumulative displacement on the
Hoshab fault. Many southward-draining, deeply incised rivers cut north-to-south across the
Hoshab range, thereby suggesting these rivers are antecedent, that is, predating uplift of the range.
We suggest that in addition to any early thrust displacements, the Hoshab fault has accommodated
at least 5.6 km, and likely ~11 km, of left-lateral strike-slip displacement. This is shown
particularly well by the offset of a major river valley at 27°19’15”N, 65°39’31”E (Fig. 7). This
major drainage is offset by ~5.6 km along the Hoshab fault, extending toward the fault at near right
angles along both the southeastern (downstream) and northeastern (upstream) reaches. This
geometry suggests that the leftward deflection of the river at the fault records true fault offset. This
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offset is a minimum, however, as it does not include any distributed component of near-surface
deformation. Indeed, consistent 4-km-wide, convex-to-the-west bends in all of the numerous
drainages that cut the Hoshab Range in this area suggest that significant additional strike slip
(manifest as strike-slip ‘‘drag folding’’) is distributed to the northwest of the Hoshab fault. For
example, the upstream reach of the major drainages offset by 5.6 km exhibits a very linear NE-
trending reach upstream of the large bend in the river. Projecting this trend to the Hoshab fault
indicates that the apparent folding of the river NW of the Hoshab fault has accommodated an
additional 4.8 km of distributed left-lateral strike slip, for a total of ~10.4 km of cumulative strike
slip on the Hoshab fault at this location. A similar ~11 km offset of bedrock 40 km to the NE along
the Hoshab fault suggests that this value may record cumulative strike-slip over much of the
section of the Hoshab fault that ruptured in the 2013 earthquake (Fig. 7).
These measurements indicate that the Hoshab fault is a structurally immature fault in terms
of its cumulative strike slip. Other nearby faults in this system also exhibit small amounts of
cumulative strike slip. For example, left-lateral offset of a distinctive bedrock unit on a related
fault to the southwest of the river valley that is offset 5.6 km suggests cumulative left-lateral
displacement of ~6 km on that strand (Fig. 7). Similarly, offset of a bedrock ridge on another strand
to the east of the Hoshab fault indicates a total of ~4 km of displacement. Thus, despite being
geomorphically well expressed in the landscape, the Hoshab fault appears to be a structurally
immature fault in terms of its total strike slip.
Based on the findings of Dolan and Haravitch (2014), we would expect 40–60% of the
total slip at depth for the earthquake to be localized at the surface for a fault with 6–11 km of
cumulative strike slip. Following the method of Dolan and Haravitch (2014), we define the surface
slip ratio as the ratio of integrated slip (the areaunder the curve of surface slip measurements as a
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function of distance along the rupture trace) measured from offset geomorphic features (blue lines
in Fig. 6) to that the total surface displacement, including any off-fault, distributed deformation
measured by COSI-Corr (red lines in Fig. 6) over the same 205 km long stretch of the rupture for
which the data sets overlap. This definition yields an SSR value of 0.56 +0.16/-0.15 (Table 1),
which is near the upper range expected for a fault with only 6–11 km of cumulative strike slip
(Dolan and Haravitch, 2014). Our estimate of cumulative strike slip on the Hoshab fault does not,
however, include any reverse slip that has occurred along this fault. This additional slip may serve
to enhance, somewhat, the overall level of structural maturity of the fault.
We have no constraints on the amount of reverse slip on the Hoshab fault, nor the precise
timing for the onset of strike slip, so it is difficult to predict what effect the extent to which reverse
slip would smooth the rupture plane. It is also difficult to predict the effects of structural features
such as large striations, mullions, and structural segmentation oriented in a geometry that would
allow dip-slip motion but would resist oblique motion. Inversions for slip distributions by Jolivet
et al. (2014), however, show that once nucleated, the propagating 2013 rupture did not encounter
any geometrical obstacles, nor was it hindered by any significant segmentation. Thus, it appears
that cumulative reverse slip experienced by the Hoshab fault did not generate significant strike-
perpendicular barriers to smooth rupture propagation, but rather may have served to smooth the
rupture plane and may have enhanced the overall structural maturity of the fault beyond the ~11
km of strike slip we document.
To more closely examine the influence of structural complexity on the percentages of off-
fault deformation along the rupture, we divided the surface rupture into 10 segments based on
whether the surface trace along those sections of the fault was structurally complex or structurally
simple (Fig. 6). In this context, when we describe the surface expression of faulting, we are not
24

referring exclusively to large zones of structural complexity, such as fault step overs, bends, or
intersections. Rather, we are referring to stretches along the fault that exhibit multiple strands,
rotations, and warping (see Fig. 5 for examples). For each structurally simple and structurally
complex stretch of fault, we divided the area under the curve of the WorldView measurements by
that of the COSI-Corr measurements over that stretch of fault, in a manner analogous to the way
that we calculated the SSR. Our analysis demonstrates that, as might be expected, there is a clear
difference in the percentage of on-fault slip between the structurally simple and structurally
complex parts of the surface rupture. Specifically, the average ratio of slip localized on the main
rupture trace to the total deformation is 0.74 for structurally simple segments of the rupture, and
0.44 for structurally complex segments (Table 1). We emphasize that these ratios are averages over
3–82 km stretches of the fault, and within any one of those zones the ratio can range anywhere
from 0–100% on-fault, localized deformation. Nevertheless, on average, even along stretches of
fault where the trace is relatively structurally simple, approximately one-quarter of the total fault
slip that reached the surface was expressed as distributed deformation surrounding the fault.
To examine this relationship more precisely, we compared each of our 279 field-scale
measurements along the main trace of the fault to the amount of surface slip estimated by COSI-
Corr analysis at each measurement location. The average of these ratios for measurement at
structurally simple locations along the rupture and at structurally complex locations yields a
ratio akin to the local surface slip ratio (LSSR) described by Dolan and Haravitch (2014). For
structurally simple sections of the fault, we calculate an LSSR of 0.8, indicating that, on average,
~20% of total surface deformation is accommodated by distributed processes off the main strand
of the fault. In contrast, for structurally complex sections of the fault, we calculate an LSSR of 0.4,
indicating that more than half of the total surface displacement is accommodated off the fault.
25

These ratios (Table 1) demonstrate the control exerted by the local structural complexity of the
fault on the localization of strain along a fault rupture, with greater strain localization in areas of
greater local structural complexity. Such along-strike differences in structural expression will, if
repeated over numerous earthquake cycles, become manifest in the landscape. Careful geomorphic
mapping of other faults should thus be able to distinguish between fault sections
where surface slip will be relatively more distributed or more discrete.

2.6.2 Surface Material Control on Strain Localization
Although the structural immaturity of the Hoshab fault exerts a primary control on the
localization of deformation, variations in the percentage of off-fault deformation along the rupture
suggest that there are other controls at work (Dolan and Haravitch, 2014). Recent experimental
studies (e.g., Ma, 2008; Ma and Andrews, 2010; Kaneko and Fialko, 2011) show that the type of
material through which a rupture propagates strongly affects the amount of distributed
deformation. Specifically, slip is expected to be more localized where a rupture passes through
bedrock than where it passes through poorly consolidated or unconsolidated sediments, due to the
velocity strengthening nature of these materials. This phenomenon has been observed in several
recent large-magnitude surface ruptures. For example, in the 2010 MW 7.0 Darfield, New Zealand,
earthquake, the average surface displacement was ~2.5 m, with maximum offsets reaching 5 m,
yet there was little or no discrete faulting (Van Dissen et al., 2011; Duffy et al., 2013). Instead,
deformation was distributed over 30–300 m wide zones, probably due to the thick sequence of
poorly consolidated alluvial gravels through which the rupture propagated (Van Dissen et al.,
2011). Similarly, recent studies show dramatic variations in the style of deformation and
26

magnitude of discrete slip associated with the 2010 MW 7.2 Sierra El Mayor-Cucapah earthquake
due to variations in the type of near-surface material
(Teran et al., 2015). In particular, remote sensing techniques revealed ~2 m of coseismic slip
occurred along the southern part of the 2010 El Mayor-Cucapah rupture (Wei et al., 2011; Oskin
et al., 2012), yet no discrete faulting was observed at the surface where the rupture passed through
the thick sediments of the Colorado River delta (Oskin et al., 2012; Teran et al., 2015). Conversely,
bedrock has been shown to localize deformation. For example, the highest coseismic slip values
associated with the 1999 MW 7.1 Hector Mine earthquake were observed where strain was most
localized where the rupture passed through the bedrock all the way to the surface
through the Bullion Mountains (Treiman et al., 2002; Milliner et al., 2014; Milliner et al., 2015).
Thus, it appears likely that the type (more specifically the strength) of the near-surface material
through which the 2013 rupture on the Hoshab passed has an effect on strain localization.
To test whether or not the type of surface material exerts a control on the localization of
deformation for this rupture, we examined the amount of slip localized on the main trace of the
fault as a function of the type of near-surface material from which each offset was measured. As
described above, the surface material types were classified as active wash sediments, young
alluvial fans, older alluvial fans, old compacted alluvium, and bedrock. Intuitively, one would
expect older sediments to be mechanically stronger than younger sediments because they have had
more time to become more indurated, and bedrock is expected to be stronger than unlithified
sediments. While we make no attempt at quantifying the exact mechanical properties of these
materials, we use their relative ages as a proxy for their relative strength. We would expect strain
to be significantly more localized in older materials and bedrock. If this hypothesis is correct, off-
fault deformation would increase from bedrock to old alluvium to older fans and surface
27

deformation would be the most distributed in the youngest, least-consolidated sediments. In this
analysis, we do not consider the grain sizes, sorting, or mineralogy of either the sediments or the
bedrock, as those characteristics cannot be inferred from satellite imagery.
We estimated the percent localization of strain (100% - percentage OFD) for each of the
field-scale measurement locations along the main strand of the fault. To compare each field-scale
measurement to the total expected slip at that location, we interpolated between the nearest two
COSI-Corr measurements using a simple linear interpolation. We then differenced the preferred
value for each field-scale measurement with its corresponding COSI-Corr slip value and
normalized that difference to the expected slip. To avoid locations that are obviously structurally
controlled (e.g., the restraining step at 26°28’00”N, 64°58’30”E and the releasing bend at
26°36’30”N, 65°10’30”E), we omitted points in and near large fault steps and structural
discontinuities.
When we compared all relevant estimates of strain localization grouped according to their
type of surface material, we observed a general tendency for strain to be more localized in younger
sediments, but the trend is weak (Fig. 9a). We hypothesize, however, that the thickness of the
material also plays a significant role in localizing or delocalizing deformation. For example, a very
thin layer of sediment overlying bedrock might not have sufficient thickness to distribute
deformation, and the localization of strain in that sediment layer would simply reflect the
localization of strain in the underlying bedrock. A thicker package of sediment, in this instance,
would more effectively distribute deformation. From the WorldView images, we have no direct
indication of sediment thickness at any given location. Instead, we use the distance from the offset
measurement location to the nearest bedrock outcrop as a proxy for sediment thickness.
28

We suggest that use of this distance-to-nearest-bedrock proxy is warranted as the Hoshab
fault exhibits a clear history of reverse slip, uplifting bedrock to the northwest, while sediments
were deposited basinward to the southeast. This simple relationship is schematically illustrated in
Figure 8. Moreover, we do not attempt to quantify the precise relationship between sediment
thickness and distance to bedrock, but would nevertheless expect the sediments to be generally
thicker farther from the edges of the river or basin. When we compare localization in different
sediment types for measurements more than 20 m away from the nearest bedrock outcrop, we see
that strain is indeed more localized in older sediments than in younger sediments (Fig. 9b),
implying a positive correlation between a material’s age and its ability to localize deformation. As
expected, strain is generally more localized in bedrock than in any of the different sediment types.
The tendency for younger and thicker sediments to distribute deformation does not,
however, apply without exception (Fig. 9). At some locations, strain is extremely localized in
active wash sediments at relatively large distances from the nearest bedrock outcrop. At those
locations, the depth to bedrock may be shallower than indicated by distance to the nearest bedrock
outcrop. Alternatively, the more localized parts of the surface rupture may simply reflect sections
of the underlying fault that are structurally simpler than other sections. For instance, we observe
locations in bedrock where strain is highly distributed, reflecting localized structural complexity
of the fault. Nevertheless, although the relative local abundance of such complexities in the
bedrock underlying different types of sediments may play some role in determining strain
localization, the consistency of the percentages of strain localization in different sediment types
along the entire length of the fault strongly suggests that the sediment type and relative thickness
of the sediment act as a basic control on the distribution of near-surface deformation.
29

A potential source of bias in this analysis arises from the method of sampling.
Measurements were taken wherever a geomorphic piercing point is available. If there are many
piercing points in a sediment package where localization happens to be controlled by other factors
(e.g., structural discontinuities) then the feature could be oversampled and any statistics will be
biased toward that feature. We assume, however, that the 278 main-strand measurements
(excluding those in zones of obvious structural control) taken over a 205 km length along the main
trace of the surface rupture are sufficiently numerous and well-spaced that they accurately
represent the localization of strain in each type of surface material.
Further study is required to examine the effects of cumulative slip in each type of surface
material. In this analysis, we treated all types of surface materials across the rupture as though they
have experienced the same amount of slip. Naturally, however, materials old enough to have
experienced several earthquake cycles will have accumulated more deformation than freshly
deposited materials. Pre-earthquake images show evidence of faulting from previous earthquakes
in some of the older fans, in old alluvium, and in bedrock, whereas younger materials (i.e., active
wash sediments and young alluvial fans) were mostly undeformed prior to the 2013 surface
rupture. Such prior deformation in older materials would serve to further enhance strain
localization, complementing the effects of strengthening due to compaction and induration.
Overall, our comparison of strain localization with surface material type strongly suggests
that, given sufficiently thick sediments, there is a correlation between percent OFD and material
type, as suggested by models (e.g., Ma, 2008; Ma and Andrews, 2010; Kaneko and Fialko, 2011)
and field observations (e.g., Treiman et al., 2002; Shelef and Oskin, 2010; Van Dissen et al., 2011;
Oskin et al., 2012; Duffy et al., 2013; Dolan and Haravitch, 2014). The precise degree of influence
that near-surface material properties and thickness have on strain localization provides fertile
30

ground for future research, and many more observations such as those detailed above from other
specific ruptures are needed.

2.6.3 Implications for the Use of Fault Slip Rate Studies Based on Geomorphic Offsets
In light of the observations described above, it is clear that measuring only the slip localized
on the main strand of a fault will significantly underestimate the total slip associated with a rupture
event. This has important implications for the proper understanding and use of slip rate studies
based on offset geomorphic markers along structurally immature faults, as such measurements
may significantly underestimate the total displacement at a site. Consequently, any slip rates based
on these offset measurements will also be underestimated (e.g., Dolan and Haravitch, 2014). This
observation, in turn, will affect any comparisons between geologic
fault slip rates and geodetic measurements in the search for transient strain accumulation.
Special care should be taken when interpreting slip rates based on offsets in thick, relatively young
sediments, as sediments of sufficient thickness tend to distribute deformation. Intuitively, the effect
of distributed, off-fault deformation is likely to be smaller for structurally mature faults than it is
for structurally immature faults. But in general, we suggest that all slip rates that do not account
for off-fault deformation should be considered minima.
When determining slip rates based on measurements of offset geomorphic features,
however, we are hesitant to suggest a numerical correction factor based on any of the factors
mentioned above (i.e., structural maturity, near-surface material type, and thickness), as further
study is required to better understand the effects of each of them. With a better understanding of
the percentage of off-fault deformation and its controls, future work should be able to more
31

accurately constrain the degree to which geological slip rates based on surface offsets
underestimate the true fault slip rate at seismogenic depths.

2.6.4 Implications for Seismic Hazard
Studies of distributed versus localized deformation have critically important implications
for probabilistic seismic hazards assessment (PSHA). Because fault slip rates are often a primary
input in PSHA models, it is important to understand how slip at depth along a fault is manifested
as slip at the surface, and especially the degree to which it is localized on the main surface trace of
a fault such as might be used by a geologist to measure an offset for a slip-rate study. As discussed
above, some percentage of the total deformation associated with an earthquake will likely be
distributed away from the main trace of the surface rupture. Consequently, slip rate studies based
solely on main-strand offsets could lead to systematic underestimation of the true fault slip rate,
resulting in a commensurate underestimation of hazard. This is especially true when determining
slip rates of small cumulative strike-slip displacement (< 25 km) faults (Dolan and Haravitch,
2014). As seen in the case of the 2013 rupture along the Hoshab fault, accounting solely for slip
manifest along the main trace of the rupture could lead to an underestimation of the total surface
slip associated with the main shock by approximately 45% of the true value. The 2013 rupture of
the Hoshab fault also demonstrates that special care must be taken when measuring geomorphic
offsets along ruptures that propagated through thick, unconsolidated sediments, as these deposits
tend to distribute deformation. Any underestimations of true fault slip rates resulting from failure
to account for distributed, off-fault deformation may lead to underestimation of the hazard posed
by the fault, with obvious implications for the built environment, especially for faults near densely
populated areas.
32

Additionally, surface slip-per-event data are commonly used to estimate paleo-magnitudes
through comparisons with regressions of slip to MW. In light of the evidence above, such paleo-
magnitude estimates may substantially underestimate the true magnitude of earthquakes,
especially on structurally immature faults and in studies based on point displacements taken from
either very young and/or thick sedimentary sections. If that is the case, then the seismic risk to an
area may be underestimated. Finally, studies addressing the style and magnitude of off-fault
deformation have important implications for the built environment, particularly in terms of the
future development of microzonation maps for earthquake-resistant building design. Specifically,
it matters greatly if the expected displacement in a probabilistic design earthquake at a specific site
will be highly localized along a single, main fault strand, or highly distributed over a wide zone.
As discussed above, studies relating various parameters (e.g., fault structural maturity, detailed
structural geometry of the fault, sediment type, and thickness) that may control such deformation
patterns will provide the basic information necessary to properly evaluate expected patterns of
surface deformation in future large earthquakes. Ultimately, data derived from analysis of these
relationships for numerous earthquakes could lead to a statistically meaningful understanding of
the likelihood that any of these parameters—all of which could potentially be documented in
advance of future earthquakes—will generate more-discrete or more-localized surface slip at any
particular study site. In turn, these results could be used to construct microzonation maps that
would capture the expected style and distribution of surface deformation at a site, thus potentially
reducing damage to infrastructure in future earthquakes.

33

2.7 Conclusions
Comparison of slip measurements made at different scales using high-resolution
WorldView satellite imagery and COSI-Corr subpixel image correlation reveal significant
complexity in surface deformation patterns during the 2013 MW 7.7 Balochistan, Pakistan
earthquake. Specifically, fault slip measurements of geomorphic features along the main trace of
the 2013 Hoshab fault rupture and measurements of total surface deformation from COSI-Corr
analysis show that 56 +16/-15% of the deformation is localized on the main strand of the rupture,
indicating that almost half of the deformation was accommodated by distributed deformation in
the damage zone surrounding the fault. Estimates of cumulative strike slip on the Hoshab fault
suggest total sinistral motion of ~6–11 km, indicating that this is a structurally immature fault. The
~56% on-fault deformation is generally similar to the ratio of on-fault to off-fault slip expected for
a fault with this degree of structural maturity (Dolan and Haravitch, 2014). Thus, although the
structural maturity of the Hoshab fault appears to be the dominant control on the degree of slip
localization at the surface, large variations in along- and across-strike patterns and magnitudes of
on-fault versus off-fault deformation indicate that other factors also acted to control the behavior
of co-seismic displacement during the 2013 main shock. Our analysis of the relationship between
surface deformation patterns and the relative age and thickness of different types of fluvial and
alluvial sedimentary units relative to bedrock along the surface rupture
indicates that these factors exert a strong secondary control on patterns of surface deformation,
with more localized deformation in bedrock and older and thinner alluvial strata. Conversely,
surface deformation in younger and/or thicker fluvial and alluvial deposits tends to be much more
distributed.
34

These observations have basic implications for a range of disciplines, including the proper
understanding and use of geologic slip rates, especially as when used as inputs to probabilistic
seismic hazard assessments, and the mechanical behavior of near-surface materials during large
coseismic ruptures, which in turn has significant implications for the built environment and
potentially for radiation of strong ground motions. Moreover, if the controls on patterns of surface
slip localization and delocalization can be determined with sufficient confidence through
additional studies similar to this one, these results will constitute important inputs into future
studies of microzonation for the built environment, potentially demonstrating in advance where
surface slip will be more localized, and where it will be more distributed.

2.8 Figure Captions
Figure 1. Topographic map showing the location of the 24 September 2013 MW 7.7 Awaran main
shock surface rupture (red line). USGS locations and focal mechanisms for the main shock
(orange) and 28 September aftershock (gray) are shown, along with global CMT solutions for three
earlier strike-slip earthquakes (red) which occurred at the NE-termination of the 2013 rupture in
1990 (MW 5.5–5.9). Small orange circles show USGS aftershock locations for the period following
the 2013 rupture. Inset map shows the location of the Awaran rupture within the wider Arabia–
India–Eurasia collision zone. Yellow star shows the location of the Awaran mainshock. Yellow
arrow shows the NUVEL-1 plate velocity of eastern Pakistan (Indian plate), relative to Eurasia.
Blue arrows show GPS velocities from eastern Pakistan, relative to Eurasia (Mohadjer et al., 2010).

Figure 2. EW and NS displacement maps covering the 2013 main shock surface rupture,
calculated from the correlation of pre-earthquake and post-earthquake Landsat-8 satellite images
35

using COSI-Corr. Displacements are given in meters. USGS focal mechanisms for the main shock
(orange) and 29 September aftershock (gray) are shown. Red arrows show the end points of the
along-strike-slip profile shown in Figure 2b. Left-lateral slip displacements calculated from the
Landsat-8 correlation data are shown in green and gray. Red line shows slip measurements made
from correlation of SPOT5 satellite images, which span the 28 September aftershock. Orange star
shows the along-strike location of the main shock and the red star shows the 28 September
aftershock.

Figure 3. (a)–(c) WorldView images showing examples of geomorphic offsets with quality ratings
‘‘A,’’ ‘‘B,’’ and ‘‘C,’’ from best defined to least, respectively (see text for quality rating criteria).
White arrows show the locations of the offsets that were measured. In (a) the river bank at the
bottom of the slope can clearly be correlated across the fault and can be precisely restored by back
slipping one side of the feature relative to the other. Although the fault trace is wider where it
extends through the older alluvial fan to the north, the channel edge is offset cleanly, and the fault
trace is relatively structurally simple at this location. In (b), although the fault trace is wider and
more diffuse where it passes through the active alluvium and young fan sediments, the offset edge
of the young fan can readily be correlated across the fault. In (c) the rupture trace is highly diffuse
and it is relatively difficult to correlate the offset edges of the active channel edge where it is
incised into young fan material to the SW across the fault (Imagery Copyright DigitalGlobe, Inc.)

Figure 4. Examples of mapped surface material types. Figure 4a shows a section of fault where
active wash sediments (blue), young alluvial fans (yellow), older alluvial fans (orange), and
bedrock (burgundy) are present at the surface. The left image shows surface material
36

types mapped on WorldView imagery; the right image shows a color image of the same area.
Figure 4b shows active wash sediments, older alluvial fans, old consolidated alluvium (ivory), and
bedrock mapped on WorldView imagery (left), as well as a color of that same area
(right; Google Earth, 2013, 2014; Imagery copyright DigitalGlobe, Inc.).

Figure 5. Fault traces and zones of continuous deformation visible in optical imagery. Zones of
visible deformation range from (a) < 1 m to (c) ~800 m in width. The structural style of the rupture
traces range from (a) structurally simple to (b, c) structurally complex (Imagery Copyright
DigitalGlobe, Inc.).

Figure 6. Comparison of offset measurements as a function of distance along the rupture. Red line
represents offset measurements from COSI-Corr analysis of Landsat-8 data. Blue line shows offset
of geomorphic features measured along the main trace of the rupture as determined by visual
analysis of WorldView <0.6 m resolution satellite images. Shaded fields represent the maximum
and minimum reasonable slip values. The colors along the bottom of the figure represent
structurally simple (black) and structurally complex (red) sections of the fault.

Figure 7. Landsat-8 satellite image (bands 7, 5, 1), showing the geology of the NE-end of the 2013
main shock rupture. Black lines show active faults, and blue lines highlight certain rivers which
have been offset by the Hoshab fault. Multicolored pointers highlight offset drainage and geology
at different locations. See text for discussion.

37

Figure 8. Schematic cross section showing increasing sediment thickness basin ward of the range
front. Range of different configurations is shown by (a) where the fault traces comes to the surface
at the range front and (b) where the fault trace is located out in the alluvium to the southeast of the
surface rupture. We see examples of both along the surface rupture. In both instances, sediment
thickness increases basin ward as distance from bedrock increases, due to the reverse component
of slip that characterizes at least some of the earlier activity along the Hoshab fault (see text for
discussion). The slope of the bedrock/sediment contact shown in the diagram reflects the average
slope of the range front. Fault dip shown reflects the average dip of the Hoshab fault (~60°) inferred
from inversion of teleseismic waveforms (Jolivet et al., 2014). The general linearity of the surface
rupture trace as it crosses topography suggests that the fault maintains a relatively steep dip all the
way to the surface, as shown in the diagram.

Figure 9. Box and whisker plots represent the percent of total strain localized on the main trace of
the fault at individual measurement locations grouped by the type of surface material in which they
are located. Locations within obvious structural steps and discontinuities have been excluded. Red
lines represent the median of localization estimates in each group; blue boxes represent the 25th
and 75th percentiles, whiskers represent the 1st and 99
th
percentiles, and red crosses represent
outliers at the 99th percentile. When considering strain localization in all such measurements, there
is a general tendency for strain to be more localized in younger sediments, but in (a) the trend is
weak. If we use distance to the nearest bedrock outcrop as a proxy from sediment thickness,
however, we see that localization generally increases with increasing age of the surface material
in materials > 20 m (b) from the nearest bedrock outcrop (bedrock samples are shown for
comparison). We attribute this observation to increasing sediment incipient lithification with age,
38

resulting in sediment deposits that are stronger and therefore better able to localize coseismic
deformation. This effect is manifest in thicker sediments because they are less subject to the strain
localization patterns in the underlying bedrock.

Table 1. Mean Fault Slip and Surface Slip Ratios.

2.8.1 Data Repository Figure Captions
Table S1. Images used for visual analysis. Describes the platform (satellite), image identification
code, acquisition date, image band, and incidence angle for each WorldView or Landsat-8 image
used for visual measurement of offsets. Image identification codes correspond to the image names
given on the USGS EarthExplorer website or the DigitalGlobe website.

Table S2. Displacement measurements from COSI-Corr analysis of Landsat-8 imagery.
a
Easting
and northing values are given for UTM Zone 41 N, using the WGS84 datum.
b,c
Negative values
indicate left-lateral offset (fault-parallel measurements) or compression (fault-perpendicular
measurements).

Figure S3. COSI-Corr analysis of pre- and post-mainshock and aftershock SPOT5 imagery. NS
displacement map covering the NE-end of the 2013 rupture, calculated from pre- and post-
mainshock and aftershock SPOT5 satellite images. USGS focal mechanisms for the mainshock
(orange) and 29
th
September aftershock (gray) are shown. Red arrows show the end points of the
along-strike slip profile shown in (Figure 2b).
39

Table S4. Geologic offsets measured by visual analysis of WorldView imagery.
a
Easting and
northing values are given for UTM Zone 41 N, using the WGS84 datum.
b
Horizontal distance
measured from the SW tip of a generalized fault trace located at E608505, N2883357 (UTM Zone
41 N).
c
Significant sources of error are reported for each measurement: “Diffuse deformation”
indicates visible distributed deformation across the fault zone; “original geometry” indicates
uncertainty in the pre-offset geometry of the geomorphic feature being measured; “obliquity”
indicates that the piercing lines used to determine offset are at a high angle to the fault trace,
making it difficult to precisely restore them to their original geometry; “topographic effects” may
lead to misrepresentation of the true amount of offset due to the look angle of the satellite.
d
Distance to nearest bedrock outcrop is rounded to the nearest 10 m.
e
The field “Structurally
complex?” indicates whether or not the fault zone at the measurement is considered “structurally
complex”, as defined in the main text.
f
The field “Obvious structural control?” indicates whether
the measurement was located in an obvious zone of broad-scale structural complexity.
g
Fault-
perpendicular distance over which the measurement was taken. Widths ≤ 5 m are considered
“discrete” in this study and were included in our analyses.

40

CHAPTER 3:
3D surface deformation in the 2016 MW 7.8 Kaikōura, New Zealand earthquake from
optical image correlation: Implications for strain localization and long-term evolution of
the Pacific-Australian plate boundary

The following work is based on R. Zinke, J. Hollingsworth, and J. F. Dolan, “3D surface
deformation in the 2016 MW 7.8 Kaikōura, New Zealand earthquake from optical image
correlation: Implications for strain localization and long-term evolution of the Pacific-Australian
plate boundary” submitted to Geochemistry, Geophysics, Geosystems (manuscript number
2018GC007951).

3.1 Abstract
We generated dense, high-resolution 3D ground displacement maps for the 2016 MW 7.8
Kaikōura, New Zealand earthquake – the most geometrically and kinematically complex rupture
yet recorded – from stereo WorldView optical satellite imagery using a new methodology that
combines subpixel image correlation with a ray-tracing approach. Our analysis reveals
fundamental new details of near-field displacement patterns, which cannot easily be obtained
through other methods. From our detailed correlation maps, we measured fault slip in 3D along 19
faults at 500 m spacing. Minimum resolvable horizontal slip is ~0.1 m, and vertical is ~0.5 m. Net
slip measurements range from < 1 m to ~12 m. System-level kinematic analysis shows that slip on
faults north of the Hope fault was oriented primarily sub-parallel to the Pacific-Australian plate
motion direction. In contrast, slip on faults to the south was primarily at high angle to the plate
motion, and secondarily parallel to plate motion. Fault kinematics are in some locations consistent
41

with long-term uplift patterns, but inconsistent in others. Deformation within the Seaward
Kaikōura Range may indicate an attempt by the plate boundary fault system to geometrically
simplify. Comparison of published field measurements along the Kekerengu fault with our
correlation-derived measurements reveals that ~36% of surface displacement was accommodated
as distributed off-fault deformation when considering only field measurements of discrete slip.
Comparatively, field measurements that project previously linear features (e.g., fence lines) into
the fault over apertures > 5–100 m capture nearly all (~90%) of the surface deformation.

3.2 Introduction
The 2016 MW 7.8 Kaikōura, New Zealand earthquake produced one of the most spatially
and kinematically complex ruptures ever recorded. The mainshock occurred shortly after midnight,
local time, on 14th November 2016, near the town of Waiau in northern South Island, New Zealand
(Kaiser et al., 2017; Cesca et al., 2017; Nicol et al., 2018; Litchfield et al., 2018). The focal
mechanism indicated a non-double couple source, with significant right-lateral and reverse motion
(e.g., www.globalcmt.org/; Duputel and Rivera, 2017, Hollingsworth, et al., 2017, Kaiser et al.,
2017). The rupture propagated northeastward for approximately 1.5 minutes, with peak moment
release occurring at ~60 seconds or later (e.g., Cesca et al., 2017; Holden et al., 2017;
Hollingsworth, et al., 2017). Fault rupture with surface displacements > 1 m was reported along
more than 15 onshore and offshore faults (some of which were oriented ~90° to each other),
resulting in a total surface trace length of ~180 km (Stirling et al., 2017; Litchfield et al., 2018;
this study). Debate remains as to what role the subduction megathrust fault played in the
earthquake, though it is generally presumed that it, or some other deep thrust fault, contributed to
the overall moment release, and likely aided rupture propagation through the complex network of
42

faults (Bai et al., 2017; Cesca et al., 2017; Clark et al., 2017; Duputel and Rivera, 2017; Hamling
et al., 2017; Hollingsworth et al., 2017; Litchfield et al., 2018; Wang et al., 2018).
Immediately following the 14th November mainshock, teams of field geologists began
measuring surface deformation resulting from the rupture (Clark et al., 2017; Stirling et al., 2017;
Kearse et al., 2018; Langridge et al., 2018; Litchfield et al., 2018; Nicol et al., 2018; Williams et
al., 2018). These measurements provide key insights into the spatial distribution, kinematics, and
complexity of slip in the event. Due to the limitations of measurable geomorphic and linear cultural
features, however, the field measurements of displacement tend to be clustered, leaving large gaps
in their spatial coverage. Furthermore, many field measurements could only be projected into the
fault zone over relatively narrow (several-meter-wide) fault-perpendicular widths, thus potentially
missing a component of the total fault offset (Rockwell et al., 2002; Shelef and Oskin, 2010; Dolan
and Haravitch, 2014; Zinke et al., 2014b; Milliner et al., 2015, 2016b). To help overcome these
limitations, field measurements were often supplemented by remotely sensed data sets, including
light detection and ranging (lidar) and interferometric synthetic aperture radar (InSAR).
Simultaneously, geodetic and seismological studies examined broad-scale patterns of deformation
(Bai et al., 2017; Cesca et al., 2017; Hamling et al., 2017; Holden et al., 2017; Hollingsworth et
al., 2017; Kaiser et al., 2017; Morishita et al., 2018; Wang et al., 2018). Although measurements
from these studies helped to constrain coarse patterns of surface deformation, and to infer fault
structure and slip at depth, they lacked the spatial resolution necessary to characterize finer-scale
patterns of surface slip variability or details of the fault zone structure. The limitations of these
various geological and geophysical data sets highlight the need for a means of determining fine-
scale, spatially continuous patterns of surface deformation across broad spatial areas, which can
link detailed surface deformation patterns with the smoother slip distributions inferred at depth.
43

In this study, we take advantage of very high resolution (~0.5 m) stereo pre- and post-
earthquake WorldView optical satellite images to retrieve a homogeneous 3D ground-surface
displacement field in the vicinity of the 2016 Kaikōura earthquake rupture using a new optical
image correlation (OIC) methodology. These deformation maps provide the most detailed,
seamless, and comprehensive view of surface displacement to date, with independent
measurements computed every 32 m (with no smoothing imposed through regularization). From
these maps, we measured fault-parallel, fault-perpendicular, and vertical displacements along
mapped surface faults. These measurements allow us to determine the kinematics of the ruptured
faults, and in turn assess the kinematic consistency between the 2016 earthquake and long-term
patterns of fault deformation expressed in the landscape. Furthermore, our data allowed an
opportunity to compare OIC-derived estimates of near-field (distributed and localized)
displacement along the Kekerengu fault rupture, with detailed published field measurements of
fault-parallel slip (Kearse et al., 2018) to reveal patterns of fault slip localization and distributed
off-fault deformation (OFD). Highly detailed documentation of the near-field displacement pattern
is crucial for understanding the physics of how faults slip, as well as informing models of dynamic
fault rupture. More specifically, the Kaikōura earthquake provides a rare opportunity to investigate
the surface deformation pattern for one of the most complex earthquake ruptures ever observed.

3.2.1 Tectonic Setting
Northeastern South Island, New Zealand lies astride the Pacific-Australian plate boundary,
at the transition between the Hikurangi subduction margin to the northeast, and the dextral-oblique
Alpine fault to the southwest (Fig. 1; Reyners and Cowan, 1993; Holt and Haines, 1995; Barnes
et al., 1998; Wallace et al., 2012). The subducting Pacific plate underlies much of the 2016
44

earthquake rupture area, at depths as shallow as 20–30 km (Eberhart-Phillips and Bannister, 2010;
Williams et al., 2013). The Hikurangi Trough terminates east of the Kaikōura peninsula at
approximately 43°S, where the buoyant continental crust of the Chatham Rise is unable to subduct
beneath South Island (Reyners and Cowan, 1993; Wallace et al., 2012). Here, the tectonic regime
transitions into continental collision (Nicol et al., 1994; Pettinga et al., 2001; Eberhart-Phillips and
Bannister, 2010; Wallace et al., 2012).
The 2016 earthquake ruptured two distinct tectonic domains. North of (and including) the
Hope fault lies the Marlborough fault system (MFS), where northeast-striking oblique right-lateral
faults transfer strain between the Hikurangi subduction zone and Alpine fault (Fig. 1; e.g., Wallace
et al., 2012; Litchfield et al., 2014). The Northern Canterbury domain (NCD) lies south of the
Hope fault. Here, faults are more geometrically and kinematically diverse than in the MFS (Nicol
et al., 1994; Pettinga et al., 2001) reflecting the complex interplay of the Hikurangi-to-Alpine fault
strike-slip tectonics to the north, and oblique continental collision to the south, which causes
northeast-southwest oriented thrust faults (Fig. 1 inset; Nicol et al., 1994; Pettinga et al., 2001;
Litchfield et al., 2018; Nicol et al., 2018).

3.3 Methods and Observations
3.3.1 Determinations of 3D Deformation from Optical Image Correlation
Optical image correlation (OIC) has shown great utility in previous studies measuring
earthquake-induced surface deformation (e.g., Michel and Avouac, 2002, 2006; Leprince et al.,
2007a, 2007b, 2008; Ayoub et al., 2009; Wei et al., 2011; Hollingsworth et al., 2012, 2017; Avouac
et al., 2014; Barnhart et al., 2014; Zinke et al., 2014b; Avouac and Leprince, 2015; Milliner et al.,
2015, 2016a, 2016b). OIC processing tools, such as COSI-Corr (Leprince et al., 2007a), MicMac
45

(Rosu et al., 2015), and Ames Stereo Pipeline (Shean et al., 2016), provide fast and cost-effective
means for determining continuous maps of on-fault and off-fault 2D surface displacement with
limited decorrelation near the fault (in stark contrast with InSAR measurements, which typically
decorrelate close to fault ruptures if the strain gradient between neighboring pixels exceeds a phase
cycle). OIC is therefore capable of providing detailed near-field surface slip measurements where
piercing markers necessary for field measurement are unavailable, and in locations that are
otherwise inaccessible to field survey teams. The result is a precise, regularly sampled set of fault
displacements that can reveal details of along-strike surface slip variability, and in turn provide
insights into the earthquake rupture processes and underlying mechanisms controlling surface
deformation patterns (Milliner et al., 2016a). Furthermore, because the correlations capture both
on- and off-fault deformation, comparison of OIC-derived fault offsets with field measurements
(which, in many instances, capture only localized on-fault slip) can reveal spatial patterns of
distributed, off-fault deformation (Zinke et al., 2014b; Milliner et al., 2015, 2016b). When
combined with other remote sensing methods, such as InSAR and GPS, these correlations can be
used to refine static inversions for slip, especially with regards to the shallowest portions of the
crust (Xu et al., 2016). For earthquakes occurring prior to the availability of modern satellite
geodetic techniques (e.g., InSAR, GPS, and lidar; before c. 1992), OIC provides the only method
for remotely characterizing the ground deformation (e.g. Hollingsworth et al.; 2012; Milliner et
al., 2015; Marchandon et al., 2018).
Using a standard 2D OIC processing approach (COSI-Corr; see also Ayoub et al., 2015)
Hollingsworth et al. (2017) correlated pre- and post-earthquake Landsat8 images covering the
2016 rupture area. While providing robust measurements of broad-scale surface deformation
patterns, the coarse pixel size (15 m) of the Landsat8 sensor limits the ability to detect fine-scale
46

details of ground displacement. Furthermore, given the nadir (i.e. vertical incidence) acquisition
geometry of Landsat8 (and Sentinel-2) imagery, OIC can only resolve the horizontal (2D)
component of displacement, thus leading to its application predominantly in studying strike-slip
earthquakes (Hollingsworth et al., 2017). Because the 2016 Kaikōura earthquake produced large
(up to 10 m) vertical displacements in addition to large horizontal slip, properly accounting for
this vertical motion becomes critical for adequately characterizing the complex kinematics of the
ruptured faults (e.g., Litchfield et al. 2018). More generally, better constraint of the vertical
component of deformation is essential for extending the use of OIC to studying normal and thrust
earthquakes.
In this study, we greatly improved upon the methods used by Hollingsworth et al. (2017)
by applying an advanced workflow to retrieve both the horizontal and vertical displacement fields
from correlations of pre- and post-earthquake high-resolution stereo imagery. This methodology
provides highly detailed maps of 3D surface deformation without the need to combine multiple
sensors, or generate separate digital elevation models (DEMs) with which to precisely orthorectify
the pre- and post-earthquake satellite images (which are not always available, and yet are essential
for the successful correlation of non-stereo WorldView images using a traditional 2D correlation
approach).
We applied this technique to stereo WorldView satellite imagery (~0.5 m pixel), which is
much higher resolution than Landsat8 (band 8: 15 m), thereby allowing us to measure finer-scale
(up to ×30) patterns of deformation. Crucially, the WorldView images were acquired at oblique
(off-nadir) viewing geometries, thus preserving a stereo effect between the two images, which we
exploited using a simple ray tracing approach to precisely recover the vertical dimension of ground
47

motion. The precise details of the correlation approach are given in the supporting information
(Table S1; Text S2).
Despite the obvious power of this processing methodology, which has been applied to an
earthquake only once previously (in a proof-of-concept study documenting the 2010 El Mayor
Cucapah earthquake rupture, see Avouac and Leprince [2015]), the lack of available high-
resolution pre- and post-earthquake stereo images has thus far severely limited its potential
application for recent earthquake studies. The 2016 Kaikōura earthquake therefore represents the
first comprehensive application of this technique to an earthquake. With increasing demand for
multi-temporal stereo acquisitions in the future, this new methodology will help to facilitate more
precise and rapid 3D characterization of ground deformation globally, with application to a wide
range of Earth Sciences applications (e.g., Herman et al., 2015).
The final EW, NS, and vertical correlation results are presented in Figure 2 and Figure S3.
The correlations resulted in well-resolved values (with high signal-to-noise ratios) across much of
the rupture, providing spatially dense and continuous measurements of surface displacement
spanning fault-perpendicular distances of ~5–20 km across a total area of ~5,000 km
2
. The
correlations show spatially and kinematically complex patterns of surface deformation rarely
observed in past ruptures. The horizontal correlations contain only minor or no topographic
artifacts unaccounted for by our processing. Horizontal displacements across faults and landslides
on the order of centimeters are detectable as gradients in the correlation maps. The vertical
deformation field shows uplift and down-dropping of the ground-surface along faults, folds, and
landslides. Vertical deformation on the order of a few 10s of centimeters is resolvable (the vertical
stereo component is less well resolved than the horizontal because the satellite incidence angles
are generally not oriented far off-nadir, i.e., < ~30°).
48

Correlation proved challenging in areas of very high topographic relief, where steep
mountainous slopes may be under-sampled due to the oblique viewing geometry of the satellite
(equivalent to foreshortening and shadowing effects in InSAR), and where talus-covered hillslopes
tended to crumble (particularly along the crest of the Seaward Kaikōura Range). Landsliding,
particularly along the Papatea fault (e.g., Massey et al., 2018), led to decorrelation or artificially
widened the apparent fault deformation zone where slumping transported coherent blocks of earth
(Fig. 2). Agricultural change (harvesting, crop growth, replanting) also caused decorrelation and
spurious values, especially in the southwestern (Emu Plain), and northeastern portions of the
rupture. Finally, the changing position of shadows from one time of day to the next biased
correlation values in a handful of settings (appearing as topographically correlated noise). This
effect is most notable within a copse of trees near the junction of the Kekerengu-Jordan Thrust,
Papatea, and Waiautoa faults, where a positive excursion in vertical correlation values is due in
part to tectonic uplift, and in part to shadow artifacts (Fig. 1, 2).

3.3.2 Fault Trace Mapping
We mapped the fault traces by visually identifying persistent gradients in our correlation
results (Fig. 1, 2), and by noting discernable surface faulting in the post-event ortho-images. Our
mapping was guided in part by the Kaikōura surface rupture map provided in the New Zealand
active faults database (https://data.gns.cri.nz/af/ [Langridge et al., 2016]). Their mapping
exclusively included brittle ground-surface breaks along discrete faults identified in field
investigations (for which we reserve the term “ground-surface rupture”). Along some stretches of
fault, it was ambiguous whether the gradients in our correlation maps indicated fault displacement
manifested as discrete ground-surface rupture, or rupture in the shallow subsurface that did not
49

reach the ground-surface along discrete planes. Because of this ambiguity, we include both cases
in our fault trace maps. The spatial complexity of the fault ruptures in the Kaikōura earthquake
(e.g., en echelon shear zones and subsidiary fault strands separated by only a few meters), required
us to define what constituted a coherent, mappable fault strand. Mapping fault strands > 500 m in
length, separated from neighboring strands by at least 500 m, provided the optimal balance
between preserving spatial complexity and resolving robust slip measurements across the fault. In
a few cases, notably along the junction of the Kekerengu and Jordan Thrust faults, we mapped the
fault strands with closer spacing. We also mapped faults as separate strands where the traces
changed strike abruptly or intersected other faults. For consistency, we mapped the fault traces as
line segments with one point every ~100 m.
With these guidelines, we mapped 19 faults in 48 segments (Fig. 1). Segment lengths
ranged between 0.5–26.5 km. All but one of the faults had been previously identified by field
teams; one fault, which we term the Snowflake Spur fault, was not identified in the field (see
Section 2.3). Along some of the faults, sharp and continuous deformation gradients in our
correlations allowed us to extend the length of fault rupture beyond the limits of mapped surface
rupture identified in the field (e.g., the Fidget fault between 173°40′ E and 173°49′ E).
Conversely, some minor stretches of surface rupture reported by field teams were not
discernable in our correlation results, likely because the displacements were too small or the
surface rupture lengths too short. These faults include, most notably, the Seaward and Conway
stretches of the Hope fault, and the Heaver’s Creek fault. Inclusion of these faults in our surface
slip analyses would likely make little difference to our final results, as collectively, their surface
ruptures are no longer than a few kilometers, and their maximum displacements are almost all < 1
m. The Needles, and other offshore faults, were not measurable with our optical data, and are
50

therefore not considered. Landsliding, evinced by decorrelation or displacement of isolated
patches, was ubiquitous throughout our correlations, especially in areas of high relief. Care was
taken not to map landslide scarps as faults. Earthquake-related folding was also observed, most
notably, north of the eastern stretch of The Humps and west of the northern Leader faults.

3.3.3 Identification and Mapping of the Snowflake Spur Fault
We identified the Snowflake Spur fault (Fig. 1) based on strong, persistent, EW and NS
correlation gradients (Fig. 2; Fig. S3). The Snowflake Spur fault trends northeast-southwest
(~210°), roughly following the general trend of the Jordan Thrust and Manakau faults (Fig. 1) to
its terminus at or near the lower, unruptured segment of the Kowhai fault (Van Dissen and Yeats,
1991; Langridge et al., 2016). Displacement across the fault is primarily right-lateral with minor,
locally variable compressional or extensional components of slip. The vertical sense of
displacement was more difficult to discern and switched from generally SW-side-up in the south,
to NW-side-up in the north (Fig. 2, 4). As mapped, the Snowflake Spur fault is not an artifact of
unremoved topographic artifacts in our correlations, because it cuts across topography, including
Snowflake Stream and Snowflake Spur. The southwestern ~2 km of the fault does, however,
follow a ridge line (the Homestead Spur), suggesting that the fault lies within the hanging wall of
a deeper structure responsible for the topography. The fault likely went unidentified in field studies
due to its location in a steep, and scree-covered remote area in the Seaward Kaikōura mountains.

3.3.4 Measuring Fault Offsets and Fault Zone Widths
To constrain the amount and spatial variability of slip along each mapped fault, we
measured offsets from projected correlation measurements at regular intervals. For each offset
51

measurement, we collected a swath of displacement values from our three correlations, which we
stacked into 1D profiles and filtered with a running median filter. We then projected the
displacement profiles into fault-parallel, fault-perpendicular, and vertical directions, relative to
local fault strike (Fig. 3). The swath dimensions were 500 m along strike and 6 km perpendicular
to strike (extending 3 km on either side of the fault). Profiles were spaced every 500 m along the
length of each fault segment. Thus, each measurement represents the average surface displacement
on a 500 m-long reach of the fault. These dimensions provided the optimal balance between
resolution and signal robustness for our correlations. Measurements were collected in 200 m-
along-strike swaths along several of the northeastern faults (e.g., the Kekerengu fault), where data
density was especially high (Fig. 2). For each stacked profile, we measured displacement across
the fault zone by projecting the displacement values from the far-field into the fault zone using a
linear fit function (Fig. 3, insets; e.g., Milliner et al., 2015). Where the distance between subparallel
faults was less than 3 km, we only measured displacement across one of the faults at a time, to
avoid overestimating the total deformation across the fault system.
In most cases, the near-fault surface deformation formed > 50 m-wide zones of distributed
deformation. Within these zones, deformation is accommodated not only by discrete, brittle
surface faulting, but by subsidiary faulting, warping, granular flow, and/or local block rotations
(Ben-Zion and Sammis, 2003; Shelef and Oskin, 2010; Zinke et al., 2014b; Milliner et al., 2015,
2016b). The fault zone is evident as “rollover” in our displacement profiles, where displacement
values curve into the primary fault trace (Fig. 3 insets). Where fault zone rollover occurred, we
ignored the reduced displacement values within the fault zone, instead projecting our displacement
profiles into the fault from the far-field to capture the full offset across the fault. Non-tectonic
phenomena such as landslides, clouds, and shadows, as well as minor uncorrected topographic
52

residuals, led to spurious local values of apparent deformation in or near the fault zone. Such
phenomena were similarly ignored to provide the most reliable estimates of total surface
displacement. We refer to the width of ignored values as the “measurement aperture” to distinguish
these values from true fault zone width measurements. In the absence of landslides, clouds,
uncorrected topographic residuals, or other noise in the correlations, the measurement aperture
equates to the fault zone width. Based on the typical noise level of our displacement measurements,
we found that fault zone width could be consistently measured with a precision of ±50 m, though
resolution was better in some locations (e.g., the Kekerengu fault). To determine the measurement
aperture, and fault zone width, we simultaneously considered the profiles for all three slip
components (fault-parallel and -perpendicular, and vertical).

3.3.5 Offset Measurement and Fault Zone Width Analysis
In all, we collected 531 measurements of fault offset at 500 m spacing (Fig. 3; Table S4).
Offsets range up to 12.0 ± 0.7 m of net slip, with a mean of 3.2 m and median of 2.0 m. The MFS
faults (north of the Hope fault; Fig. 1) had the highest average slip values: ~5.1 m net slip, with
offsets commonly > 9 m. The largest net displacements were on the Kekerengu (maximum 12.0 ±
0.7 m) and Papatea (maximum 11.0 ± 0.7 m) faults. Along the NCD faults (south of the Hope
fault), displacements were generally smaller, commonly between 1.0–2.5 m, with the north Leader
fault having the highest net slip value (5.5 ± 0.5 m).
System-wide, fault zone widths ranged from < 50 m to ~1.5 km wide (mean 370 m), based
on 302 measurements (biased measurements excluded; Fig. 3; Table S5). Fault zone widths are
overall slightly larger in the NCD: the median fault zone width for all NCD faults is 350 m,
compared to a median fault zone width of 300 m for all MFS faults. Given that the NCD faults are
53

generally less structurally mature, and more geometrically complex than the MFS faults, we might
expect the NCD fault zone widths to be larger than those along the more structurally mature
Kekerengu fault.

3.3.6 Kinematic Analysis
Assuming that the hanging wall and footwall moved as coherent blocks with negligible
internal deformation across a roughly planar fault, we can use the fault-parallel, fault-
perpendicular, and vertical slip components to determine the fault plane orientation and 3D
orientation of the slip vector at each measurement location. The fault strike is known from the
orientation of the local fault trace, and the fault dip can be inferred from the ratio of vertical to
fault-perpendicular slip. The slip vector trend is a function of the ratio of fault-perpendicular to
fault-parallel slip (the resultant of which defines total horizontal slip). The slip vector plunge is
inferred from the ratio of vertical slip to total horizontal slip. Refer to the supporting information
(Fig. S8) for a more complete description this methodology. We expressed all fault planes and slip
vectors in a lower-hemisphere projection (Fig. 4). Calculation of fault plane and slip vector
orientations in this way is sensitive to subtle differences in the ratios of the three slip components,
especially in cases where fault-perpendicular and vertical slip are small, and subject to noise in the
deformation profiles (Fig. 3 insets). On the whole, however, our calculations lead to a coherent
picture of near-surface fault plane attitude and slip vector orientations for most of the studied faults.
We determined the fault plane and slip vector orientations for each of our 531 offset
measurements (Table S9). In typical fault kinematic studies, displacement may be difficult to
determine, and all points are typically assigned equal weight. For our analyses, we scaled our slip
measurements in proportion to the net slip, such that larger net displacement values are more
54

represented than smaller ones. Generally, our kinematic orientation measurements (summarized
for each fault in Fig. S10) reflect the broader deformation patterns shown in Figure 4, and agree
with broad-scale field observations of deformation and uplift (Clark et al., 2017; Stirling et al.,
2017; Kearse et al., 2018; Langridge et al., 2018; Litchfield et al., 2018; Nicol et al., 2018;
Williams et al., 2018). Deviations from the overall fault plane attitudes and kinematics stem
primarily from local fault structural complexities, such as restraining or releasing bends and steps,
which occur at all observable length scales (≤ 100 m to > 10 km). In rare instances (e.g., the Corner
Hill fault), the surface displacement data were too noisy to robustly determine the fault kinematics
using our approach. These few instances (noted in Fig. S10) have relatively few (< 5)
measurements, and small fault-perpendicular or vertical displacements (< 0.5–1.0 m). Because of
their small displacements, the effect of these poorly constrained kinematic measurements on our
overall interpretations is insignificant. For the higher-displacement faults (net slip typically > 1.0
m) that constitute most of our data, offset measurements were more accurate (i.e., less subject to
noise) and our determinations of kinematics were consequently more robust. We expect the utility
of this method to increase as measurement techniques become more accurate.

3.4 Discussion and Implications
3.4.1 Consistency of Rupture Patterns with Long-term Kinematics: Observations
Our 3D deformation maps, offset measurements, and kinematic analyses allow us to assess
the consistency of the 2016 rupture patterns with longer-term (multi-earthquake) patterns of
tectonic deformation expressed in the landscape. In the northeastern portion of the rupture, the
Kekerengu fault exhibited primarily right-lateral slip with a component of northwest-up reverse
motion (Fig. 2, 4). These kinematics are consistent with the long-term history of the fault, which
55

is characterized by primarily dextral shear with a subordinate NW-side-up reverse component, as
shown by the ~300 m right-lateral offset of the Pleistocene Winterholme gravels and the
Kekerengu River, and uplift of the northeastern Seaward Kaikōura mountain range (Little et al.,
2018; Kearse et al., 2018).
To the southwest, near its junction with the Papatea and Fidget faults, the Kekerengu fault
transfers slip onto the Jordan Thrust (Fig. 1). The Jordan Thrust is part of the same fault system as
the Kekerengu fault, and typically helps drive uplift of the Seaward Kaikōura Range by
accommodating NW-side-up dextral-reverse motion (Fig. 1; Van Dissen and Yeats, 1991). In the
2016 earthquake, however, the sense of throw was opposite to the long-term trend: the Jordan
Thrust fault experienced primarily normal-dextral, SE-side-up motion (Fig. 2, 4). This anomalous
sense of slip resulted from southward movement of the Papatea block (Fig. 4), which experienced
up to ~7.3 m of left-lateral displacement, and ~9.0 m of W-side-up vertical slip along the Papatea
fault (Hamling et al., 2017; Langridge et al., 2018; this study). This Papatea block motion is
attributed to either: (1) the extrusion of the Papatea block to accommodate space between the Hope
and Jordan Thrust faults (Hamling et al., 2017; Langridge et al., 2018); or (2) convergence of the
Papatea fault with a deeper (blind) north or northwest-dipping detachment underlying the Papatea
block (Van Dissen and Yeats, 1991; Cesca et al., 2017). In either case, the fact that the Seaward
Kaikōura Range northwest of the Jordan Thrust is substantially higher (and therefore building
faster) than the mountains of the Papatea block indicates that the Papatea fault is less active and
moves slower, on average, than the Jordan Thrust fault. The large Papatea fault slip observed in
2016 must therefore have been a relatively rare event.
Further to the southwest, the Upper Kowhai and Manakau faults splay off the Jordan Thrust
fault and straddle the crest of the Seaward Kaikōura Range (SKR; Fig. 1). Despite some
56

decorrelation in this area (see section 2.1), our 3D correlations allowed us to resolve slip in most
locations along each of these faults, including places where field and lidar data were limited or
unavailable (Fig. 2–4), and where field access was hampered by the rough, steep topography. Our
kinematic analyses demonstrate that the southern of the two faults, the Upper Kowhai,
accommodated NW-side-down, normal-dextral motion. In contrast, the (northern) Manakau fault,
accommodated SW-side-down, dextral-normal motion. The sense of slip along these faults was
confirmed by helicopter field reconnaissance (Kearse et al., 2018), and is consistent with long-
term slip patterns, indicated by uphill-facing scarps preserved in the pre-2016 geomorphology, as
observed in pre-earthquake stereo aerial photography, the LINZ 8 m DEM
(https://data.linz.govt.nz), and post-earthquake lidar. Interestingly, despite their positions near the
top of the fastest-rising mountain range in northern South Island (Van Dissen and Yeasts, 1991),
slip along these two faults produced relative down-dropping of a graben along the range crest
(Fig.2; Fig. S3). Whereas gravitational spreading (as sackungen) could explain the extensional
behavior of these faults, it cannot explain the significant right-lateral motion accommodated by
each of them, nor their relation to the Jordan Thrust fault. Instead, we invoke a model in which
uplift of the Seaward Kaikōura Range is primarily controlled by thrusting and associated range-
scale anticlinal folding (possibly along a blind ramp at the base of a fault propagation fold; Van
Dissen and Yeats, 1991). In accordance with this model, the Manakau and Upper Kowhai faults
may act as transtensional structures, accommodating extension along the crest of the fault
propagation fold, while simultaneously transferring right-lateral strain northeastward from the
Hope fault onto the Jordan Thrust-Kekerengu fault system.
Prior to the 2016 rupture, the Snowflake Spur fault was only subtly expressed in the
topography, suggesting it has a relatively slow slip rate. Geomorphic evidence for a pre-existing
57

structure includes colinear, possibly right laterally offset ridges and bedrock channels visible in
the 8 m LINZ DEM. The dextral kinematics and northeast-southwest orientation of the fault are
consistent with other nearby faults within the Seaward Kaikōura Range, including the Manakau
and Upper Kowhai faults that ruptured in 2016, and the Fyffe and lower Kowhai faults that did not
rupture (Van Dissen and Yeats, 1991; Rattenbury et al., 2006). Right-lateral slip along the
Snowflake Spur fault averaged ~1.1 m, and generally increased northeastward, reaching a
maximum of ~2.2 m near the southern end of the Manakau fault. The kinematics and orientation
of the Snowflake Spur fault suggest that it likely acts as part of the same system as nearby faults
in the Seaward Kaikōura Range, transferring slip from the Hope fault to the Jordan Thrust-
Kekerengu system. The fact that the southwestern ~2 km of the Snowflake Spur fault follows the
crest of a ridge line (the Homestead Spur) suggests that the fault lies within the hangingwall of a
deeper structure that is likely responsible for uplift of the Seaward Kaikōura Range.
South of the Hope fault, uplift along the NCD faults generally correlate with topography
(e.g., compare Fig. 1 with Fig. 2). For example, the reverse-sinistral Whites fault ruptured through
a range of low mountains north of Oaro, generating W-side-up movement and accommodating
uplift of the highest summits of the range. The Whites fault does not appear to be the dominant
fault within the area, however, as it cuts across topography and does not consistently correspond
with deflected streams or range fronts. The Hundalee fault bounds the southern edge of these low
mountains. North-side-up uplift observed in 2016 along the Hundalee is therefore consistent with
previous kinematics. Similar to the Whites fault, the Stone Jug fault cuts through hilly or
mountainous terrain, but the Stone Jug fault was evident in the 8 m LINZ DEM in some places as
scarps and valleys. Loci of uplift to the southwest and east of the Stone Jug fault correspond with
preexisting topographic highs. Our correlations also captured fault-related uplift and coseismic
58

folding in the Mt. Stewart area, west of the northern Leader fault, and north of the eastern stretch
of The Humps fault (Fig. 2). Uplift produced by faulting and folding in this area in 2016 is
consistent with long-term uplift patterns expressed as high hills (Nicol et al., 2018; this study).
Though we note that the topography of the NCD is at least partially controlled by bedrock structure
that pre-dates the Neogene faulting we investigate here (Rattenbury et al., 2006; Nicol et al., 2018),
the uplift patterns resulting from the 2016 earthquake evince the influence of active faults in
controlling the relief of this area. Thus, despite relatively slow slip rates and long recurrence
intervals along the NCD faults (Nicol et al., 2018; Litchfield et al., 2018), the correspondence of
the fault traces with the topographic features described above suggests that 2016 rupture patterns
were largely consistent with longer-term patterns of tectonic and landscape deformation.

3.4.2 Consistency of Rupture Patterns with Long-term Kinematics: Implications
The close link between long-term uplift patterns, as expressed in the pre-2016 topography,
and the co-seismic uplift pattern produced in the earthquake along most faults may help geologists
better predict potential rupture patterns in complex fault systems. Given that structural
complexities along faults are thought to play a significant role in controlling the start and end
points of earthquake ruptures (e.g., Wesnousky, 1988; Stirling, et al., 1996; Wesnousky, 2006),
the Kaikōura earthquake was surprising for its ~180 km-long, continuous yet highly segmented
rupture pattern. This was especially so in the NCD region, where the active faults are more
discontinuous, less well expressed in the geomorphology, exhibit lower slips rates, and are
considered less structurally mature than the major plate boundary faults to the northeast, such as
the Kekerengu fault (Kearse et al., 2018; Litchfield et al., 2018; Nicol et al., 2018). From a seismic
hazard perspective, a single rupture linking all the broken faults in the 2016 earthquake would not
59

have been expected prior to the earthquake. Nevertheless, consistency between the long-term and
short-term uplift patterns demonstrates that faults in complex networks (such as those in the
northern NCD) need not necessarily change their kinematics in order to accommodate rupture
propagation in a geometrically complex rupture. Detailed characterization of the 2016 Kaikōura
rupture is therefore of great importance to our mechanical understanding of how faults slip, and
consequently how ruptures can propagate (or not) through complex fault networks over multiple
earthquake cycles. These observations have fundamental implications for the potential collective
rupture of multi-segmented faults and fault networks (e.g. Milliner et al., 2015), which should
therefore be considered in fault rupture scenarios.
A second observation of general importance for predicting future fault activity comes from
the anomalous motion of the Jordan Thrust and Papatea block during the 2016 earthquake. It is
generally assumed that although the relative components of fault movement (e.g., horizontal to
vertical slip ratio) can vary slightly from earthquake to earthquake, the overall kinematics of a fault
remain roughly constant through time, especially over geologically short time periods such as the
Holocene. Such consistent behavior results in the formation of offset landforms, the interpretation
of which forms the basis for tectonic geomorphologic analysis. However, the anomalous slip along
the Jordan Thrust fault in the 2016 Kaikōura earthquake suggests that in some cases long-term
indicators of fault kinematics (e.g., topography) may not adequately predict the sense of slip in
every future earthquake, and that kinematic switches may occur without broad-scale tectonic
reorganizations. Alternatively, evidence of past kinematic reversals identified in the geologic
record (e.g., sets of slickensides with different orientations, as in the 2014 MW 7.7 Balochistan,
Pakistan earthquake [Platt et al., 1988; Zinke et al., 2014b]) could suggest that a fault previously
60

participated in complex ruptures. Identification of such phenomena could therefore be of
significance to seismic hazard assessment.

3.4.3 The Snowflake Spur Fault and Controls on Rupture Propagation
Prior to identification of the Snowflake Spur fault, the largest distance between surface
rupture end points in the 2016 earthquake was > ~12 km, between the northern tip of the Whites
fault and the southern tip of the Upper Kowhai fault. With identification of the Snowflake Spur
fault, this gap decreases to ~4.2 km. The largest gap in surface rupture extent then becomes ~4.5
km between the eastern tip of the Hundalee fault, and the southern tip of the Whites fault (this
distance possibly decreases if offshore rupture of the Hundalee fault is considered [Williams et al.,
2018]). Previously, discontinuous ground-surface fault rupture was mapped along the Fidget fault,
however, rupture appears to be more continuous (at least in the shallow subsurface) based on our
north-south correlation results (Fig. 2; Fig. S3).
The distance between fault tips has a direct effect on the ability of a rupture to propagate
or not between segments (e.g., Wesnousky, 2006). In the 2016 Kaikōura earthquake, continuous
rupture propagation through a complex system of upper plate faults was permitted by either: (1)
rupture of deeper connecting structures such as the subduction megathrust, or a shallowly dipping
midcrustal detachment (e.g., Cesca et al., 2017; Hamling et al., 2017; Hollingsworth et al., 2017;
Litchfield et al., 2018); (2) stress transfer between the upper plate faults themselves (e.g., Hamling
et al., 2017; Nicol et al., 2018); or (3) some combination of these. Although rupture propagation
along a deeper structure is likely, our mapping of the surface rupture (including rupture in the
shallow subsurface, which may not be identifiable in field studies as discrete breaking of the
ground surface) substantially reduces the gap between the rupture end points of upper plate faults.
61

This may suggest that stress transfer between upper plate faults indeed played a prominent role in
rupture propagation. For example, static stress modeling by Hamling et al. (2017) indicated that
slip on the Humps and Hundalee faults may have increased the Coulomb failure stress along the
Upper Kowhai fault It is unlikely, however, that static stress transfer between the Whites fault and
the Snowflake Spur fault aided in rupture propagation across the Hope fault: Although the Whites
and Snowflake Spur faults are approximately colinear, the Whites fault is sinistral and the
Snowflake Spur fault is dextral. Such a configuration would not be ideal for static stress triggering.
Instead, rupture may have transferred across the Hope fault by dynamic stress triggering, or
through stress transfer along a deeper structure, such as the subduction interface.

3.4.4 Rupture Propagation Between Tectonic Domains
Using our displacement data, we tested whether the slip kinematics in the 2016 earthquake
were fundamentally different between the MFS and NCD, and whether the complex fault
movements in both the MFS and NCD collectively accommodate net plate-parallel motion.
Specifically, we grouped our kinematic data into faults from the MFS and NCD (Fig. 4) and scaled
our kinematic data values to account for the relative differences in net slip associated with each
measurement.
To a first order, our data show that patterns of strain accommodation within the MFS are
simpler than in the NCD (Fig. 4). In the MFS, surface deformation is mostly oriented NE-SW, and
accommodated on faults that generally strike NE-SW. A notable exception to this is the NW-SE
slip accommodated by the S-SE striking Papatea fault (Fig. 4). In contrast, surface deformation
patterns in the NCD are highly variable, with 3D deformation vectors arrayed in a diversity of
orientations. Here, strain was accommodated through faulting, folding, and block rotation. NCD
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faults cluster into two sets: (1) a NE-SW striking set, which mostly accommodated right-lateral
shear; and (2) a NW-SE set that accommodated left-lateral shear (Fig. S10). These observations
are consistent with those of Nicol et al. (2018), whose kinematic analysis of fault rupture in the
NCD was based primarily on field data.
Our kinematic analyses of offset measurements allowed us to more finely determine the
surface slip patterns expressed in each domain. Rose diagrams in Fig. 4 show the scaled slip vector
orientations for faults in the northeastern MFS, and southwestern NCD domains, as well as the
plate motion vector (~260°; Beavan et al., 2002; DeMets et al. 2010). In the MFS, the largest
component of motion is oriented at ~250°, subparallel to the plate motion direction. In contrast,
slip vector orientations in the NCD are bimodal. Our kinematic analyses indicate that the primary
sense of shear across the NCD faults was oriented ~325° (see NCD rose diagram in Fig. 4). This
motion was primarily accommodated by left-lateral strike-slip along the NE-SW to N-S oriented
structures (i.e., the Leader, Stone Jug, and Whites faults) and by thrusting along the Hundalee fault.
A secondary component of motion along the NCD faults was oriented at ~250–260°, almost
perfectly parallel to the plate motion direction. This component of shear was primarily
accommodated by right-lateral slip on the E- to NE-oriented faults (i.e., The Humps, and Conway-
Charwell faults), excepting the Hundalee fault. These observations are again consistent with recent
field studies (Nicol et al., 2018; Williams et al., 2018). Together, the opposite kinematics of the
(reverse-dextral) northeast-oriented faults and (reverse-sinistral) north-northwest-oriented faults,
as well as the ~60° difference between groups of slip vector orientations, indicate that the ruptured
NCD faults behave as a conjugate fault system accommodating transpression, with an overall slip
direction of ~318°.
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These results demonstrate that despite the different fault orientations within each domain,
the ruptured faults composed coherent networks in which kinematics were generally similar,
corresponding with fault orientation (e.g., E-striking faults accommodated EW slip). In contrast,
the overall patterns of slip were fundamentally different between the two domains, reflecting the
broader tectonic roles played by each. Specifically, the MFS generally accommodates strike-slip
parallel to the Pacific-Australian plate motion direction, whereas the NCD is a complex transition
zone between the primarily dextral faults to the north and thrust faults to the south accommodating
continental collision where the Hikurangi subduction margin terminates against the buoyant
Chatham rise (Fig. 1). Despite the fundamentally different kinematics of these domains, the 2016
rupture propagated from one to the other, resulting in substantially greater moment release than
would have occurred had the rupture been arrested within the NCD. Care should therefore be taken
when considering similar differences in tectonic domains as barriers to rupture propagation for the
purposes of seismic hazard assessment (a conclusion shared by Litchfield et al., 2018).

3.4.5 Implications for the Evolution of the Pacific-Australian Plate Boundary in Northern
South Island, New Zealand
We also note the rupture of the Manakau, Upper Kowhai, and Snowflake Spur faults, as
well as potential future activity along other faults in the SKR, may represent at attempt by the plate
boundary fault network to geometrically simplify the transition between the Hope fault and Jordan
Thrust-Kekerengu fault system. Specifically, ~23 mm/yr of relative plate motion is transferred
from the Hope fault onto the Jordan Thrust-Kekerengu fault system (Van Dissen and Yeats, 1991;
Langridge et al., 2003; Little et al., 2018). At their point of nearest intersection, the Hope and
Jordan Thrust faults differ in strike by ~43°, forming a mechanically unfavorable kink across
64

which strain is transferred (Fig. 1). If the faults within the SKR begin accommodating increasing
amounts of right-lateral strain, the southern Jordan Thrust fault may become less active. Thus, the
kink angle formed may decrease to as little as ~30° between the Hope fault and faults of the SKR
(west of the Jordan Thrust), making this zone of strain transfer mechanically more favorable, and
simplifying the geometry of the plate boundary in this area.

3.4.6 Off-Fault Deformation: Analysis
We assessed the degree to which surface deformation along the Kekerengu fault is
localized within a narrow, high-strain fault core, or expressed as distributed, off-fault deformation
(OFD). To do so, we compared our correlation-derived measurements of fault offset (which
capture the total surface displacement across a fault) to field measurements of fault slip (which
capture deformation across various fault-perpendicular apertures).
Our analyses focus on the Kekerengu fault, for which many well-documented field
measurements are available (Kearse et al., 2018), and along which our image correlations provided
consistently dense coverage along the Kekerengu fault. To ensure the highest accuracy possible in
calculating OFD, we collected correlation-derived offset measurements and fault zone width
measurements at 200 m intervals (Table S6, S7). The detailed field measurements provided in the
Kearse et al. (2018) data set consist of 99 well-documented offsets along the Tinline Downs,
Kekerengu, Heaver’s Creek, Jordan Thrust, and Upper Kowhai faults. Their measurements are
based on various geomorphic and man-made features, such as fence lines, roads, paleoseismic
trench margins, tree roots, and fluvial channels. Some of their measurements were unprojected, or
projected into the fault trace over only a very short (< 5 m) fault-perpendicular distance. These
unprojected measurements represent the discrete component of slip across the fault and include
65

little or no OFD. Other measurements reported by Kearse et al. (2018) were projected into the fault
trace across measurement apertures of various widths (typically < ~50 m, and up to ~1.8 km) along
offset and warped linear features (e.g., fence lines). Because coseismic surface displacements can
be expressed over fault-perpendicular distances of meters to kilometers, these projected field
measurements capture varying amounts of OFD, thus complicating their comparison with the
optical correlation data. To finely examine patterns of OFD, we therefore sorted the Kearse et al.
(2018) data into three categories: (1) all field measurements, regardless of how they were projected
(or not); (2) a set of “discrete” measurements, which capture slip within a narrow, 0–5-m-wide
zone of faulting, typical of most field studies; and (3) a set which includes only measurements that
were projected into the fault over some distance (> 5 m).
Our OFD calculations follow the methods of Milliner et al. (2015, 2016b). We calculated
OFD by subtracting each field measurement from the nearest optical correlation offset value.
OFD% is the ratio of each OFD measurement to the corresponding image correlation. We
restricted our OFD analysis to consider only measurements of fault-parallel slip, because fault-
perpendicular slip estimates were not reported in the field, and vertical slip measurements were
relatively sparse and subject to larger uncertainties. Where multiple fault strands were present, we
considered only field measurements along the highest-slip, primary fault strand (resulting in a
slightly more conservative comparison [e.g., Milliner et al., 2015]). Additionally, where multiple
field measurements fell within the 200-m width of a COSI-Corr measurement, we took the median
value of the field measurements to account for the fact that profiles we used to measure offset were
stacked over 200 m along-strike lengths. Upon testing, we found that for the data sets presented
here, our choice of binning method did not make a substantial difference in the final result (Table
S11).
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We first considered the full set of both unprojected and projected Kekerengu main-strand,
fault-parallel measurements, consisting of 81 measurements in 56 bins. The median OFD value
was 1.8 m (17.6%), and mean was 2.0 m (18.0%). Subdividing our data set into discrete-only field
measurements yielded 36 field measurements in 28 bins, with a substantially larger median OFD
value of 3.5 m (35.8%) and mean of 3.9 m (37.8%). Finally, consideration of only the 36 projected
field measurements (in 32 bins) resulted in a median OFD value of just 0.6 m (6.4%) and mean of
1.3 m (12.7%). Our observations that the discrete-only set of field measurements yield higher
values of OFD than the projected-only field measurements is consistent with independent (field
and structure-based) observations showing a similar nonlinear decrease in OFD with increasing
distance from the fault (e.g., Rockwell et al., 2002; Shelef and Oskin, 2010).
We note that a few field measurements were larger than the nearest correlation
measurement (Fig. 5). Some of these values represent local maxima in surface slip that are
smoothed over in our correlation measurements by both the 32×32 pixel correlation window size,
and the 200 m along strike stacking length of our correlation measurement profiles (Zinke et al.,
2014b; Milliner et al., 2015). At either end of the slip profile (Fig. 5), however, the Kekerengu
fault is overlapped by the Jordan Thrust and Tinline Downs faults. Therefore, in measuring offsets
from our correlation maps, every attempt was made to capture slip across only the Kekerengu fault,
without influence from the neighboring faults (i.e., the Jordan Thrust and Tinline Downs faults).
Because of the proximity of these faults (typically < 200 m), however, we had difficulty accurately
projecting some of our offset profiles into the fault zone. Excluding measurements from
overlapping portions of the Kekerengu fault yields median OFD values (for the combined discrete
and projected measurement data set) of 1.9 m (18.1%), and mean values of 2.3 m (20.0%). Because
no discrete measurements occurred within the overlapping zones, the median OFD from discrete
67

measurements remains 3.5 m (35.8%) with a mean of 3.9 m (37.8%). The median OFD value of
projected measurements (excluding overlapping portions) is 1.1 m (10.6%), and the mean was 1.3
(12.7%). Here again, we see that projected field measurements yield significantly lower OFD
values than discrete, on-fault field measurements.
Because the field measurements are projected over different fault-perpendicular
measurement apertures, they capture different amounts of OFD. In general, however, larger
projection distances incorporate more OFD. To test this relationship more thoroughly, we
examined the OFD% associated with individual projected field measurements along the
Kekerengu fault as a function of projection distance (Fig. 5d). For the OFD calculations in this
instance, we did not bin the field measurements, as described above. Instead, we individually
compared each field measurement to the nearest correlation measurement, which allowed us to
consistently match each measurement with the aperture over which it was measured (identified
from Kearse et al. [2018]). From this data set, we also excluded measurements where the
Kekerengu fault overlaps with the Jordan Thrust and Tinline Downs faults, to provide a more
meaningful comparison.
Our calculations (Fig. 5d) show that, at each measurement location, some component (≤
~75%) of coseismic surface deformation along the Kekerengu fault was typically expressed as
OFD. By plotting OFD% for each projected measurement versus the fault-perpendicular width
over which it was projected (Fig. 5d) two key observations emerge. Firstly, the data show an
overall negative trend toward 0% OFD with increasing projection distance, implying that field
measurements projected across increasingly large fault-perpendicular distances will eventually
capture the full amount of surface displacement. While this observation is intuitive, and serves as
the basis for comparing field measurements with image correlation measurements, it is worth
68

pointing out that, in general, the percentage of “missing” deformation decreases rapidly as the
projection width increases (over meters to 10s of meters). Secondly, the scatter in the data suggest
that the distribution of OFD is not uniform along the Kekerengu fault. For instance, field
measurements projected across apertures of < 20 m exhibited < 5% to > 35% OFD%. This implies
that both the magnitude and fault-perpendicular distribution of OFD can vary along the strike of a
rupture.

3.4.7 Off-Fault Deformation: Implications
Various studies have addressed the possible controls on the magnitude and distribution of
OFD by comparing (as we have) near-fault field data to remotely sensed measurements of fault
slip. For instance, by comparing near-fault field measurements to the maximum geodetically
inferred slip at depth in six recent large (MW ≥ 7.1) earthquakes, Dolan and Haravitch (2014) found
that fault structural maturity (cumulative fault displacement) exerts a first-order control on
localization of surface slip. Little and Jones (1998, and references therein) report a cumulative
displacement of < 18 km on the Kekerengu fault. Analysis of all (projected and discrete) field
measurements yielded ~18% OFD, which falls within the range of OFD expected for structurally
mature faults with > 80 km of cumulative displacement based on the surface slip ratio estimates
of Dolan and Haravitch (2014). However, the Kearse et al. (2018) data sets contain a high
percentage of projected measurements, which capture some or all OFD. If we instead consider the
end-member case of discrete-only OFD% determinations, our value of ~36% OFD is comparable
to the surface slip ratios determined for other faults of moderate structural maturity (~20–80 km
cumulative displacement [Dolan and Haravitch, 2014]). Our OFD% values show that strain is more
localized along the primary trace of the Kekerengu fault than along less structurally mature faults,
69

based on similar comparisons of optical correlation measurements to field data. For instance,
rupture of the Hoshab fault in Balochistan, Pakistan, which had a cumulative left-lateral
displacement of 6–11 km, resulted in ~45% OFD (Zinke et al., 2014b). Faults in eastern California
that ruptured in the 1992 Landers earthquake had experienced ~3.5 km of net slip, and resulted in
~46% OFD (Milliner et al., 2015). Similarly, faults that ruptured in the 1999 Hector Mine
earthquake had undergone up to ~7 km of slip, and resulted in ~39% OFD (Milliner et al., 2016b).
Our OFD analyses for the Kekerengu fault thus add to a growing body of evidence that cumulative
fault displacement exerts a fundamental control on strain localization in the near-surface.
Emerging evidence, however, suggests that other factors likely play a role.
In comparing short-aperture field measurements to image correlation measurements of
fault offset, Zinke et al. (2014b) and Milliner et al. (2015) found that OFD% increases where faults
rupture through weaker near-surface materials, such as thick accumulations of unconsolidated
sediment. Similarly, geologists found that only ~25% of the total surface displacement resulting
from the 2010 MW 7.1 Darfield, New Zealand earthquake was expressed as offset on discrete
shears at the ground surface (Van Dissen et al., 2011; Quigley et al., 2012; Duffy et al., 2013).
Existing geologic maps show that, along much of its length, the Kekerengu fault juxtaposes
Cretaceous Torlesse greywacke bedrock against Tertiary mudstones and siltstones (Rattenbury et
al., 2006). However, these maps are not sufficiently detailed (scale 1:250,000) to readily compare
OFD with near-surface material properties. More detailed geotechnical studies of the near-surface
geologic materials along the Kekerengu and other recently ruptured faults may one day facilitate
such comparisons.
We also test whether surface topography exerts a strong control on OFD. Local,
topographically controlled stress perturbations along steep slopes have been shown to enhance
70

rock failure through both the formation of new fractures and reactivation of existing fracture
networks (e.g., St. Clair et al., 2015). Additionally, topographic amplification of seismic (SH)
waves can further enhance damage in fault zone rocks through the creation of broad, “flower-like”
structures (e.g., Boore, 1972; Ma, 2008). Both these mechanisms might cause fault slip to become
more distributed in steeper areas, resulting in a positive correlation between OFD% and local
topographic slope. Alternatively, topographic relief may serve as a global first-order proxy for
lithology and rock strength (e.g., Wald and Allen, 2007), as steeper slopes are sustained by stronger
geologic materials (e.g., bedrock), whereas low-relief areas commonly correspond to basins in
which weaker sedimentary deposits accumulate. If such a relationship holds at the scale of our
individual measurements, averaged over 200 m swaths along the Kekerengu fault, we might expect
higher-relief areas to correspond to lower amounts of OFD.
To distinguish between these competing hypotheses, we computed the local slope (median
slope value from the LINZ DEM within a 200 m radius; the same distance over which we stacked
our correlation measurement profiles) to which we compared the OFD% values for the discrete-
only measurements (Fig. 5e). We only considered the set of discrete field measurements, because
the projected field measurements capture various amounts of OFD. As shown in Figure 5, we
found a weak positive correlation (Spearman rank coefficient = 0.52) between OFD% with
increasing topographic slope, suggesting that OFD% is greater in steeper areas.
Alternatively, increased OFD in high relief regions along the Kekerengu fault may simply
result from instances of high structural complexity (especially restraining bends) along the fault.
Some of the largest values of OFD% occur along the central portion of the Kekerengu fault,
northeast of the Clarence River, where several prominent restraining bends are likely responsible
for uplift of steep local topographic features, including Mole Hill (42°1.7′S, 173°55.8′E) and Misty
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Hill (42°0.5′S, 173°56.0′E). If increases in OFD% with slope along the Kekerengu fault are
ultimately related to uplift and broadening of the fault zone at restraining bends, these data
contribute to an increasing body of knowledge documenting the distribution surface deformation
with fault structural complexities (e.g., Milliner et al., 2015; Van Dissen et al., 2015).
Our results, along with other comparisons of field measurements against remotely sensed
data, show that OFD is not only ubiquitous among surface ruptures, but can constitute a substantial
portion of total surface deformation (e.g., Dolan and Haravitch, 2014; Zinke et al., 2014b; Milliner
et al., 2015, 2016b). These observations have significant implications for interpreting differences
between geologic slip rates and geodetic slip deficit rates (i.e., “geodetic slip rates”), as well as for
the use of empirical scaling laws between surface slip and earthquake magnitude, and the
development of potential earthquake microzonation maps for the built environment.
In some locations, studies have reported significant differences between geologic estimates
of fault slip rates, and geodetically inferred slip deficit rates (e.g., the Garlock fault [Peltzer et al.,
2001; Ganev et al., 2012; Dolan et al., 2016]; eastern California shear zone [Meade and Hager,
2005; Oskin et al., 2008]; Doruneh fault [Walpersdorf et al., 2013; Farbod et al., 2016]; Wasatch
fault [Friedrich et al., 2003]). In some cases, such as the eastern California shear zone, geodetic
slip deficit rates exceed geologic slip rates, leading some workers to conclude that those faults are
undergoing transiently elevated rates of strain accumulation (e.g., Peltzer et al., 2001; Dolan et al.,
2007; Oskin et al., 2008; Kozacı et al., 2009). However, if OFD is considered in determining fault
slip rates (whether by direct measurement of near-field warping, or estimating OFD% based on
empirical studies [e.g., Kearse et al., 2018]), such differences may narrow considerably (e.g.,
Shelef and Oskin, 2010; Dolan and Haravitch, 2014; Milliner et al., 2016b). Properly accounting
for OFD is therefore important for determining the roles of faults in accommodating crustal
72

deformation (e.g., in Tibet [Tapponier and Molnar, 1976]), and characterizing the mechanics of
lithospheric rheology based on comparisons of fault slip rates to geodetically inferred slip deficit
rates (Meade et al., 2013; Dolan and Meade, 2017, and references therein). Furthermore,
Quaternary fault slip rates provide an important input for Probabilistic Seismic Hazard Assessment
(e.g., Stirling et al., 2012; Field et al., 2017), and underestimating slip rate may cause the seismic
hazard posed by a fault to be underrepresented.
Consideration of OFD is also important when using empirically derived scaling laws that
relate mean or maximum surface slip to earthquake magnitude (Wells and Coppersmith, 1994;
Wesnousky, 2008; Stirling et al., 2012; Milliner et al., 2016b). For instance, if slip in the most
recent earthquake on a fault is known from a distribution of offset geomorphic features, the
moment magnitude expected for that amount of offset based on empirical scaling laws will vary
depending upon whether the offset features are projected over larger fault-perpendicular widths,
or smaller ones (i.e., as the field measurements encompass more or less OFD), resulting in
ambiguity and potential error in estimates of magnitude and expected displacements in future
earthquakes (Milliner et al., 2016b). Alternatively, if OFD is not considered in predicting surface
slip for a given expected earthquake magnitude, then the empirical scaling laws may overpredict
mean slip.
Application of high-resolution surface displacement data such as those presented above to
next-generation seismic hazard mitigation strategies requires a thorough understanding of the
factors controlling OFD. This will require not only additional high-resolution analyses of surface
deformation patterns (such as those presented above), but also detailed mapping and geotechnical
characterization of the distribution and mechanical properties of the near-surface materials specific
to each fault zone. Comprehensive data sets collected in a variety of geologic settings will facilitate
73

the detailed assessment of possible causes and effects of distributed versus discrete deformation
patterns in future earthquakes. In turn, these analyses will help to facilitate predictive studies that
can assist planners in establishing site-specific guidelines of likely ground-surface deformation
patterns in future earthquakes.

3.5 Conclusions
We used an advanced workflow for optical image correlation of high-resolution
WorldView satellite stereo imagery to determine the 3D surface deformation generated during the
2016 Kaikōura earthquake. The resulting deformation maps revealed the spatial and kinematic
complexity of the 2016 earthquake in unprecedented detail. We identified a previously
unrecognized fault, the Snowflake Spur fault, that acts as a continuation of the Upper Kowhai and
Manakau faults and may have aided in rupture propagation. These faults, together with other,
unruptured faults of the Seaward Kaikōura Range, may represent an attempt by the plate boundary
fault system to geometrically simplify the transition between the Hope fault, and the Jordan Thrust-
Kekerengu fault system. From our correlations, we collected 531 measurements of displacement
across 19 faults. Our measurements revealed a high degree of spatial variability in surface slip,
with generally smaller displacements in the NCD, and larger displacements in the MFS. Kinematic
analysis of our displacement measurements showed that overall slip patterns in the NCD
fundamentally differed from those in the MFS: faults in the MFS accommodated motion that is
primarily subparallel, though slightly rotated from, the plate motion direction; faults in the NCD,
in contrast, accommodated motion that was primarily sub-perpendicular (and secondarily parallel)
to the plate motion direction. These observations demonstrate that although fault kinematics may
be grossly consistent within a tectonic domain, fault ruptures may cross between tectonic domains
74

with fundamentally different overall kinematic characteristics. Future seismic hazard assessment
strategies should include such scenarios. The kinematics of individual faults ruptured in the 2016
rupture were generally consistent with their long-term fault kinematics, as indicated by the
topographic expression of the faults. The most notable exception to this was normal-dextral
coseismic slip on the Jordan Thrust fault, which typically acts as a reverse-dextral fault,
accommodating uplift of the Seaward Kaikōura Range (Van Dissen and Yeats, 1991).
Comparison of our displacement measurements along the Kekerengu fault with published
field measurements revealed up to ~36% of surface deformation is accommodated as OFD. The
amount of OFD not captured by field measurements, however, decreases nonlinearly as the fault-
perpendicular distance over which the field measurements are projected increases, and most OFD
is accommodated within ~100 m of the primary fault. More high-resolution surface deformation
studies such as that presented above, and detailed geotechnical characterization of the fault zone
materials, are necessary to determine what factors control the magnitude and distribution of OFD
on faults generally.

3.6 Figure Captions
Figure 1. (Inset) The Kaikōura region accommodates relative Pacific-Australian plate motion
between the Hikurangi subduction zone (Hik) and dextral-oblique Alpine fault (Alp F). CR is
Chatham Rise. (Main figure) Red faults show our mapping of the ground-surface (or shallow
subsurface) rupture in the 2016 earthquake based on gradients in our correlation maps; the Needles
fault ruptured the seafloor (Kearse et al., 2018) but is not considered here. Black star is the
epicenter of the 14
th
November 2016 mainshock (Nicol et al., 2018). Black lines are active faults
(from Langridge et al., 2016) on which we did not observe surface rupture, though the Hikurangi
75

megathrust may have ruptured at depth. The Hope fault separates the MFS (north) from the NCD
(south). SKR is Seaward Kaikōura Range. EP is Emu Plain. Base map is Shuttle Radar Topography
Mission DEM. Black arrows is the plate motion vector (Beavan et al., 2002; DeMets et al., 2010).

Figure 2. Image correlations showing EW, NS, and vertical deformation. Red lines show our
mapping of fault ruptures; gray lines are faults without rupture discernable in our correlations.
(Insets) Details of the Kekerengu-Jordan Thrust, Papatea, and Fidget fault junction. Gray areas
show decorrelation caused mostly by landsliding (especially along the Papatea fault).

Figure 3. Net slip measurements (colored dots) from stacked displacement profiles collected every
500 m (black bar in upper right corner represents one 500 m-long profile). (Insets) Three
components of slip on the Kekerengu fault. Solid blue lines are linear best fits of surface
deformation. Dashed blue lines show fault zone width. (Lower left) Histogram of all net slip
measurements. (Lower right) Histogram of all valid fault zone width measurements.

Figure 4. Rupture kinematics. Black arrows show horizontal surface displacement vectors on map
of vertical deformation. Gray lines show unruptured faults in the SKR; Hope fault separates the
MFS and NCD. (Insets) Lower hemisphere projections (colored contours) and rose diagrams of
scaled slip vector orientations for slip along MFS and NCD faults. Black triangles at edges of rose
diagrams indicate relative plate motion direction (Beavan et al., 2002; DeMets et al., 2010).

Figure 5. (a–c) Comparison of fault-parallel slip profiles along the Kekerengu fault. Black lines
and gray fields are our correlation measurements (200 m spacing), shown by distance from
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Kekerengu-Papatea fault junction. Dark blue lines show where the Kekerengu fault overlaps with
the Jordan Thrust (JT) and Tinline Downs (TD) faults. Pale blue dots are main-fault field
measurements from Kearse et al. (2018), binned at 200 m intervals: (a) all field measurements; (b)
discrete field measurements; (c) projected field measurements. (d) Blue dots are OFD% for
projected field measurements (unbinned), versus distance across which they are projected. Dashed
gray line shows interpreted decrease in OFD% with increasing projection distance. (e) Blue dots
are OFD% for discrete-only field measurements versus local slope angle; gray line shows linear
regression.

3.6.1 Captions for Supplementary Materials
Table S1. WorldView image identification codes, look angles, and acquisition dates.

Text S2. Image correlation methodology and parameters.

Figure S3. Detailed figures of optical image correlation results. (A) East-west (EW); (B) north-
south (NS); and (C) vertical components of deformation are presented as separate figures.

Table S4. Fault displacement measurements for all faults, determined from our correlation maps,
collected at 500 m spacing.

Table S5. Measurement aperture and fault zone width measurements, collected every 500 m.

77

Table S6. Fault displacement measurements for selected faults, determined from our correlation
maps, collected at 200 m spacing.

Table S7. Measurement aperture and fault zone width measurements, collected every 200 m.

Figure S8. Methodology for slip vector determinations.

Table S9. Slip vector measurements.

Figure S10. Summary of kinematic analyses for each fault.

Table S11. Table of off-fault deformation (OFD) measurements, for which OFD is calculated in
different ways.

78

CHAPTER 4:
Evolution and progressive geomorphic manifestation of surface faulting: A comparison of
the Wairau and Awatere faults, South Island, New Zealand

The following work is based on R. Zinke, J. F. Dolan, R. Van Dissen, J. R. Grenader, E.
J. Rhodes, C. P. McGuire, R. M. Langridge, A. Nicol, and A. E. Hatem (2015), “Evolution and
progressive geomorphic manifestation of surface faulting: A comparison of the Wairau and
Awatere faults, South Island, New Zealand” and corresponding Forum Reply (2016a) by the
same name published in Geology (doi: 10.1130/G37065.1 and 10.1130/G38188Y.1).

4.1 Abstract
Field mapping and lidar analysis of surface faulting patterns expressed in flights of
geologically similar fluvial terraces at the well-known Branch River and Saxton River sites along
the Wairau (Alpine) and Awatere strike-slip faults, South Island, New Zealand, reveal that fault-
related deformation patterns expressed in the topography at these sites are markedly less
structurally complex along the higher-displacement (hundreds of kilometers), structurally mature
Wairau fault than along the Awatere fault (~13–20 km total slip). These differences, which are
generally representative of the surface traces of these faults, provide direct evidence that surface
faulting becomes structurally simpler with increasing cumulative fault offset. We also examine the
degree to which off-fault deformation (OFD) is expressed in the landscape at the Saxton River site
along the less structurally mature Awatere fault. Significantly greater amounts of OFD are
discernible as a wide damage zone (~460 m fault-perpendicular width) in older (c. 15 ka), more-
79

displaced (64–74 m) fluvial terraces than in younger (c. 1–7 ka), less-displaced (< 55 m) terraces;
no OFD is discernible in the lidar data on the least-displaced (< 35 m) terraces. From this, we infer
that OFD becomes progressively more geomorphically apparent with accumulating displacement.
These observations imply that (1) the processes that accommodate OFD are active during each
earthquake, but may not be evident in deposits that have experienced relatively small
displacements; (2) structures accommodating OFD will become progressively geomorphically
clearer with increasing displacement; (3) geomorphic measurements of overall fault zone width
taken in deposits that have experienced small displacements will be underestimates; and (4) fault
slip rates based on geomorphic surface offsets will be underestimates for immature faults if based
solely on measurements along the high-strain fault core.

4.2 Introduction
As faults accumulate greater amounts of displacement, strain progressively localizes into
a relatively narrow, structurally simple zone (e.g., Wesnousky, 1988; Chester et al., 2004; Dolan
and Haravitch, 2014; Hatem, 2014). This process, known as structural maturation, decreases the
proportion of total shear accommodated as off-fault deformation (OFD), which includes secondary
faulting, warping, rotation, and distributed granular flow, that occur outside the fault core (e.g.,
Dolan and Haravitch, 2014). Additionally, the width and expression of OFD in geologic and
geomorphic features will increase as deformation accumulates over multiple earthquakes,
especially along structurally immature faults. In this way, the style and structural complexity of
surface deformation recorded in recent deposits along a fault will reflect both the structural
maturity of the underlying fault as well as the cumulative deformation resulting from earthquakes
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experienced by the deposit. Understanding how these processes become manifest in the landscape
is essential to the proper geomorphic interpretation of surface faulting patterns.
We examine the relationships between structural maturity, strain localization,
and the geomorphic expression of OFD by characterizing surface faulting patterns along two
strike-slip faults with vastly different cumulative displacements. Specifically, we combine field
observations with aerial lidar topographic data to compare surface faulting patterns in flights of
fluvial terraces at the Branch River and Saxton River sites along the Wairau and Awatere faults in
the Marlborough fault system of South Island, New Zealand (Fig. 1). These observations provide
key insights into the interplay between overall fault structural maturity and the progressive
geomorphic manifestation of OFD with increasing fault offset in young deposits.

4.3 Comparison of Surface Deformation Patterns Along Faults with Different Structural
Maturities
The Wairau and Awatere faults have similar slip rates, lithologies, and tectonic settings
(see the supplementary material; e.g., Wallace et al., 2012, and references therein), but have
accommodated considerably different amounts of slip. Whereas the Wairau fault has
accommodated much of the total ~460 km of right-lateral offset along the Alpine fault system, the
Awatere fault has accommodated only ~13–20 km of strike slip (Fig. 1, S1; Sutherland, 1999;
Rattenbury et al., 2006; this study). As a first-order observation, the Wairau fault is geomorphically
and structurally simpler over its length than the Awatere fault (e.g., Fig. S2; Rattenbury et al.,
2006). Fault surface expressions are, however, subject to numerous variables, including variations
in the age, composition, fabric, and thickness of the near-surface geologic material through which
the fault propagates (e.g., Zinke et al., 2014b; Teran et al., 2015). The Branch River and Saxton
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River sites are ideal for comparing surface deformation patterns on these faults, as both consist of
flights of fluvial terraces that are similar in depositional age, geologic material, grain size, fault
slip experienced by the deposits, and climate. We adopt the names and interpretations of the
terraces used by previous workers at each site (Fig. 2; Lensen, 1968; Mason et al., 2006b). Seven
terraces (A–F and W in Fig. 2a) compose the flight at Branch River, whereas the Saxton River site
contains six terraces (T1–T6 in Fig. 2b) and a bedrock promontory (“bedrock spur” of Mason et
al., 2006b). The terrace deposits at both of these sites consist of greywacke-dominated cobble
gravels. Luminescence dating and cobble weathering-rind analyses indicate that the terrace ages
at Branch River (7–18 ka) overlap with those at Saxton River (0–15 ka; see Knuepfer, 1992; Mason
et al., 2006b). Moreover, the oldest terraces at each site have experienced similar amounts of
displacement (53 ± 2 m at Branch River; 69 ± 5 m at Saxton River [see the supplementary material;
Lensen, 1968; Mason et al., 2006a, 2006b; Grenader et al., 2014; Zinke et al., 2014a]).
We used high-resolution (≥ 12 shots m
–2
) aerial lidar topographic data (see details in the
Data Repository) to map fault-related deformation expressed in the micro-topography (Fig. 2; Fig.
S3). Field observations of surface deformation at these sites support our lidar mapping. At both
sites, subtle (≥ 20 cm) intra-terrace channels and geomorphically expressed minor secondary faults
are well preserved in the landscape. Throughout both study sites, secondary faulting expressed by
vertical relief on this order is the only form of OFD discernible in the lidar data, and in the
following we use the distribution of secondary faults as a proxy for other, geomorphically
undetectable forms of OFD (e.g., warping, distributed granular flow).
The trace of the Wairau fault at Branch River (Fig. 2a) is straight and continuous over most
of the 1.2-km-long study area, though we note the presence of a sag basin and a pop-up ridge near
the eastern edge of the study area, each expressed by 2–3 m of topographic relief in terrace A. No
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secondary fault strands are geomorphically evident in the youngest four terraces (F–C). Two
secondary fault strands, which are subparallel to the main trace, are discernible in the older W and
A terraces. The fault-perpendicular width of geomorphically evident deformation at this site is <
100 m at its widest and is effectively discrete (< 3 m wide) along ~80% of the length of the study
site in all but the oldest, most-displaced terraces.
In contrast, the surface expression of the Awatere fault at Saxton River (Fig. 2b) is much
more structurally complex. The main fault trace is multi-stranded throughout most of its length,
and the main fault strands exhibit curvature on the half-kilometer scale. A 160-m-long, 2-m-deep
sag basin is evident in the oldest T1 surface. The most striking difference in fault trace complexity
between the two study sites is the abundance of secondary fault strands along the Awatere fault.
These secondary faults are 14–342 m in length and are expressed by 0.2- to 1.1-m-tall scarps.
Secondary fault strands are discernible to a maximum fault-perpendicular width of ~460 m in the
oldest terrace, T1.
The Branch River and Saxton River sites are generally representative of surface
deformation patterns along the Wairau and Awatere faults, as similar trends in structural
complexity are observed along the entire 40 km and 80 km fault lengths covered by our lidar data
(Fig. S2). Although sections of relatively structurally simple surface faulting occur at some
locations along the Awatere fault, such examples are not representative of the overall structural
complexity of this structurally less-mature fault. We also note that the wider zone of OFD at Saxton
River is not a function of thicker sediments at that site, as the terrace gravels are only a few meters
thick there (Mason, 2004), whereas they are ≥ 10 m thick at the structurally simpler Branch River
site (observed in nearby cut banks).
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We suggest that the dearth of OFD expressed as secondary faulting at the Branch River
site, relative to that at the Saxton River site, results from the greater cumulative offset and
consequent higher structural maturity of the Wairau fault. This inference is consistent with
previous theoretical and observational studies that demonstrate that whereas faulting may be
complex and relatively diffuse along lower-displacement, structurally immature faults, strain
progressively localizes into a relatively discrete, structurally simple zone with increasing amounts
of displacement (e.g., Sibson, 1985; Wesnousky, 1988; Stirling et al., 1996; Frost et al., 2009;
Dolan and Haravitch, 2014).
Changes in fault structure will lead to the co-evolution of many important fault behaviors.
Structural complexities such as steps and bends exert strong controls on fault rupture arrest, so
structurally simpler faults will tend to rupture in larger events (e.g., Wesnousky, 1988, 2006;
Stirling et al., 1996; Elliott et al., 2009). Additionally, the frequency content of seismic energy
released in earthquakes likely depends on fault complexity (Dolan, 2006). Furthermore, fault
structural maturity exerts a primary control on percentages of OFD in large (MW > 7) earthquakes
(Dolan and Haravitch, 2014). Percentages of OFD can also affect measurements of slip in past
earthquakes, with implications for the proper interpretation of fault slip rates based on offset
geomorphic features and for the use of such data in seismic hazard assessments (Dolan and
Haravitch, 2014).

4.4 Progressive Geomorphic Manifestation of OFD with Increasing Fault Slip
The distribution of OFD expressed as secondary fault strands is strongly heterogeneous
across the Saxton River site (Fig. 2b). In the oldest T1 surface, secondary faults discernible in the
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lidar define a zone of deformation across a total fault-perpendicular width of ~460 m. Secondary
faults evident in the younger T2 surface define a narrower deformation zone with a fault-
perpendicular width of ~100 m. OFD is also evident in the bedrock spur at fault-perpendicular
distances of ~220 m north of the main fault trace (the same distance as OFD in the adjacent T1
terrace) and ~30 m south of the main fault trace, though some evidence for OFD south of the main
fault trace may be obscured by colluvium or destroyed by slumping. Regardless, the total width of
OFD within the bedrock spur evident in the lidar is ≥ 230 m. In contrast to these wide zones of
secondary faulting in the older deposits, no geomorphic evidence of OFD is visible in either the
lidar data or in our field mapping in the T3–T6 surfaces. In these younger terraces, the fault is
expressed as a single, discrete, through-going feature.
Given that flights of fluvial terraces such as those at Saxton River represent time-
transgressive sequences of deposits, the observations above indicate that OFD becomes more
geomorphically discernible in progressively older terraces. The lack of topographically expressed
OFD in the T6–T3 terraces is not likely to reflect changes in the structural maturity of the bedrock
Awatere fault beneath the terrace deposits that occurred during the additional ~19 m of
displacement accommodated between formation of the T2 and T3 terraces, as tens of kilometers
of fault slip is necessary to cause such an increase in structural maturity (e.g., Wesnousky, 1988;
Stirling et al., 1996; Dolan and Haravitch, 2014). Rather, the processes that accommodate OFD
are equally active during each earthquake recorded at the Saxton River site. Variations in the
thicknesses of the gravel deposits composing each terrace could influence the distribution of fault-
related deformation, such that thicker gravel deposits would cause more OFD (e.g., Van Dissen et
al., 2011; Zinke et al., 2014b; Teran et al., 2015). Paleoseismic trenching revealed bedrock as
shallow as ~1.2 m below the T1 surface (Mason, 2004), whereas a road cut along the T2–T6 terrace
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riser exposes bedrock cropping out at 2–3 m below the T2 surface, suggesting that the T1 terrace
gravels are similar in thickness to, or somewhat thinner than, the deposits composing the younger
T2–T6 terraces. The fact that we observe more OFD in the T1 terrace than in the slightly thicker
T2–T6 terraces reinforces our argument that cumulative slip, not sediment thickness, is the primary
control on the manifestation of OFD in the landscape at Saxton River. Thus, whereas OFD may
not be immediately evident in the landscape, it will become geomorphically better expressed as a
function of increasing displacement (Fig. 3). In gravel-dominated deposits such as those at Saxton
River, along faults of similar structural maturity to the Awatere fault, 35–50 m of displacement
may be necessary for OFD to become geomorphically evident.
We observe a similar though much less-pronounced relationship between OFD and terrace
displacement on the Wairau fault at Branch River. Whereas no OFD is observable in the lidar
along the five youngest, least-offset terraces (F–B), we observe minor secondary faulting, out to
~90 m fault-perpendicular width, in the two oldest terraces (W and A) which have been displaced
by ≥ 55 m (Fig. 2). We reemphasize, however, that the dearth of geomorphically expressed OFD
at the Branch River site relative to that at the Saxton River site in similarly offset terraces supports
our inference that strain is much more localized along the structurally mature Wairau fault than
along the much less-mature Awatere fault.
Analysis of surface deformation patterns associated with recent large-magnitude
earthquakes using remote sensing techniques and field studies of offset linear cultural features
reveals that surface ruptures are commonly surrounded by zones of OFD that extend as much as
several hundred meters perpendicular to the fault (e.g., Rockwell et al., 2002; Van Dissen et al.,
2013; Zinke et al., 2014b; Milliner et al., 2015). As much as ~50% of total coseismic surface
displacement can be accommodated as OFD along structurally immature faults (Dolan and
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Haravitch, 2014), and numerous geologic studies report evidence for off-fault coseismic strain
(e.g., cracking, en echelon shearing) associated with recent surface ruptures (e.g., Zinke et al.,
2014b; Teran et al., 2015). Yet quantifiable evidence of OFD is seldom manifest in the surrounding
geomorphology, as many of the structures that accommodate OFD during surface ruptures are not
likely to be preserved in the landscape. Thus, despite the importance of OFD in accommodating
coseismic surface strain, well-defined geomorphic evidence for these zones that is likely to remain
discernible for centuries to millennia is commonly lacking.
Geomorphic measurements of overall fault zone width may thus underestimate the true
width of the fault zone, especially in deposits that have experienced small displacements along
structurally immature faults. The ability to predict the width of fault-related deformation in
advance of future events is important for understanding fault rupture mechanics and mitigation of
seismic hazards to the built environment. For instance, improved constraints on expected surface
deformation patterns could aid in the potential development of earthquake hazard microzonation
maps detailing the expected magnitude and width of OFD, which in turn could be incorporated
into standards for more effective earthquake-resistant design. Furthermore, failure to account for
OFD in geomorphic measurements of fault slip may lead to systematic underestimation of the total
fault slip, and consequently to underestimation of paleoearthquake magnitudes and fault slip rates.
Because fault slip rates are a basic input into most probabilistic seismic hazard analyses, this will
lead to underestimation of the seismic hazard associated with a fault. Proper interpretation of fault-
related deformation patterns in the landscape therefore rests on the recognition that although OFD
may not be fully geomorphically expressed, the processes that accommodate OFD nevertheless
play a crucial role in accommodating coseismic strain in the near surface, especially along
structurally immature faults.
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4.5 Further Evidence for the Structural Evolution and Progressive Geomorphic
Manifestation of OFD at the Branch River and Saxton River sites
In Forum Comments, Quigley and Pettinga (2016), and Duffy (2016) challenge different
aspects of our comparisons of off-fault deformation patterns at the Branch River (BR) and Saxton
River (SR) sites along the Wairau and Awatere faults in South Island, New Zealand. The following
is based on the Geology Forum Reply to Comment (Zinke et al., 2016a).
Quigley and Pettinga (2016) argue that the BR and SR sites are structurally and
topographically dissimilar, and cannot, therefore, be compared. As we thoroughly discuss,
however, the differences in structural complexity observed between the two sites are exactly what
is expected for faults with vastly different cumulative displacements (> 150 km for the Wairau and
< 20 km for the western Awatere fault [Little and Jones, 1998, and references therein]). The BR
and SR sites are comparable because of their similarities in tectonic setting, subparallel strikes,
climate history, sediment composition and age, and slip accumulated within each terrace.
Differences in the structural complexities of the fault traces (including bends, steps, and amount
of geomorphically evident OFD) can therefore be attributed to the structural maturity of the
underlying faults. Thus, the more structurally complex trace of the Awatere fault at SR relative to
that of the Wairau fault at BR is exactly what we would expect, given the differences in structural
maturity.
Although there are topographic differences between the two sites—the SR site includes an
~150-m-high bedrock promontory (“bedrock spur”) and the BR site is ~0.5 km from the nearest
significant topography—the observed patterns of deformation do not correspond with the
topographic differences, as maintained by Quigley and Pettinga. For instance,modeling of
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gravitationally induced stresses due to topography (St. Clair et al., 2015) shows that failure
potential of rock is greatest along and immediately adjacent to steep slopes. Topographically
induced fracturing should therefore be more concentrated within and around the bedrock spur. At
the SR site, secondary fault strands are most concentrated in the T1 terrace, hundreds of meters
away from the break in slope (our figure 2B). In addition, secondary fault strands within the T1
terrace show no preferred orientation relative to the topographic trend of the bedrock ridge.
Quigley and Pettinga further suggest that the topography of the underlying bedrock-sediment
contact may influence surface fault expression. The limited control on the depth-to-bedrock
beneath the terraces at the SR site (~1.2 m in paleoseismic trenches in T1, and ~2 m along a road
cut at the southwest edge of T2, as discussed by us), however, suggests a relatively flat underlying
bedrock surface that would not gravitationally affect the distribution or character of OFD. These
observations obviate topography as a significant control on the patterns of OFD evident at these
sites.
Quigley and Pettinga then go on to argue that structural maturity (cumulative offset) may
not control the complexity of faulting along the Wairau and Awatere faults, citing examples of
structurally complex sections and variable structural complexity along the Alpine and Hope faults
in South Island, New Zealand. Quigley and Pettinga point out that despite the fact that the Alpine
fault has accommodated > 400 km of right-lateral displacement (e.g., Sutherland, 1999), its surface
expression is, in many places, structurally complex. However, whereas the Wairau and Awatere
faults are steeply dipping strike-slip faults, the Alpine fault is a moderately dipping oblique-fault,
along which fault segmentation and structural complexity result from strain partitioning and
gravitational effects due, in large measure to its substantial oblique-reverse component (e.g.,
Cooper and Norris, 1994; Barth et al., 2012). Thus, comparison of the surface expression of the
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distinctly kinematically dissimilar Alpine fault with the kinematically similar Wairau and Awatere
faults is inappropriate.
Unlike the Alpine fault, the Hope fault is a relatively structurally immature fault, having
accommodated only ~20 km of cumulative displacement (Freund, 1971). Along-strike differences
in structural complexity—ranging from linear, single-stranded, simple sections to extremely
complex sections—are common along such immature faults. We explicitly addressed this issue in
our primary text, and in the supporting material (Fig. S2), acknowledging these differences and
showing that the BR and SR sites are representative of the broader structural complexity of the
Wairau and Awatere faults. While examples of along-strike variability in the structural complexity
of the Hope fault can be pointed out, these limited observations do not change the fundamental
point made by us that the decreased structural complexity of the Wairau fault relative to that of the
Awatere fault is the result of the different cumulative offsets between the faults, and resulting
differences in their structural maturities. We therefore strongly dispute their assertion that
“Clearly, structural maturity is not a primary control of OFD complexity and width variations
along [the Wairau and Awatere] faults.” In fact, as shown by our analysis and numerous other
analyses of faults outside of New Zealand, the association of structural complexity and fault
maturity is inescapable (e.g., Wesnousky, 1988; Stirling et al., 1996; Dolan and Haravitch, 2014).
Finally, Quigley and Pettinga assert that strain localizes onto faults within only a few (~2–
3) meters of displacement, and thus it is unlikely that the difference in accumulated displacement
between the T2 and T3 terraces at SR accounts for the lack of OFD observable in the T3 and
younger terraces. In doing so, they seem to have misunderstood that we are discussing two
different processes that occur on completely different time scales—the long-term process of fault
structural maturation, which occurs during tens of kilometers of fault slip (e.g., Wesnousky, 1988;
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Stirling et al., 1996), and the progressive manifestation of localized fault slip within relatively
young sedimentary deposits, which we maintain occurs over tens of meters of slip. Quigley and
Pettinga incorrectly assumed that geomorphically evident OFD observed in the progressively
displaced terraces at SR decreases in younger, less displaced terraces due to “structural
maturation” of the underlying bedrock-hosted fault. We explicitly stated that this is not the case.
The basic point expressed by us is that the structural maturity of the underlying bedrock-hosted
fault is roughly constant across all terraces at the SR site, even where it is not geomorphically
evident. Recent work by Milliner et al. (2015) indicates that, in fact, several meters of coseismic
displacement can be distributed throughout the near surface without any geomorphically
discernable evidence in the microtopography. This was also shown in the 2010 Darfield rupture
(which Quigley and Pettinga cited as an example of strain localization), where nowhere along the
rupture was horizontal shear >30% localized onto a fault; most deformation was distributed over
25–150-m-wide zones—a significant amount of which was not discernible in lidar imagery (Van
Dissen et al., 2011). Instead, multiple earthquake cycles are required for OFD to accumulate and
coalesce into geomorphically discernable features that are preserved in the landscape. Quigley and
Pettinga’s argument therefore stems from an invalid understanding of a basic concept discussed in
our paper.
Duffy (2016) raises a valid and important point. In retrospect, we should have discussed
the impact of the local kinematics more fully, and we welcome the opportunity afforded by Duffy’s
Comment to describe more fully the structural and geomorphic relationships at the SR site. Duffy
correctly points out that the patterns of OFD at the SR site are at least partially controlled by the
paired releasing and restraining bends along the Awatere fault. Specifically, deformation within
~100 m of the fault across terrace T1 does indeed reflect oblique-normal faulting associated with
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the releasing bend. However, deformation farther from the fault in T1 does not. If this distal
deformation is related to the adjacent restraining bend, or more generally the structural complexity
of the fault expressed in the underlying bedrock (which at SR may be controlled by complex strain
transfer southeastward to the Barefell Pass fault), then it should not stop as it does at the T1-T2
terrace riser. Rather, it should extend out to similar fault-perpendicular widths across T2, which it
certainly does not. Secondary fault strands are notably absent across most of T2, with almost all
OFD concentrated in a narrow, ~20–50-m-wide pressure ridge along the fault trace. The broad
regions of T2 devoid of geomorphically discernable OFD are directly adjacent to regions with
clear geomorphic evidence for OFD in the T1 terrace (our Figure 2). This sharp divide between
discernable OFD in the older, more displaced T1 terrace and the younger, less displaced T2 terrace
implies that while a significant amount of off-fault strain is accommodated within T2, OFD has
not accumulated sufficiently to localize into geomorphically evident fault strands. This observation
supports our original conclusion that OFD occurring along most surface ruptures in loose
sediments will not be geomorphically discernable in surfaces that have not experienced sufficient
overall cumulative slip. Consequently, OFD will become progressively better expressed in older,
more displaced features within kinematically similar settings. The fact that greater amounts of slip
in older surfaces are not observed in the western part of the Hope fault Poplars Graben site (cited
by Duffy as a kinematic and geomorphic analog to SR), where the secondary faults cross terraces
of different ages (Cowan, 1990), may be due in part to the position of the releasing bend within
the younger deposits, and in part to the reactivation of the secondary faults as landslides down the
~150-m-high bank of the Hope River.
To further illustrate our conclusion that OFD becomes progressively manifest as a function
of cumulative displacement, we examine another releasing bend along the Awatere fault, ~3 km
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west of the SR site (Fig. 4). This releasing bend is kinematically similar to the releasing step at the
SR site. However, the Terrace B tread near the releasing bend has experienced ~11 m of offset;
channel offsets on the tread of ~4 m indicate limited subsequent flow across the tread in local
incised channels. Yet no OFD is geomorphically evident within Terrace B. While OFD almost
certainly occurs during slip through this releasing bend, as it does at the SR site, the terrace has
not accumulated enough slip for the OFD to become manifest in the microtopography. Taken in
context with the more abundant, geomorphically evident OFD in older, more displaced terraces
around the releasing bend at SR, these observations show that, as stated by us and shown by studies
of surface deformation in recent surface ruptures (e.g., Van Dissen et al., 2011; Milliner et al.,
2015), while the mechanisms accommodating OFD are active throughout each earthquake, OFD
may not become manifest in the landscape until sufficient slip has accumulated.

4.6 Figure Captions
Figure 1. Inset: Marlborough fault system (MFS) on South Island, New Zealand, transfers relative
Pacific-Australian plate motion between dextral-oblique–slip Alpine fault (Alp F) and Hikurangi
subduction margin (Hik). Wairau fault has accommodated much of the ~460 km of right-lateral
shear along the Alpine fault system, as shown by Dun Mountain–Maitai ultramafic terrane (black)
(Sutherland, 1999). Main figure: Simplified representation of MFS showing active, predominantly
strike-slip faults in red. Black arrow shows Pacific plate motion relative to Australian plate
(DeMets et al., 1994). Rakaia (dark gray) and Esk Head (medium gray) basement terranes are
offset ~13 km along Awatere fault by discrete slip, with potential additional ~5 km of slip manifest
as bending (“drag folding”) of terrane boundaries (Fig. S2; Rattenbury et al., 2006). Branch River
(BR) and Saxton River (SR) sites are shown.
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Figure 2. Lidar hillshade images of the two study sites in New Zealand. (a) Branch River site on
Wairau fault. (b) Saxton River site on Awatere fault. Fault traces (including secondary fault
strands) shown in red. Terraces are demarked by colors and labeled according to previous studies
(Lensen, 1968; Mason et al., 2006b). Contacts between colored surfaces represent terrace risers.

Figure 3. Fault-perpendicular width of geomorphically evident deformation at Saxton River
(Awatere fault), New Zealand, as function of terrace offset. Terraces (T1–T6) are as shown in Fig.
2b. Width of boxes represents uncertainty in lateral terrace offset (Zinke et al., 2014a). Whereas
strain per event accommodated as off-fault deformation (OFD) remains constant, secondary faults
become better expressed as terraces accumulate slip. “Dist.” is distance.

Figure 4. Releasing bend along the western Awatere fault. No off-fault deformation is
geomorphically discernible in the vicinity of the bend due to the small amount of offset (4–11 m)
accumulated by Terrace B.

4.6.1 Captions for Supplementary Materials
Text S1. Characteristics of the Wairau and Awatere faults.

Figure S2. Bedrock geology of the Marlborough fault system, northern South Island, New
Zealand, showing the cumulative offset of the Awatere fault near the Saxton River site (modified
from Rattenbury et al., 2006). Simplified traces of the Alpine-Wairau, Awatere, Clarence, and
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Hope faults are highlighted in red. (b) is a finer-scale map of the area shown in (a). Cumulative
displacement of the Awatere fault is shown by offset of the Esk Head and Rakaia basement
terranes. Although we suggest a maximum of 20 km of fault displacement on the Awatere fault,
including 13 km of main strand slip and 5–7 km of distributed “drag folding”, as can be seen from
the detail map, much of the distributed off-Awatere right-lateral shear appears to be
acoommodated by several secondary fault strands to the N of the main Awatere fault. Maps are
drawn using the New Zealand Map Grid Projection. Coordinates given in meters based on the New
Zealand Map Grid using the Geodetic Datum 1949. Coordinates in decimal degrees are given for
reference.

Figure S3. Fault traces and sackungen (red lines) mapped using the lidar data (acquisition outlined
in orange). Gaps in mapped fault traces represent discontinuities in the fault trace, or places where
erosion (e.g., by active rivers) has obliterated geomorphic evidence of the fault. The surface
expression of the higher-displacement Wairau fault (a) is much structurally simpler than that of
the lower-displacement Awatere fault (b; Langridge et al., 2016). The surface deformation patterns
expressed at the Branch River and Saxton River sites are generally representative of those observed
along their respective faults. Other examples of relatively structurally simple faulting occur at
some locations along the Clarence and Hope faults to the south (which each have ~20 km of
cumulative displacement [Little and Jones, 1998, and references therein]) as well as along the
Awatere fault. Such examples may suggest the influence of other possible mechanisms locally
controlling the surface expression of deformation, including topographic effects and variations in
fault strike and dip (Barth et al., 2012; Khajavi et al., 2014). Such relatively structurally simple
sites are not representative of broader deformation patterns along their respective faults.
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Table S4. Offsets and ages of features and the Branch River and Saxton River sites (data from
Grenader et al., 2014; Knuepfer, 1992; Zink et al., 2014a; Mason et al., 2006b, and references
therein).

Figure S5. Lidar acquisition and processing report. The lidar data were collected along a total of
254 km from five fault segments in the Marlborough fault system, northern South Island, New
Zealand. Lidar swaths are (nominally) 1.2 km wide, centered on each fault. The shot density is ≥
12 shots/m
2
. These data were collected as part of NSF Grant EAR-1321914 (Dolan; Dolan and
Rhodes, 2016) for us by the US National Center for Airborne Laser Mapping (NCALM) and NZ
Aerial Mapping using an Optech Gemini Aerial Laser Terrain Mapper (ALTM) serieal number
06SEN195 mounted in a twin-engine Cessna 402B. Lidar was collected along the Wairau fault on
18 March 2014 with the sensor’s pulse rate frequency (PRF) set to 100 kHz. Lidar was collected
along the Awatere fault on 18–20 March 2014 using th same sensor, with PRF set to 125 kHz in
Multi-Puse Mode, which allows the laser-measured range to be twice as long as what the speed of
light allows for a given altitude. The aircraft was nominally at 1400 m above ground level (AGL)
for lines flown at this setting. Ground point classification and bare-earth digital elevation model
(DEM) construction was performed by automated routines using TerraSolid TerraScan Version
14.008 software, a surfer kriging algorithm, Perl, Python, and AML scripts. Bare-earth DEMs used
in this study have a pixel size of 0.33 m. Typical flight line height mismatch (Δz) in relatively flat
areas is 0.02–0.05 m; in steep terrain, Δz increases to 0.05–0.12 m. Geomorphic features expressed
by ≥ 20 cm of relief are discernable in the DEM, as determined by visual inspection. Features
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expressed by < 20 cm of relief will likely not be evident in the lidar, providing a minimum
measurement threshold.

Figure S6. Uninterpreted lidar hillshade digital elevation models of the Branch River (a) and
Saxton River (b) sites along the Wairau and Awatere faults, respectively. See supplementary
material Figure S3 for lidar acquisition and processing details.

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CHAPTER 5:
Highly variable latest Pleistocene–Holocene incremental slip rates on the Awatere fault at
Saxton River, South Island, New Zealand, revealed by lidar mapping and luminescence
dating

The following work is based on R. Zinke, J. F. Dolan, E. J. Rhodes, R. Van Dissen, and
C. P. McGuire (2017), “Highly variable latest Pleistocene–Holocene incremental slip rates on the
Awatere fault at Saxton River, South Island, New Zealand, revealed by lidar mapping and
luminescence dating” published in Geophyscial Research Letters (doi: 10.1002/2017GL075048).

5.1 Abstract
Geomorphic mapping using high-resolution lidar imagery and luminescence dating reveal
highly variable incremental Holocene-latest Pleistocene slip rates at the well-known Saxton River
site along the Awatere fault, a dextral strike-slip fault in the Marlborough Fault System, South
Island, New Zealand. Using lidar and field observations, we measured seven fault offsets recorded
by fluvial terraces and bedrock markers. Improved dating of the offsets is provided by post-IR-
IRSL225 luminescence ages. Incremental slip rates varied from < 2 mm/yr to > 15 mm/yr over
intervals of thousands of years and tens of meters of slip, demonstrating order-of-magnitude
temporal variations in rate at a single site. These observations have basic implications for
earthquake fault behavior, lithospheric mechanics, discrepancies between geodetic and geologic
slip rates, and probabilistic seismic hazard assessment.

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5.2 Introduction
Fault slip rates reflect the interplay among multiple geologic and geodynamic processes
that are as-yet not fully understood. Such understanding, however, is essential to developing
theories of lithospheric mechanics and can inform debates such as those over the role of faults in
accommodating relative plate motion (e.g., Tapponier and Molnar, 1976), and discrepancies
between geodetically inferred slip deficit rates and geologic fault slip rates (e.g., Dolan et al.,
2016). Moreover, fault slip rates provide a basic input for earthquake recurrence models used in
probabilistic seismic hazard assessment (e.g., Field et al., 2014, 2015, 2017). Studies of fault
behavior in a variety of tectonic settings are necessary to determine whether slip rates vary in
predictable ways, and what factors influence their behavior. The key problem is that there exist far
too few incremental fault slip rate records from faults and fault networks around the world to allow
for full system-level analysis of the consistency of fault slip in time and space. The dearth of such
data sets leads us to revisit the well-known Saxton River site on the Awatere fault in northern
South Island, New Zealand. We use high-resolution lidar microtopographic data and a newly
developed infrared stimulated luminescence (IRSL) dating protocol to determine incremental
Holocene-latest Pleistocene slip rates for the Awatere fault at this site. Such incremental slip rate
records are key to understanding fault behavior through time and space, with basic implications
for earthquake recurrence, system-level fault interactions, and plate boundary mechanics.

5.3 The Saxton River Site
The Awatere fault is a principal right-lateral strike-slip fault within the Marlborough Fault
System (MFS), a network of mainly right-lateral faults that accommodate strain between the
dextral-reverse Alpine fault and the Hikurangi subduction margin (Fig. 1; e.g., Wallace et al.,
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2012). The Saxton River site consists of six progressively offset fluvial terraces and a bedrock spur
(Fig. 1). At the end of the Last Glacial Maximum, gravels aggraded throughout the Saxton River
valley, carving the valley edges and filling the valley to the elevation of the highest (T1) terrace
tread (Bull and Knuepfer, 1987; Mason et al., 2006b). Throughout latest Pleistocene-Holocene
time, the Saxton River has incised into the fill sequence, creating “degradational” terrace surfaces
or “treads” (T2–T6) at progressively lower elevations where the river floodplain temporarily
stabilized for periods of hundreds to thousands of years, depositing relatively thin units of sandy
gravel and silts (Fig. 1; Fig. S1 in the supplementary material; see also Mason et al., 2006b).
Terrace risers are the scarps between terrace surfaces and are denoted herein by the older tread
separated from the younger tread by a forward slash (e.g., T5/T6). Progressive strike slip along the
Awatere fault has been recorded by the terrace features and bedrock spur, resulting in a time-
transgressive sequence of fault offsets. Using different techniques for measuring and dating the
offset features, previous researchers have concluded that the slip rates were either constant
(Lensen, 1973; Mason et al., 2006b) or variable through time (Knuepfer, 1992; McCalpin, 1996;
Gold and Cowgill, 2011).

5.4 Offset Determinations
To measure the geomorphically expressed displacements recorded at Saxton River, we
combined mapping using high-resolution aerial lidar microtopographic data
(http://www.opentopography.org/; Dolan and Rhodes, 2016; Fig. S1; Text S2), with observations
made during 2012, 2014, and 2017 field seasons. We identified six offset features that can be
unequivocally restored across the fault, and a potential seventh that provides a robust maximum
estimate of slip (Fig. 2; Fig. S3). These include, from oldest and most-offset, to youngest and least-
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offset: (1) a bedrock ridge crest and associated T1/bedrock onlap (i.e., terrace inner edge) contact,
(2) the potential edge of initial river incision into T1, (3) channel bed forms within the T2 surface,
(4) the T1/T2 riser, (5) the T2B/T3 riser, (6) the T3–T4/T5 riser, and (7) the T5/T6 riser. Using
hillshaded, contoured topography, and other visualizations of a lidar-derived 33 cm per pixel
digital terrain model (DTM), we progressively back-slipped one side of each offset feature relative
to the other until we determined the maximum and minimum sedimentologically plausible offset
values, as well as a preferred value range.
Many of the offset features were identified in previous studies (Lensen, 1973; Knuepfer,
1992; McCalpin, 1996; Mason et al., 2006b), and generally, our offset measurements are similar
to those reported by Mason et al. (2006b; Fig. S3). However, our lidar data allowed us to determine
several key, previously unrecognized geomorphic relationships. For example, the lidar data reveal
subtle bed forms within the T2 surface that suggest that the T2 terrace and T1/T2 riser underwent
a complex, multistage geomorphic history (Fig. S3). Specifically, an older, higher terrace remnant
(T2A) is separated from a younger, lower floodplain (T2B)—an observation that is supported by
our age estimate data, described below. Two sets of channels within the broader T2 terrace are
offset more than the T1/T2 riser (56 ± 2.0 m and 45 ± 3.0 m, respectively; explained below).
Additionally, the cuspate geometry of the T1/T2 riser and adjacent channel (Fig. 2d; Fig. S3)
indicate that a late stage of lateral incision cut the riser embankment, and overprinted, but did not
completely obliterate, the older channel features in T2. This late stage of T2 erosion may or may
not have modified the edge of the original T1 surface north of the fault, which marks initial river
incision into the T1 fill terrace, indicating that this is a maximum-possible offset for the T1/T2
riser.

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5.5 Age Determinations
We collected 36 luminescence samples from 10 hand-dug pits in the terraces (Fig. 3; Text
S4). The terrace stratigraphy revealed by the pit excavations typically consists of one or more bed
load gravel units (pebble–boulder-sized clasts in a silty sand matrix) that are generally capped by
silts and soils. The abandonment age of each terrace is represented by the youngest sand or gravel
floodplain bed load deposits in each pit, as these coarse-grained deposits record the final phase of
deposition during which the river had the erosive capacity to laterally trim the channel margins.
Most terraces at Saxton River are capped by silt deposits (typically ≤ 60 cm thick). These silts
were deposited either during the waning stages of floodplain occupation (during which
streampower was insufficient to laterally trim the coarse-grained terrace risers), or post-
abandonment avulsion events, or as eolian deposits. The silt ages therefore provide a robust
minimum age for the sands and gravels in each pit. In some pits, older phases of gravel deposition
are recorded beneath the younger bed load gravels. These older deposits, which are in most
instances lithologically indistinguishable from the overlying gravel deposits, provide a maximum
age for the most recent bed load gravel deposition. Gravel luminescence samples were taken from
the sandy matrix.
Thirty-four of the IRSL samples were processed according to the newly developed post-
IR-IRSL225 single K-feldspar grain procedure (Huntley and Baril, 1997; Rhodes, 2015; Lewis et
al., 2017). Age estimates are reported in thousands of years before 2017 (kyb2017). We calculated
the terrace tread abandonment ages using a two-step OxCal Bayesian statistical model (further
details in Text S4 [Bronk Ramsey, 2001; Rhodes et al., 2003]): First, we undertook a Bayesian
model for each pit individually, using the lithostratigraphic observations as input to create
sequences of ordered samples within OxCal (step 1), providing us with posterior age estimates for
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each sample constrained by the results of the other samples in their respective pits. Second, we
built a morphostratigraphic sequence age model for the different terraces, incorporating the terrace
risers as OxCal boundaries between dating results for each terrace. The age distributions
constrained by lithostratigraphic observation in step 1 were input within a phase for each terrace,
using only the results from sand or gravel deposited during the latest stage of floodplain
occupation. This two-step Bayesian age model approach allows us to make use of all available
IRSL data in order to constrain the ages of terrace occupation and deposition, and the ages of the
different terrace risers.
Finally, we consider that the samples from an individual bed load gravel or sand deposit
represent the age range of that deposit, which we term a “deposit age” (Text S4). In order to
estimate this age range we sum all the step 2 posterior age distributions from that deposit. This
approach makes no specific assumptions regarding the duration of development of each deposit
and provides a conservative measure of the age uncertainty that we use to date each terrace
abandonment event.

5.6 Slip Rate Determinations
The incremental slip rate history of the Awatere fault at Saxton River is determined by
matching terrace ages with the geomorphic offsets. Lensen (1964, 1973) suggested that for rivers
capable of laterally trimming faulted risers, the riser age is best constrained by the youngest bed
load gravels on the lower terrace floodplain. Geomorphic evidence and sedimentological reasoning
indicate that this model, referred to as a “lower-terrace reconstruction” (Cowgill, 2007), applies to
most of the offset terrace risers at Saxton River (Text S5). Specifically, (1) the coarse, sand, or
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pebble-boulder grain size of the terrace bed load gravels that we sampled indicates deposition by
high-energy flows with high erosive power to trim risers. (2) Right-lateral displacement of a terrace
riser on the eastern bank of Saxton River exposes the downstream section of the riser directly to
Saxton River streamflow, creating an “exposed corner” susceptible to fluvial erosion, rather than
shielding it (e.g., Bull, 1991; McGill and Sieh, 1993). (3) The terrace risers exhibit little evidence
of diachroneity (e.g., different slopes north and south of the fault) that might potentially indicate
incomplete trimming of the risers during lower terrace floodplain occupation (Cowgill, 2007; Gold
et al., 2009).
In contrast to the erosional geomorphic features discussed below, the T1/bedrock contact
is a depositional feature that records the onlap of the T1 “fill terrace” floodplain gravels onto the
underlying bedrock ridge that formed the valley wall. The 72.5 ± 7.5 m offset of the T1/bedrock
contact is therefore dated by the most recent phase of fluvial gravel deposition in the easternmost
T1 pit (12.9 +1.2/-1.0 kyb2017). Thus, the average latest Pleistocene-Holocene slip rate of the
Awatere fault at Saxton River is 5.6 +0.4/-0.3 mm/yr. T1 terrace abandonment coincided with
initial incision into the T1 surface. The edge of the original T1 surface (offset 71 +2.0/-17 m; Fig.
2b) is best dated by the youngest bed load gravels in the western T1 pit, nearest the T1/T2 riser
(11.3 +1.4/-1.2 kyb2017), which provides a robust maximum age for initial T1 incision.
The T2 bed forms are incised into T2A (Fig. 2c), indicating that they are younger than the
youngest T2A bed load gravels. However, the bed forms are not incised into T2B and are therefore
older than the youngest gravels in T2B. We therefore use the boundary age between the T2A and
T2B surfaces (which encompasses the T2A and T2B terrace ages, and all ages in between),
yielding 56 +3.0/-2.0 m of slip since 8.1 ± 0.8 kyb2017.
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The T1/T2 riser is the youngest, least-offset feature associated with T2 (Fig. 2d). The
channel system that was responsible for final shaping of the T1/T2 riser crosscuts all other features
preserved in T2 and is incised into the T2B surface. The age of T2B therefore represents a likely
close maximum age (7.6 +0.7/-0.8 kyb2017) for the 45 ± 3.0 m T1/T2 riser offset.
Both the T3–T4/T5 and T2B/T3 risers are dated by their respective adjacent lower terrace treads
(Fig. S3; Text S5). The 12.5 +3.0/-1.5 m T3–T4/T5 riser offset is dated by T5 floodplain
abandonment (4.3 +0.3/-0.4 kyb2017), and the 33.5 +2.5/-3.5 m T2B/T3 riser offset is dated by
T3 floodplain abandonment at 5.2 ± 0.5 kyb2017.
The 9.5 ± 1.0 m of offset recorded by the T5/T6 riser is best dated by the gravel age of the
T6 terrace tread (1.8 ± 0.3 kyb2017). This offset and age are supported by the Mason (2004) paleo-
earthquake record from their T1 trench (Fig. S6), as well as by the smallest (~2.5 m) geomorphic
offsets observed just east of Saxton River (Mason, 2004; Zinke et al., 2016b). Specifically, the
most recent surface-rupturing earthquake (MRE) at Saxton River occurred < 300 calendar year
B.P. (Mason, 2004). This event (inferred to be the 16 October 1848 M 7.4–7.5 Marlborough
earthquake [Grapes et al., 1998; Mason, 2004]) was the most recent of an apparent four-event
cluster of events that likely occurred during the past c.1 kyr (i.e., since T6 was abandoned c.1.8
ka; Mason, 2004). If each of these four events had ~2.5 m of slip, the resulting ~10 m of slip
closely matches the 9.5 ± 1.0 m offset we determine for the T5/T6 riser. Moreover, the paleo-
earthquake age data appear to support a preceding period of slow slip, with only two additional
surface ruptures documented between c.1 ka and 4 ka (Mason, 2004). This supports our lower-
terrace reconstruction and use of the 4.3 +0.3/-0.4 kyb2017 abandonment age for T5 to date the
12.5 +3.0/-1.5 m T3–T4/T5 riser offset. We include the age and displacement of the MRE in our
final analysis of incremental slip rates.
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Incremental slip rates were calculated using a Monte Carlo sampling technique (Text S7).
Our method is similar to that of Gold and Cowgill (2011) in that it discards values that result in
negative (i.e., left-lateral) slip rates; however, our sampling scheme draws random displacement
and age values according to the probability density functions of each respective measurement,
allowing for propagation of uncertainties. The resulting incremental slip rates (Fig. 4; Table S8)
between geomorphic features are 3.4 +1.0/-0.8 mm/yr (T1/bedrock contact to T2 bed forms), 16.8
+28.2/-7.6 mm/yr (T2 bed forms to T1/T2 riser), 6.2 +3.4/-1.7 mm/yr (T1/T2 riser to T2B/T3
riser), 15.2 +9.6/-4.6 mm/yr (T2B/T3 riser to T3–T4/T5 riser), 1.4 +0.5/-0.4 mm/yr (T3–T4/T5
riser to T5/T6 riser), and 4.2 +0.6/-0.5 mm/yr (T5/T6 riser to MRE).

5.7 Discussion and Conclusions
The new age and displacement data described above demonstrate that incremental slip rates
of the Awatere fault at Saxton River varied by a factor of ~12, over tens of meters of slip and
millennial timescales during Holocene and latest Pleistocene time (Fig. 4). During latest
Pleistocene and early Holocene time (~12–8 ka), the fault exhibited a relatively slow slip rate of
~3 mm/yr. This was followed by a period of exceptionally rapid slip rate (three separate rates that
range from ~6 to 17 mm/yr), involving ~45 m of slip between ~4–8 ka. Subsequently, since ~4 ka
the fault has been slipping at a much slower slip rate of ~3 mm/yr, encompassing a period of
extremely slow (~1.4 mm/yr) slip rate between ~1.8 and 4.3 ka.
Importantly, all of these slip rate data were documented at the same site, providing a robust
record of fault behavior that is not complicated by potentially temporally different fault behavior
at widely separated sites along strike. The extreme slip rate variability observed at Saxton River
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raises important issues about the mechanics of fault behavior and the use of geologic slip rates in
seismic hazard analysis and the search for strain transients.
Specifically, these extreme rate changes suggest the possibility that either the strength of
the fault is varying through time and/or that the rate of elastic strain accumulation has changed
with time. For example, it has been proposed that variations in slip rate could be caused by
alternating periods of fault strengthening and weakening, controlled by either temporal changes to
the fault zone rocks themselves (e.g., Chéry andVernant, 2006; Dolan et al., 2007; Oskin et al.,
2008; Dolan et al., 2016), or by addition or removal of gravitational loads (e.g., Hetzel and Hampel,
2005; Luttrell and Sandwell, 2010). Alternatively, others have suggested that variations in slip rate
could be controlled by external factors, such as regional kinematic fault interactions (Dolan et al.,
2007), or true temporal changes in relative plate motion rate (e.g., Anderson, 1975; Romanowicz,
1993; Pollitz et al., 1998; Dolan et al., 2016; Meade and Loveless, 2017). Whatever the causes of
the variable slip rate behavior at Saxton River, these data add to a growing list of examples from
other faults that exhibit temporally variable slip rates (e.g., Wallace, 1987; Friedrich et al., 2003;
Weldon et al., 2004; Dolan et al., 2007, 2016; Sieh et al., 2008; Gold and Cowgill, 2011;
Goldfinger et al., 2013; Ninis et al., 2013; Onderdonk et al., 2015).
The possibility that such variations in rate could be more common than generally thought,
perhaps masked by the dearth of well-constrained incremental slip rate records, raises concern
about comparisons of geologic and geodetic rates in the search for strain transients: When
comparing such data sets, what is the “correct” slip rate to use? In the case of Saxton River, if the
rate of elastic strain accumulation on the Awatere fault has remained constant through latest
Pleistocene-Holocene time at ~5.5 mm/yr (based on the ~13 ka average slip rate we determined),
use of the very slow (~1.4 mm/yr) slip rate averaged between 1.8 and 3.3 ka might be interpreted
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as an example in which the rate of elastic strain accumulation far exceeds the “longterm” geologic
slip rate. In contrast, if we had happened upon Saxton River ~4 kyr ago, at the end of the mid-
Holocene period of extremely fast slip, especially at the end of either of the exceptionally fast
periods ~8 ka and 5–6 ka (~17 and ~15 mm/yr), comparison with the geodetic data might have
suggested that the fault was storing elastic strain energy much more slowly than the geologic slip
rate.
Such extreme variations in rate suggest the alternative possibility that the rate of elastic
strain accumulation along the Awatere fault may not have remained constant through time. While
it is impossible to directly document any potential mid-Holocene changes in elastic strain
accumulation rate, an acceleration in strain accumulation could help to explain the extreme
variation between the late-Holocene period of slow slip and the preceding period of very rapid
slip. In another possible example of such behavior, Dolan et al. (2016) noted that in the San
Andreas Fault (SAF) Wrightwood record (Weldon et al., 2004), within each of the three-to five-
earthquake-long periods of alternating slow (~15 mm/yr) and fast (~89 mm/yr) slip rate at that site,
the rate of elastic strain release yielded a relatively continuous slip rate within each period. This
suggests that the SAF may be “keeping up” with a variable elastic strain accumulation rate that
remained relatively constant over tens of meters of slip and multiple earthquakes within each
supercycle (Dolan et al., 2016). To provide context for the degree to which Saxton River slip rates
vary temporally, the variation in rate at Wrightwood between three to five earthquake-long fast
and slow periods is a factor of ~6 (~15 versus ~90 mm/yr). Assuming, as discussed above, that the
average slip per event at Saxton River was ~2.5 m, the millennial-scale slip rate variations there
likely also spanned three to five or more earthquakes, similar to the number of events in each fast
or slow period at Wrightwood. But the resulting factor of ~12 rate variation on the Awatere fault
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is even more extreme than at Wrightwood. These observations demonstrate the need for caution
in comparing geodetic and geologic slip rates.
The slip rate variations revealed by the Saxton River data also raise important concerns
about the use of geologic slip rates in seismic hazard analysis. Geologic slip rates are a basic input
for most probabilistic seismic hazard models (e.g., UCERF3 [Field et al., 2014, 2015, 2017]). In
situations such as Saxton River, however, the resulting probabilities of earthquake occurrence will
covary with the different incremental slip rates through time. For example, if we only used a slip
rate averaged over the late-Holocene slow period, the resulting seismic hazard would be relatively
low. Conversely, if we happened to measure the slip rate at the end of the mid-Holocene fast
period, the resulting earthquake probabilities would be approximately an order of magnitude
higher. It might therefore be tempting to simply use longer-term slip rates averaged over many
tens to hundreds of kiloyears, with the expectation that rate variations may average out over these
timescales. However, slip rates have been shown to vary in some systems over longer timespans
due to fault evolution, changes in relative plate boundary motions, or other causes (e.g., Wallace,
1987; Marco et al., 1996; Friedrich et al., 2003; Bergen et al., 2017). Resolution of this problematic
issue will require the addition of many more incremental slip rate records, similar to the one we
document at Saxton River, for many more faults in different tectonic settings and over a wide range
of timescales. Comprehensive incremental slip rate records from all major faults in a fault system
are required to understand system-level behavior through time, which will in turn facilitate a
system-level search for causes, as well as the predictability of slip rate variability.

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5.8 Figure Captions
Figure 1. (a) Tectonic map of New Zealand. The MFS is a system of faults that transfer strain
between the Alpine fault (Alp F) and Hikurangi subduction zone (Hik). (b) Major strike-slip faults
of the MFS (J-K is Jordan-Kekerengu fault). The black arrow indicates NUVEL-1A relative plate
motion vector (DeMets et al., 1994). (c) the Saxton River site comprises six terraces and a bedrock
promontory, shown by colors on lidar hillshade. The green dots are sample pits, labeled by location
number. The black rectangles are paleoseismic trenches by Mason (2004).

Figure 2. (a–g) Hillshaded lidar maps showing preferred restorations for offset geomorphic
features. The arrows show the primary feature(s) restored in each panel: (a) T1/bedrock contact;
(b) edge of T1 original (uneroded) surface marks possible edge of initial fluvial incision into T1;
(c) remnant bed forms within T2, predating final T1/T2 modification; (d) T1/T2 riser and channel
at base of riser, shaped by fluvial erosion during latest stages of T2 occupation; (e) T2B/T3 riser;
(f) T3–T4/T5 riser; and (g) T5-T6 riser. (h) 50 cm contour map on DTM colored by elevation.

Figure 3. Schematic diagram of IRSL sample pits, labeled by pit number in terraces (T1–T6). The
black dots represent IRSL samples and corresponding uncalibrated sample ages within the
stratigraphic context of each pit. Elevation in meters above sea level (asl); all horizontal distances
arbitrary except where indicated.

Figure 4. (a) Displacements and ages of offset features. The error bars show 95% uncertainty
limits. (b) Monte Carlo sampling of displacements and ages yields incremental slip rates (mm/yr
± 1-sigma uncertainties) for each interval. (c) Terrace tread elevation versus tread age (gray field)
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superimposed on incremental fault slip history. Note that Saxton River incision rate generally
tracks incremental fault slip rate, suggesting that incision may be controlled by local base level
changes related to vertical tectonic motions. (d) Incremental slip rates versus feature age. In
Figures 4a and 4c, the potential edge of initial incision into T1 is shown as dashed line.

5.8.1 Captions for Supplementary Materials
Figure S1. Uninterpreted hillshaded lidar digital terrain model (DTM) of the Saxton River site
(Dolan and Rhodes, 2016), as shown in main text Figure 1. Pixel dimensions are 33 cm × 33 cm.
Lighting angle is from azimuth 315°, and altitude 45°. North is rotated to 010°. Data are available
from OpenTopography (https://opentopography.org).

Text S2. Lidar collection and processing details. The lidar data we used to map the Saxton River
site were collected as part of a 300 km
2
acquisition along five sections of fault in the Marlborough
fault system, South Island, New Zealand, with acquisition funded by NSF Grant EAR-1321914
(Dolan). These data are now available through OpenTopography (https://opentopography.org;
Dolan and Rhodes, 2016). The acquisition was completed in 2014 by the US National Center for
Airborne Laser Mapping (NCALM) and NZ Aerial Mapping using two different sensors for the
Awatere fault: an Optech 3100EA Airborne Laser Terrain Mapper (ALTM) operated with a pulse
rate frequency (PRF) of 70–100 kHz; and an Optech Gemini ALTM operated in Multi-Pulse mode
with a PRF of 125 kHz. These surveys were combined to produce a point cloud with a shot density
of ≥ 12 shots/m
2
along the length of the Awatere fault, and with a raw return density of ~18
shots/m
2
at the Saxton River site. The data were processed to make digital elevation models
(DEMs) and bare-earth digital terrain models (DTMs) using TerraSolid TerraScan Version 14.008
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software, a surfer kriging algorithm, and other Perl, Python, and AML scripts. The DTMs used in
this study have a pixel size of 0.33 m. Additional details of the lidar acquisition and processing
can be found in the NCALM survey report on OpenTopography
(https://cloud.sdsc.edu/v1/AUTH_opentopography/www/metadata/2014_Dolan_NewZealand.pd
f). These data allowed us to resolve topographic features expressed by ≥ 20 cm, as determined by
visual analysis and field checks at the Saxton River site.

Figure S3. Descriptions and restorations of offset geomorphic markers. We precisely measured
and documented the offset geomorphic features at the Saxton River site by digitally mapping on
high-resolution aerial lidar micro-topographic data (available from OpenTopography,
http://www.opentopography.org/; Dolan and Rhodes, 2016), and using observations made during
2012, 2014, and 2017 field surveys. To determine the total offset recorded by each feature, we
progressively back-slipped one side of the feature relative to the other using hillshaded, slope
angle, slope aspect, and contoured topographic representations of our 0.33 m-pixel lidar-derived
digital terrain model (Text S2). Commonly, we found that the largest source of uncertainty in
restoring the offset markers was in how we projected the piercing lines into the fault zone. Widely
used digital tools for determining offset likelihoods, though useful in many situations, require the
assumption of a unique piercing line projection into the fault, and therefore could not fully account
for the uncertainty in our data. Our visual estimates of displacement, however, account for the full
range of geomorphic and topographic uncertainty associated with each feature. To quantify the
relative likelihood of offset values for each displaced feature, we first determined maximum and
minimum values, outside of which the feature geometry is sedimentologically implausible. In
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cases where a single offset value appeared more likely than the rest, we described the offset using
a triangular probability density function (PDF), with the peak value being the most likely. In cases
where a range of values seemed equally likely, we used a trapezoidal PDF for which the “preferred
range” forms a boxcar within the minimum and maximum values. For convenience in reporting
our values in Table 1 and Fig. 2 in the main text, we used only a single “preferred” value for each
measurement, even in cases where the offset likelihood is better described by a trapezoidal PDF
with a preferred range. In such cases, the reported “preferred” value is simply the average of the
two ends of our preferred range.
The offset geomorphic features were restored by back-slipping one side of the feature
relative to the other, and visually determining the best restoration. These panels show our offset
restorations. The top row of each panel shows uninterpreted and unrestored images of our lidar-
derived hillshaded digital terrain model (DTM) and contoured topography (left and center,
repsectively). The rightmost figure on the top row shows our interpretations of the geomorphic
features, with strong black arrows indicating the piercing lines that we restore. Below, we show
our restorations using our hillshaded DTMs (middle row) and contoured topography maps (bottom
row). For each offset feature, we show the minimum and maximum sedimentologically plausible
restorations. For cases where a single value best restored the offset, we show a “preferred”
restoration; and for cases in which all restorations all equally likely within a range of values, we
show the upper and lower ends of the “preferred range.”

S3.1 – (Panel A) Bedrock spur and T1/bedrock offset
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The bedrock spur and T1/bedrock depositional contact are the oldest, most-offset features
at Saxton River. These features formed after the last glacial maximum (LGM), when Saxton River
discharge refreshed the bedrock valley walls and filled the Saxton River valley to the present-day
height of T1 with aggradational “fill” gravels (e.g., Mason et al., 2006b). The crest of the bedrock
spur is poorly defined, especially south of the fault. Uncertainty in the trend of the ridge crest north
of the fault results from a series of notches or benches in the ridge, which appear to be left-stepping,
and which may be the result of secondary faulting (shown in Fig. 1 in the main text) or meteoric
erosion. Uncertainty in defining the correlative ridge crest south of the fault results from the limited
fault-perpendicular extent and diffuse nature of the crest, indicated by a topographic high. Because
of these large uncertainties, we find the ridge crest to be an unreliable geomorphic marker, and
instead use the T1/bedrock contact at the base of the bedrock spur to define the oldest offset feature
at Saxton River.
The T1/bedrock depositional contact offers a more reliable offset marker. Unlike the
erosionally formed fluvial terrace features discussed below, the T1/bedrock contact represents a
depositional feature. The T1/bedrock contact formed when post-LGM gravels aggraded
throughout the Saxton River valley (e.g., Mason et al., 2006b, and references therein), consistent
with the climate-controlled aggradational fill terrace model of Bull and Knuepfer (1987).
Deposition of the aggradational T1 gravels against the flanks of the bedrock spur (i.e., the eastern
wall of the LGM Saxton River drainage) formed a buttress unconformity, the timing of which is
dated directly by the depositional age of the gravels themselves, as discussed in Text S4. The
T1/bedrock contact formed a gently curving feature, which presently extends 100s of m north of
the fault, and > 100 m south of the fault. In places, the true boundary of the T1/bedrock contact
north of the fault is obscured by small alluvial fans that were deposited after T1 abandonment. The
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alluvial deposits lead to apparent widening of the bedrock spur north of the fault, and lessen the
apparent offset. In our offset restorations, we use exposed portions of the bedrock slope to help
determine the true projection of the northern piercing line through the fan material. Our estimates
therefore reflect a reasonable range of the true orientation of piercing lines defining this feature.
Our projections also account for erosion and colluvial deposition in the vicinity (± 20 m) of the
fault. Based on these observations, our estimates of offset are described by a trapezoidal
probability distribution, with minimum and maximum values ranging from 65.0–80.0 m, outside
of which the restorations are geomorphically implausible. Our restorations have a preferred range
of 70.0–75.0 m, between which we find all restorations to be equally highly likely.

S3.2 – (Panel B) Offset of initial incision into T1
The far-field (~20–75 m from the fault) projection of the northern and southern portions of
the T1/T2 riser into the fault forms a smooth, curvilinear form. This smoothly varying shape may
represent the configuration of the Saxton River channel edge when it initially incised into the
original T1 floodplain (i.e., the edge of initial incision). Subtle topographic differences across T1
near the T1/T2 riser seem to indicate a higher, less-eroded surface representing the original,
immediately pre-incision T1 floodplain. This unmodified surface is geomorphically distinct from
topographically lower erosional features modified by subsequent river incision, as well as faulting,
and weathering and erosion. Above, and in main-text Fig. 2b, the darker orange surface shows the
present-day extent of the original, unmodified T1 floodplain; the paler orange demarks parts of T1
that appear to have been modified by erosion after initial incision into T1. The channel edge could
have maintained a smooth, continuous geometry throughout the first few decimeters of river
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incision, indicated by the vertical difference between the top of the original T1 terrace floodplain
and the younger, more modified parts of the surface. With increasing fault slip and further river
incision, the T1/T2 riser would have developed a more cuspate geometry, as shown above. Faulting
and weather-driven erosion further modified parts of the T1 surface in the vicinity of the fault
zone. The edge of the original T1 floodplain creates a smooth, arcuate form when restored by 71
± 2 m. However, the T1/T2 riser was likely modified by fluvial erosion during T2 occupation
(described in more detail below). Specifically, lateral incision caused by the Saxton River is shown
by the cuspate geometry of the terrace risers throughout the site. A later stage of lateral incision
shaped the base of the T1/T2 riser into such a cuspate geometry. If this geometry was formed by a
pulse of lateral incision propagating from upstream (north), then the northern edge of the original
T1 surface may have been laterally trimmed back, enhancing the apparent size of the offset. The
apparent edge of incision into the original T1 floodplain therefore represents a robust maximum
offset. Because we have no constraints on the likely amount of trimming that would have occurred
in such an event, we use the offset recorded by the lower, T2 surface as the absolute minimum
amount of offset recorded by the T1 original surface edge. Based on these constraints, we use a
trapezoidal PDF with a maximum value of 73 m and a boxcar range of 69–71 m (based on our
plausible restoration of the T1 original surface edge), and a minimum value of 54 m, based on the
minimum allowable restoration of the next least-offset features in the lower, T2 floodplain
(described below).

S3.3 – (Panel C) Offset of T2 bedforms
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Our lidar data allowed us to identify several previously unrecognized sedimentologic
relationships between geomorphic features preserved in terrace T2, including the T1/T2 riser, and
several preserved fluvial bedforms. These relationships indicate that the T2 terrace and the T1/T2
riser underwent a complex, multi-stage geomorphic history. We found that T2 is best divided into
two distinct surfaces based on differences in elevation and morphology. A 1–1.5 m-high, roughly
N–S trending scarp (shown in Fig. 1 and Fig. 2c in the main text) separates a higher, older surface,
which we call T2A, from a younger, lower surface, which we call T2B. These surfaces differ in
terms of their microtopographic relief: whereas T2A is characterized by pronounced channel
morphology preserved in the microtopography, T2B has relatively little microtopographic relief.
The pronounced channel morphology evident in T2A may be original to the Saxton River
floodplain during T2A occupation, or it may be the result of fluvial dissection that occurred during
high-river stages (e.g., flooding events) when T2B was the primary floodplain. Our distinction
between T2A and T2B is also supported by our age data (see main text and supplementary Text
S4). The latest stage of T2A bedload gravel deposition is ~1.3 ky
older than the latest stage of T2B bedload gravel deposition (8.9 +1.1/-1.9 kyb2017 vs 7.6 +0.7/-
0.8 kyb2017, respectively; see Table 1, Text S4). Understanding this kind of multi-stage
depositional and erosional history of geomorphic development is essential to interpreting the two
sets of landforms associated with T2, which record distinctly different displacements.
Two sets of differently offset landforms are associated with T2: (1) an older, more-offset
series of fluvial bedforms incised into T2A; and (2) younger, less-offset channel features incised
into T2B, and the T1/T2 riser. The microtopography of T2 indicates several preserved channel
forms incised into the T2A surface. Specifically, two channel thalwegs can be restored across the
fault, bordered by a channel bar (interfluve) between them, and a sharply defined channel margin
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at the eastern edge of the eastern channel. These features restore well with 56 +3.0/-2.0 m of back
slip. Uncertainties in the amount of offset recorded by these features result from the slight
curvature of the bedforms as they project into the fault, and later erosion that removed part of the
easternmost channel margin immediately south of the fault, as discussed below.

S3.4 – (Panel D) T1/T2 riser and channel features offset
The late-stage, erosionally modified T1/T2 riser is offset notably less than the T2 bedforms.
The riser is characterized by a curvilinear (cuspate) shape. It exhibits a pronounced difference in
height across the fault: it is ~1.5 m high north of the fault, and ~2.5 m high south of the fault. Both
north, and immediately south of the fault, it is bounded by a channel, as shown above.
Uncertainties in restoring the T1/T2 riser stem from: (1) the curvilinear, cuspate shape of
the riser; (2) the oblique strike of the riser as it projects into the fault zone (> 20° off-normal to the
fault); (3) ambiguity as to how much of the curvature in riser strike is the result of fluvial processes,
and how much is the result of tectonic action such as fault drag; and (4) a relatively broad (> 5-m-
wide), slightly curved fault zone with multiple geomorphically expressed surface fault traces (see
main-text Fig. 1c). We assume that the curvature of the restored T1/T2 riser is solely the result of
fluvial processes that occurred when the Saxton River occupied T2, and not the result of post-T2-
abandonment modification by erosive processes, or fault drag, either of which would imply a larger
offset. Our interpretation is supported by the conformal geometry of the channel at the base of the
T1/T2 riser (shown above; main text Fig. 2D). This channel follows the base of the riser from north
of the fault, to ~10 m south of the fault, where the riser strike abruptly changes from a more NE–
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SW, to a more N–S strike. These observations imply that a late stage of river incision cut the basal
channel and shaped the riser into a cuspate shape, at or near the end of T2 occupation.
Based on this interpretation, our measurements of offset account for the full range of these
uncertainties, and restored the riser such that it forms a clearly defined, sedimentologically
plausible arc across the fault. We use a trapezoidal PDF to describe the offset probability, with a
minimum value of 42.0 m, a preferred range of 44.0–46.0 m, and a maximum value of 48.0 m.
The microtopography of T2 further suggests the existence of another geomorphic marker
indicating ~45 m of slip since c. T2B time. Specifically, a topographically subtle, ~20-cm-high
channel margin is evident in T2B, north of the fault. This channel margin projects sub-
perpendicularly into the fault, and is on-trend with the western edge of the more sharply defined
channel south of the fault. Immediately to the south of the fault, the corresponding western channel
margin is disturbed by a pressure ridge, resulting from a bend in the fault. The channel margin
restores best at ~45 m, within the range of the T1/T2 riser offset.

S3.5 – (Panel E) T2B/T3 riser offset
The T2B/T3 riser is 6–7 m high, and has similar slope north and south of the fault. There
is little or no vertical difference (< 1 m) in the T3 tread elevations across the fault. The strike of
the T2B/T3 riser exhibits an overall concave shape, especially south of the fault, but is relatively
linear within 20–40 m of the fault scarp. The riser is continuous across the fault, but is limited in
its fault-perpendicular extent north of the fault, adding a small amount of uncertainty to the
restoration. Additionally, the presence of dense vegetation (including tall grasses and matagouri
bushes) along the base of the riser, could not be completely removed in processing of the lidar
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data, resulting in small distortions at the edge of the T2B/T3 riser and the T3 tread, and adding a
further small amount of uncertainty to the restoration. In spite of this, our estimates of displacement
define a relatively narrow range, described by a trapezoidal PDF, with a minimum value of 30.0
m, a preferred range of 33.0–34.0 m, and a maximum value of 36.0 m.

S3.6 – (Panel F) T3–T4/T5 riser offset
The T3–T4/T5 riser is the next younger, less-offset landform preserved at Saxton River.
The riser represents the basal bounding feature of the T4 terrace north of the fault, and the T3
terrace south of the fault. This configuration implies that after the river stabilized to form the T3
terrace, it incised and re-stabilized briefly – creating the T4 terrace – before incising again to form
the T5 terrace tread. Sometime after the river began incising into T4, it swept eastward, relative to
its previous course, removing all remnants of T4 south of the fault. Thus, the correlative to the
T4/T5 riser north of the fault is the T3/T5 riser south of the fault (Mason et al., 2006b). Because
of the height difference between the two sections of the riser (the T4/T5 riser is 5–6 m tall north
of the fault, whereas the T3/T5 riser is 7–8 m tall south of the fault), the middle and top of the T3-
T4/T5 riser do not present valid piercing lines. Instead, we match only the base of the riser (where
the most recent lateral incision occurred) across the fault. The largest source of uncertainty results
from the fact that the riser is somewhat curved near, both north and south of the fault, creating a
concave shape. It is difficult to determine the extent to which this curvature is the result of lateral
river erosion during occupation of the T5 tread, and how much is the result of erosion that may
have occurred after T5 abandonment. For instance, runoff channeled along the fault scarp could
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have partly eroded the T4/T5 riser directly north of the fault. Failure to recognize and account for
such erosion could result in overestimation of the offset.
We use a trapezoidal PDF to estimate the offset, with a minimum value of 11.0 m, a
preferred range of 12.0–13.0 m, and a maximum value of 15.5 m. Our maximum value, though
unlikely, assumes that there has been no post-T5 abandonment erosion of the T4/T5 riser. Our
minimum and preferred estimates, however, assume there has been some post-T5 abandonment
erosion of the T4/T5 riser, and we use piercing lines that project from several 10s of m distance
from the fault. We note that all plausible restorations of the T3-T4/T5 riser are larger than the
offset of the T5 channels in the lower adjacent tread. The significance of this is further discussed
in Text S5.

S3.7 – (Panel G) T5/T6 riser offset
The youngest, least-offset feature at Saxton River is the T5/T6 riser. The riser is ~2–2.5 m
high along its length, separating the T6 terrace tread (which is ~1.6 m above the active Saxton
River floodplain) from the T5 tread. The strike of the T5/T6 riser is slightly different north and
south of the fault, and the riser curves by ~15° within 10 m south of the fault. The slope of the riser
is similar north and south of the fault. We used a trapezoidal PDF to represent the offset of the
T5/T6 riser, with a minimum offset estimated to be 8.5 m. Our preferred range for this offset is
9.0–10.0 m, and our maximum estimate is 10.5 m.
Additionally, our lidar data allowed us to discern a series of topographically subtle
channels and interfluves within the T5 tread. These features include at least two shallow channel
thalwegs and two interfluves. These channel features are offset by a similar amount to the T5/T6
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riser. As discussed further in Text S5, we do not consider these channel forms as an independent
data point from the T5/T6 riser. Instead, these channels most likely resulted from erosion during a
transient flooding event, in which the Saxton River overtopped its banks sometime late during its
occupation of the T6 floodplain and eroded these shallow channels into the fine-grained capping
silts.

Text S4. IRSL sample collection, processing, and analysis. Description of the IRSL dating process,
and two-step Bayesian age model.

Text S5. Slip rate determinations, providing detailed descriptions of how we dated the offset
markers to reconstruct the displacement-time history of the Awatere fault at the Saxton River site.

Figure S6. Comparison of displacement-time history of the Awatere fault at Saxton River
determined in this study (A), and earthquake chronologies (B) from paleoseismic trenches in the
terrace T1 graben (after Mason [2004] Figure 3.8a). The three–four youngest surface ruptures
along this stretch of the Awatere fault occurred after T6 abandonment (~1.8 ka). Assuming
relatively similar slip of ~2.5 m in each of those events (estimated from the least-offset nearby
geomorphic markers [Mason, 2004; Zinke et al., 2016b]), this sequence of earthquakes likely
resulted in ~10 m of slip. This is consistent with the ~10 m offset recorded by the T5/T6 riser,
assuming a lower-terrace reconstruction. The T3-T4/T5 riser experienced an additional 1–3
earthquakes. Again assuming ~2.5 m of slip per event, this would result in an additional 2.5–7.5
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m of slip, consistent with the offset and age of the T3-T4/T5 riser if we use a lower-terrace
reconstruction. Though some variability in slip-per-event at a site is expected, the good agreement
between total offset expected from Mason’s (2004) earthquake record based on an average,
approximate displacement value, and the slip rate record presented in this study further supports
our use of lower-terrace reconstructions for the T5/T6 and T3-T4/T5 risers. The frequency of
surface-rupture earthquakes recorded in Mason’s (2004) paleoseismic record decreases with age
prior to c. 5–6 ka. We suggest that this could be due to the diminished ability to resolve events
with increasing age and deformation, and therefore may not reflect the true frequency of surface
rupturing events prior to the mid-Holocene.

Text S7. Detailed description of Monte Carlo analysis and slip rate determinations.

Table S8. Offsets, ages, and slip rates recorded by features preserved at the Saxton River site.
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CHAPTER 6:
Multi-millennial incremental slip rate variability of the Clarence fault at the Tophouse
Road site, Marlborough fault system, New Zealand

The following work is based on R. Zinke, J. F. Dolan, E. J. Rhodes, R. Van Dissen, C. P.
McGuire, A. E. Hatem, and N. D. Brown, “Multi-millennial incremental slip rate variability of
the Clarence fault at the Tophouse Road site, Marlborough fault system, New Zealand”
submitted to Geophyscial Research Letters (manuscript number 2018GL080688).

6.1 Abstract
Incremental slip rates of the Clarence fault, a dextral fault in the Marlborough fault system
of South Island, New Zealand, varied by a factor of 4–5 during Holocene–latest Pleistocene time,
as revealed by geomorphic mapping and luminescence dating of faulted fluvial landforms at the
Tophouse Road site. We used high-resolution lidar microtopographic data and field surveys to
map the fine-scale geomorphology and precisely restore the offset features. We dated the offsets
using a stratigraphically informed protocol for infrared stimulated luminescence dating. These data
show that incremental slip rates varied from ~2.0–9.6 mm/yr, averaged over multiple earthquakes
and millennial timescales. Comparison to incremental slip rates of the nearby Awatere fault
suggest that these faults may behave in coordinated (and opposite) fashion. This study adds to a
growing body of evidence suggesting that incremental slip rate variation spanning multiple
earthquake cycles may be more common than previously recognized.

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6.2 Introduction
Fault slip rate data are key to understanding spatiotemporal patterns of earthquake
occurrence and can provide insights into the processes governing lithospheric dynamics and plate
boundary behavior. Furthermore, fault slip rates provide a basic input for most seismic hazard
assessment strategies (e.g., Stirling et al., 2012; Field et al., 2017). Early conceptual models of
fault behavior assumed that slip rates are relatively constant over timescales spanning several
earthquakes (e.g., Shimazaki and Nakata, 1980). Emerging evidence suggests, however, that
whereas some faults indeed exhibit relatively constant slip rates over time (e.g., Van der Woerd et
al., 2002; Kozacı et al, 2009; Gold and Cowgill, 2011; Salisbury et al., 2018), others appear to
undergo periods of accelerated strain release spanning multiple earthquake cycles (e.g., Wallace,
1987; Weldon et al., 2004; Mason et al., 2006b; Gold and Cowgill, 2011; Ninis et al., 2013; Dolan
et al., 2016; Zinke et al., 2017). Constraining the incremental slip history of a fault requires a
relatively rare set of geologic conditions (i.e., multiple datable progressively offset features sharing
a common slip history). Thus, the ability to assess whether faults commonly exhibit episodes of
elevated strain release has been severely limited by the number and quality of data available.
In this study, we precisely constrain the incremental slip history of the right-lateral
Clarence fault at Tophouse Road, northeastern South Island, New Zealand. Specifically, we use
lidar microtopographic maps and field data to accurately measure fault displacements recorded by
a series of progressively offset fluvial landforms and employ a stratigraphically informed
luminescence dating protocol to date the landforms. From these measurements, we determine the
incremental slip rates of the Clarence fault spanning Holocene and latest Pleistocene. Finally, we
discuss the implications of the temporal patterns of strain release observed at Tophouse Road in
relation to the use of geologic slip rates in active tectonic studies and seismic hazard assessment.
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6.3 The Tophouse Road site, Clarence fault
The Clarence fault is one of the four principal dextral strike-slip faults within the
Marlborough fault system (MFS) of northern South Island, New Zealand. Together, these faults
accommodate most of the relative Pacific-Australian plate boundary motion between the
Hikurangi subduction margin to the northeast, and the Alpine fault to the southwest (Fig. 1; e.g.,
Van Dissen and Yeats, 1991; Wallace et al., 2012; Litchfield et al., 2014). The Tophouse Road
site is located at the western edge of the junction between the Clarence and Elliott faults, where
the Clarence fault crosses the western bank of the southward-flowing Clarence River (Kieckhefer,
1979; Knuepfer, 1992). The site comprises a series of faulted and progressively offset fluvial
terrace risers and channels that formed at high angle to the fault trace, providing ideal piercing
lines for offset restoration.
The fluvial depositional history of the Tophouse Road began when late Pleistocene-aged
gravels mobilized by glacial retreat after the Last Glacial Maximum (LGM) aggraded throughout
the upper Clarence River Valley, buttressing a large glacial moraine that bounds the western edge
of the site and forming the T1 floodplain (Fig. 1c; Bull and Knuepfer, 1987; Knuepfer, 1992). As
sediment supply waned, the Clarence River incised into T1, eventually isolating the T1 terrace
tread from further river occupation. Further episodes of incision and floodplain stability led to the
formation of younger terrace treads (T2–T4; Fig. 1c) separated by steep terrace risers, which we
denote as the tread names separated by a slash (e.g., T1/T2 riser). Periods of “main-phase” terrace
occupation by the Clarence River floodplain, during which highly energetic streamflow
transported coarse-grained sediments and laterally trimmed the adjacent risers, are indicated by
pebble–boulder gravels and sand deposits (Lensen, 1964; Bull and Knuepfer, 1987; Mason et al.,
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2006b; Cowgill, 2007; Zinke et al., 2017). The gravels and sands are typically capped by younger
silt horizons, which we interpret to represent: (1) the waning stages of terrace occupation; (2)
overbank deposits from younger floodplains; and/or (3) loess. In any of these cases, the silt
deposits represent periods of low-energy streamflow (or no flow at all) that was incapable of
significantly modifying terrace risers or channel geometries. Remnant channel morphologies are
discernable within several of the terraces. Notably, “Channel Y” cross-cuts both the T3 and T4
terraces (Fig. 1), as discussed below.

6.4 Offset Measurements
Our geomorphic mapping of the Tophouse Road offsets builds on air photo- and field-
based interpretations by Kieckhefer (1979) and later Knuepfer (1992). Here, we use lidar
microtopographic data (Fig. 1, 2; Fig. S1, S2 in the supplementary material; Dolan and Rhodes,
2016) and field observations to precisely document four restorable and dateable offset geomorphic
features: (1) The T1/T2 terrace riser; (2) the T2/T3 riser; (3) the T3/T4 riser; and (4) Channel Y.
To measure the offset of each feature, we backslipped one side of the feature relative to the other
and visually determined a preferred (most likely) displacement value. Our maximum and minimum
estimates of each offset are based on the limits of sedimentologically allowable geometries.
Because our visual estimates of offset account for uncertainty in projecting each feature into the
fault, they capture a more complete range of plausible restorations than common software-based
offset restoration packages, which require a fault trace orientation and piercing line projection
(e.g., Zielke et al., 2012, 2015). In our slip rate calculations, we expressed our offset restoration
values as triangular or trapezoidal-shaped probability density functions (PDFs).
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The T1/T2 riser is relatively linear across the primary fault trace. The topographic
sharpness of this feature and its linear morphology allow for precise right-lateral offset
determination of 42.0 +2.0/-1.5 m (Fig. 2a, S3a). Additionally, a secondary fault strand cuts the
T1/T2 (and T2/T3) riser ~70 m S of the main fault (Fig. S3e). The secondary strand accommodates
5.0 +1.0/-1.5 m of fault slip, yielding a total of 47.0 ± 3.0 m of right-lateral slip.
The T2/T3 riser is defined by a gentle, continuous curve within a ~40 m width on either
side of the primary fault, where a backedge channel at the base of the riser formed a curvilinear
edge. Our reconstructions restore the broad, smooth curvature of the riser, resulting in a geometry
similar to those of other channels within the active Clarence River floodplain to the east. This
yielded a right-lateral offset of 20.5 ± 1.5 m (Fig. 2b, S3b). The secondary strand to the S
accommodates 1.0 ± 0.5 m of fault slip. Adding this additional offset yields a total right-lateral
offset of 21.5 ± 2.0 m.
The T3/T4 riser represents the next potentially younger and less-offset feature. Uncertainty
in projecting the riser into the fault stems from whether or not the riser is coherent and discernable
across a ~15 m-wide pop-up structure immediately north of the fault (Fig. 2c, S3c). If the riser is
continuous and uninterrupted (i.e., not horizontally displaced) across this feature, our preferred
restoration is 21.5 ± 0.5 m (the same as the T2/T3 offset). Alternatively, the pop-up might disrupt
the near-fault geometry of the riser (e.g., by accommodating antithetic shear and clockwise
rotation). If so, our preferred restoration is 17.5 ± 0.5 m. Because we consider these interpretations
equally valid, we describe the riser offset using a trapezoid-shaped probability distribution, with a
uniform “boxcar” from 17.5–21.5 m and probabilities diminishing to zero at the extreme ends
(17.0 and 22.0 m).
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After main-phase flow across the T4 floodplain ceased, Channel Y formed as an isolated,
minor offshoot of the Clarence River, cross-cutting T3 and T4 (Fig. 2d, S3d). The northern and
southern reaches of Channel Y restore at 9.0 ± 1.0 m right-lateral offset.

6.5 Age Determinations
We dated the landforms at Tophouse Road using a sedimentologically informed protocol
for infrared stimulated luminescence (IRSL) dating (details in Text S4). We excavated seven pits
into the Tophouse Road terraces and channels, in which we logged the stratigraphy and collected
samples for dating (Fig. 1c, 3a). In all, we collected and dated 18 samples using the post-IR-
IRSL225 single K-feldspar grain procedure of Rhodes (2015), and Lewis et al. (2017; see also
Huntley and Baril, 1997). The ages are reported in thousands of years before 2018 (kyb2018). We
used a two-step Bayesian age model (based on OxCal 4.3; Bronk Ramsey, 2001, 2017; Rhodes et
al., 2003; Zinke et al., 2017; Text S4) to refine the terrace and channel ages based on stratigraphic
observations. The first step of our age model trimmed the gravel and silt ages in each pit according
to their lithostratigraphic ordering. For example, knowledge that the silts in Pit 17-01 are
stratigraphically higher and younger than the gravels allowed us to trim the gravel sample age.
Step two of our age model accounts for the morphologic information that lower terraces are
sequentially younger than higher ones, and the cross-cutting Channel Y is younger than the treads
into which it is incised. In this step, we included only the gravel and sand ages representing high-
energy, main-phase terrace occupation by the river floodplain or channelized streamflow (Fig. 3b).
We combined (averaged) samples CR12-01 and CR12-02 in Pit 12-12 based on their similar ages
and stratigraphic context. We refer to post-step two ages as “modeled ages”.

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6.6 Slip Rate Determinations
To reconstruct the incremental slip history of the Clarence fault at Tophouse Road, we
dated each offset geomorphic feature by considering its relation to the dated stratigraphic units at
the site (e.g., Zinke et al., 2017). We make no assumptions about whether the upper or lower terrace
age is more appropriate to date an offset riser without consideration of morphological and
sedimentological context (Lensen, 1964; Cowgill, 2007). The offsets, ages, and slip rates
determined in this study are tabulated in Table S6.
The T1/T2 morphology is sharply defined and similar on either side of the fault, showing
no evidence of a diachronous origin (see Gold et al., 2009), as would have resulted from inefficient
lateral trimming by the Clarence River. This morphology instead implies that any fault offset was
effectively removed by the river until abandonment of the lower T2 floodplain, as might be
expected considering the that high-energy streamflow across T2 was capable of transporting the
cobble–boulder bedload of the T2 floodplain. We therefore use the modeled T2 abandonment age
of 11.2 ± 1.3 kyb2018 (2-sigma confidence, based on sample CR17-103) to date the T1/T2 riser
offset (47.0 ± 3.0 m).
The T2/T3 riser is bounded by a backedge channel within the T3 surface. In its restored
configuration (Fig. 2b), the riser was trimmed by channelized flow along the back edge of the T3
floodplain during high-energy, main-phase T3 occupation. Thus, the coarse-grained sand deposits
dating T3 abandonment also date the end of T2/T3 trimming. The T3 backedge channel may have
sustained minor streamflow after this time, but any such flow did not significantly erode the T2/T3
riser north of the fault, based on the smooth, continuous curvature of the restored riser. The T2/T3
riser therefore records 21.5 ± 2.0 m of offset since 9.0 +1.0/-0.9 kyb2018 (based on samples CR12-
01 and CR12-02).
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The T3/T4 riser offset (17.5–21.5 ± 0.5 m) is dated by the youngest bedload gravel deposits
of T4, which mark the main-phase abandonment of the T4 floodplain and the end of high-energy
streamflow capable of modifying the riser. Sample TR17-03 dates these gravel deposits, yielding
a modeled age of 8.1 +0.8/-0.7 kyb2018.
Channel Y cross-cuts the T3 and T4 terraces, and therefore postdates main-phase river
occupation of those surfaces. We excavated two pits into the Channel Y deposits, revealing silt
overlying sands and gravels at ~50–75 cm below the present-day ground surface (Fig. 1, 3). The
youngest gravel sample from these pits represents the most recent age at which streamflow was
sufficiently high-energy to erode and modify the channel geometry. We therefore use the 4.5 +0.8/-
0.7 kyb2018 age of gravel sample CR14-01 to date the 9.0 ± 1.0 m of slip recorded by Channel Y.
These four dated, offset geomorphic markers constrain the incremental displacement-time
history of the Clarence fault at Tophouse Road (Fig. 4a). We first calculated the slip rate recorded
by each individual feature, averaged since the time of marker formation to the present day (Fig.
4b) by convolving the offset and age PDFs using the formulation of Bird (2007). These slip rates
(and associated 68.27% uncertainties) are: 4.2 +0.2/-0.3 mm/yr (T1/T2 riser); 2.4 +0.1/-0.2 mm/yr
(T2/T3 riser); 2.4 ± 0.2 mm/yr (T3/T4 riser); and 2.0 ± 0.2 mm/yr (Channel Y).
We also determined the incremental slip rates between different offset markers to show
how slip rate varied over different time periods (Fig. 4c, d). We achieved this using a Markov
chain Monte Carlo sampling approach, in which physically realistic estimates of incremental slip
rates were obtained by rejecting sample paths that resulted in negative (left-lateral) slip rates (Gold
and Cowgill, 2011; Zinke et al., 2017; Text S5). The displacement-time markers at Tophouse Road
characterize slip rates over four distinct intervals. First, from c. 9.0–11.2 ka the Clarence fault
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slipped at an average rate of 9.6 +5.0/-2.5 mm/yr (Fig. 4d), sustained over ~25.5 m of slip. Second,
for a relatively brief period between c. 8.1–9.0 ka, the fault may have experienced ~3–4 m of slip,
or may not have experienced any surface-rupturing earthquakes. Specifically, if our ~21.5 m offset
estimate for the T3/T4 riser is correct, then the Clarence fault at Tophouse Road experienced a
seismic lull between the age of the T3/T4 riser (c. 8.1 ka) and the age of the T2/T3 riser (c. 9.0 ka)
which is offset by the same amount. Alternatively, if our estimate of ~17.5 m is correct, then the
Clarence fault experienced ~4 m of surface slip – possibly one large event – over this time period.
Making no assumptions about which case is correct (i.e., allowing the slip rate to range between
zero and infinity over this short interval) yields an incremental slip rate of 1.3 +2.2/-1.2 mm/yr.
More recently, during the third interval spanning c. 4.5–8.1 ka, the Clarence fault slipped at a
relatively slow rate of 2.9 ± 0.5 mm/yr. Finally, the fault slip rate has been similarly slow since c.
4.5 ka to the present, averaging 2.0 ± 0.2 mm/yr.

6.7 Discussion and Conclusions
The incremental slip history of the Clarence fault at Tophouse Road is marked by
significant (factor of 4–5) variations in incremental slip rate, sustained over periods spanning
thousands of years (~1–3 ky) and meters or 10’s of meters of slip (~4–25 m) during Holocene and
latest Pleistocene time. As detailed above, the incremental slip history of the Clarence fault at
Tophouse Road can be characterized as relatively fast during latest Pleistocene and early Holocene
time (c. 9.0–11.2 ka), followed by a transitional period, and finally a period of relatively slow
strain release since mid-Holocene time (c. 8.1 ka).
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The Tophouse Road site presents an ideal opportunity to study incremental slip rate
variability in which all offset markers come from a single, ~300 m-long stretch of the fault, and
can confidently be assumed to share a common tectonic history without the need to combine
features from different sites (e.g., Gold and Cowgill, 2011). Although we note the presence of a
small sag basin at the eastern edge of the site, it is unlikely that deformation associated with this
feature significantly influenced our slip rate measurements, as the features upon which we based
our offset measurements do not appear to bend into the fault (Fig. 1). We also note that the site is
on the western edge of a transfer zone between Clarence and Elliott faults (Fig. 1b). However,
because the mapped surface trace of the Elliott fault ends ~2 km to the southeast (Langridge et al.,
2016), the proportion of strain localized on the main fault should be similar across the site.
Slip rate data such as these provide crucial constraints on the likely tectonic processes
controlling plate boundary deformation, as they result from interactions between the physical
properties of the fault zone and lithospheric stresses. For instance, alternating cycles of strain
hardening and weakening of fault zone rocks could lead to seismic lulls and clusters of earthquake
activity (Chéry and Vernant, 2006; Dolan et al., 2007, 2016; Oskin et al., 2008;). These effects
could be especially pronounced if crustal rocks act as an “elastic strain capacitor” (Fay and
Humphreys, 2006; Dolan et al., 2016), storing more elastic strain than is released in a single event,
then releasing that stored energy in an earthquake cluster (Weldon et al., 2004; Sieh et al., 2008;
Dolan et al., 2016).
Additionally, spatial and temporal variability in lithospheric stresses can produce variable
fault slip rates. At the broadest spatial scales, plate boundary faults are driven by relative tectonic
plate motion. Although these motions are generally assumed to be constant over million-year
timescales (e.g., DeMets et al., 2010), shorter-term variations (due to, e.g., subduction
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earthquakes) may be masked within the temporal resolution of the record (Anderson, 1975; Meade
and Loveless, 2017). In contrast, at the most local, short-term spatiotemporal scales, inter-event
coordination between heterogeneously stressed fault patches may lead to phases of increased
earthquake activity, including anomalously large displacements in some events (e.g., Cisternas et
al., 2005; Konca et al., 2008; Barbot et al., 2012). However, these effects are likely most important
over time and displacement scales spanning one to several earthquakes and cannot explain the
longer-term patterns of slip rate variability observed at Tophouse Road.
Between these end-member time and length scales may operate a host of different
processes controlling the distribution and evolution of stresses on a fault, and rates of strain
accumulation and release. These include climate-driven processes such as changes in glacial
loading (Hetzel and Hampel, 2005) and eustatic sea level changes (Luttrell and Sandwell, 2010),
as well as interactions between faults in mechanically complementary fault networks (Sammis et
al., 2003; Scholz, 2010; Loveless and Meade, 2011). Considering the location of the Clarence fault
within the mechanically complementary fault network of the MFS (Fig. 1), one might expect
earthquake activity along nearby faults to influence Clarence fault behavior. Interestingly, the
extreme slip rate variability observed at Tophouse Road is similar in magnitude and duration to
that on the Awatere fault at Saxton River (Zinke et al., 2017). The periods of relatively “fast” and
“slow” slip on these faults, however, appear to be anticorrelated in time: While the Clarence fault
at Tophouse Road was experiencing a rapid slip rate between 9–11 ka, the Awatere fault at Saxton
River was moving relatively slowly, and when the Clarence fault at Tophouse Road slowed down
following c. 8 ka, the Awatere fault at Saxton River sped up. Although reliable incremental slip
rate data along the nearby Wairau and Hope faults are forthcoming (Hatem et al., 2016), these
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observations hint at potentially coordinated system-level behavior in which the major strike-slip
faults of the MFS trade slip in time and space.
Regardless of the cause, the striking differences in incremental slip rate during the “fast”
and “slow” periods at Tophouse Road illustrate the need for caution when using only a single slip
rate marker for geologic and geodetic fault studies, and seismic hazard assessment. For instance,
geologically determined fault slip rates and geodetically inferred slip deficit rates (sometimes
referred to as “geodetic slip rates”) are often compared to assess the role of faults in
accommodating strain across a region. Geologic slip rates based on a single marker formed during
a “slow” period (such as that between present day and c. 8 ka at Tophouse Road) may be
significantly slower than the geodetic slip deficit rate (e.g., Dolan et al., 2016; Dolan and Meade,
2017). Without further evidence, this may lead one to conclude that either: (1) the fault of interest
plays a relatively minor role in accommodating the total strain across the region (e.g., England and
Molnar, 2005); or (2) a significant amount of strain across the fault is accommodated as distributed,
off-fault deformation not accounted in the offset restoration (e.g., Hergert and Heidbach, 2010;
Dolan and Haravitch, 2014; Zinke et al., 2015; Milliner et al., 2016). Conversely, if the slip rate
marker formed during a “fast” period (such as that between c. 9–11 ka at Tophouse Road), the
geologic rate may overestimate the geodetic slip deficit rate. This may lead one to infer extreme
earthquake-cycle effects (e.g., Dolan and Meade, 2017) or variations in the strength of lithospheric
rocks (e.g., Dolan et al., 2007, 2016; Dolan and Meade, 2017). Similarly, the unknowing use of a
slip rate representing a “slow” period in a seismic hazard assessment may result in an
underestimate of the hazard posed by a fault, whereas use of a slip rate from a “fast” period may
overestimate the hazard. More incremental slip rate data like those presented above are required
to properly quantify the prevalence and magnitude of slip rate variability on faults worldwide.
135

These observations raise the question, over what time and displacement scales do
incremental slip rates reflect their “average”, long-term rate? Put another way, is there a
characteristic amount of slip or time over which the variations in slip rate average out? The
incremental slip rate data we present here give no indication that the Clarence fault has reached a
consistent average rate over the total time (~11 ky) and displacement (~47 m) recorded at
Tophouse Road. Instead, even larger-displacement and longer-term data could be necessary, and
these values may depend on the specific properties of the fault in question (e.g., tectonic setting,
slip rate, structural maturity). Further development of high-resolution incremental slip rate records
such as the one presented here may shed light on these questions.

6.8 Figure Captions
Figure 1. (a) The MFS transfers strain between the Hikurangi subduction zone (Hik) and Alpine
fault (Alp F). (b) Major faults of the MFS. The Tophouse Road site (TR) is at the western edge of
the Clarence-Elliott fault junction (Langridge et al., 2016). SR is the Saxton River site on the
Awatere fault. Topography shown by ASTER GDEM. (c) Geomorphic surfaces mapped on
hillshaded lidar. “Ch Y” is Channel Y. Thick red line is the primary fault trace; thin red lines are
secondary faults. Green dots are sample pits, labelled by pit number.

Figure 2. Offset markers restored to their preferred values. White arrows indicate the primary
feature restored in each panel. (a) T1/T2 riser. A secondary fault strand to the S accounts for an
additional ~5 m of slip (small white arrows; S3b). (b) T2/T3 riser. The southern secondary fault
strand accommodates an additional ~1 m of slip (S3b). (c) T3/T4 riser, restored to the middle of
136

our preferred range. Curved red line is the N boundary of a pop-up structure that may disrupt near-
fault riser geometry. (d) Channel Y (Ch Y).

Figure 3. (a) Morphostratigraphic diagram of sample pits and ages relative to elevation (m) and
corresponding geomorphic surfaces. Because pits were excavated at slightly different distances
from the fault on gently south-sloping terrace surfaces (Fig. 1), apparent relative vertical positions
do not necessarily reflect terrace heights at the fault. (b) Modeled gravel and sand samples ages
from step 2 of our age model.

Figure 4. Displacement-age history of the Clarence fault at Tophouse Road. (a) Offset and age
determinations for restored geomorphic features (preferred values and 95% confidence limits). (b)
Relative probability of slip rates averaged since present day for each marker. Blue fields show
highest 68.27% probability. (c) Monte Carlo sampling of offset and age distributions (blue boxes
as in (a)). Labels are incremental slip rates. (d) Incremental slip rate PDFs (blue fields as in (b)).
The T2/T3 to T3/T4 riser interval appears truncated at zero because there is a significant
probability that no slip occurred during this time.

6.8.1 Captions for Supplementary Materials
Figure S1. Uninterpreted hillshaded lidar (based on the devegetated digital terrain model; Dolan
and Rhodes, 2016) of the Tophouse Road site, Clarence fault, NZ.

Figure S2. Uninterpreted 0.25 m contour map (based on the devegetated digital terrain model;
Dolan and Rhodes, 2016) of the Tophouse Road site, Clarence fault, NZ.
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Figure S3. Offset restorations. These figures show detailed restorations for the minimum,
maximum, and preferred value of each offset.

S3a – the T1/T2 riser
The T1/T2 riser is linear across the primary (northern) fault. The primary fault trace is also
linear and well-defined, and the width of the fault surface expression (geomorphically discernable
fault zone) is narrow. We restored the T1/T2 riser across the main fault trace at 42.0 +2.0/-1.5.
Approximately 70 m south of the primary fault, we observed 5.0 +1.0/-1.5 m of fault
displacement across a secondary fault strand. Offset across that strand is added to our T1/T2 riser
offset restoration.

S3b – the T2/T3 riser
The T2/T3 riser is defined by a broad curve on either side of the primary (northern) fault.
It is bounded at the base by a backedge channel within the T3 terrace. Our reconstructions (20.5 ±
1.5 m) restore the curvature of the riser, projecting across the fault zone.
Approximately 100 m to the south, we observed 1.0 ± 0.5 m of fault displacement across a
secondary fault strand. Offset across the secondary fault is added to our T2/T3 riser offset
restoration.

S3c – the T3/T4 riser
The T3/T4 riser is topographically more subtle than the T1/T2 or T2/T3 risers (it is 60–70
cm high). North of the fault, the riser is defined by the western margin of a backedge channel
138

associated with T4. To the south, the channel broadens into the monoclinal riser observed S of the
fault. Where the riser intersects the primary fault trace, the fault zone is relatively wide (15–20 m,
measured perpendicular to the fault).
Uncertainty in restoring the T3/T4 riser stems primarily from whether the riser is
discernable across a ~15×20 m pop-up structure, noted in the figures below. As shown in the
hillshaded lidar DEM, the riser appears to be a coherent feature across the fault, leading to a
preferred restoration value of ~21.5 m. Alternatively, the pop-up might accommodate subtle, left-
lateral (antithetic) shear along its northern boundary. This would artificially enhance the apparent
offset. If the riser geometry is disrupted in this way, then one must project the riser into the fault
zone from ~15 m further away. Doing so yields a preferred offset of ~17.5 m.

S3d – Channel Y
Channel Y cross-cuts the T3 and T4 terraces. Back-slipping Channel Y across the fault is
straightforward, resulting in an offset of 9.0 ± 1.0 m.

S3e – Map of primary and subsidiary faulting at Tophouse Road.

Text S4. IRSL sample collection, preparation, processing, and analysis.

Text S5. Detailed description of Markov chain Monte Carlo analysis process used in this study.

Table S6. Offsets, ages, and slip rates recorded by features preserved at the Tophouse Road site.

139

CHAPTER 7:
Conclusions
The observations, interpretations, and discussions presented above provide important new
insights into fault behavior and earthquake expression in single earthquakes and on timescales of
hundreds to thousands of years. In Chapters 2 and 3, I used optical image correlations of pre- and
post-event imagery to determine high-resolution ground surface deformation maps for the 2013
MW 7.7 Balochistan and 2016 MW 7.8 Kaikōura earthquakes. I determined the amounts of on-
versus off-fault deformation for the Hoshab fault (Balochistan) and Kekerengu fault (Kaikōura)
by comparing correlation-derived fault displacement measurements encompassing both on- and
off-fault deformation, with measurements of surface slip localized on a primary fault trace. In the
context of similar studies (e.g., Milliner et al., 2015, 2016b), these results demonstrate that OFD
is not only ubiquitous among large-magnitude (MW > 7.0) surface ruptures, but can account for a
significant portion of the total coseismic surface deformation. Whereas fault structural maturity
(cumulative displacement) exerts a primary control on the amount of OFD at the surface, numerous
secondary controls exist. For the 2013 Balochistan earthquake, which resulted in ~45% OFD
overall, the type and thickness of near-surface geologic materials strongly influenced the
proportion of OFD, with thick deposits of alluvium tending to produce more OFD. For the 2016
Kaikōura earthquake, which resulted in ~36% OFD on the Kekerengu fault, the percent OFD at
any measurement location appeared to correlate with local topographic slope, with higher slopes
corresponding to more OFD.
Reconstructions of incremental fault slip rate histories at the well-known Saxton River site
on the Awatere fault, and Tophouse road site on the Clarence fault, definitively showed that fault
slip rates averaged over thousands of years (several earthquake cycles) can be highly non-constant.
140

Instead, incremental slip rates varied by a factor of 10–11 on the Awatere fault at Saxton River,
and factor of 4–5 on the Clarence fault at Tophouse Road. These variations could be due to myriad
factors, including changes in strength of the mid- to lower-crust and upper mantle, or changes in
the rates of tectonic plate motions themselves.
With further study and detailed documentation of the phenomena discussed here, these
conclusions might serve as part of a larger body of knowledge used to inform next-generation
seismic hazard mitigation strategies. Studies of the relative amount, distribution, and controls on
OFD can lead to the potential development of seismic hazard microzonation maps for the built
environment. As more incremental slip rate data like those presented above become available to
properly quantify the prevalence and magnitude of slip rate variability on faults worldwide, the
potential for such variability can be incorporated into probabilistic models of seismic hazard.

141

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160

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Geophysical Research Letters, v. 44, doi: 10.1002/2017GL075048.

161

Chapter 2 Figures
Chapter 2, Figure 1.

162

Chapter 2, Figure 2.

163

Chapter 2, Figure 3.

164

Chapter 2, Figure 4.

165

Chapter 2, Figure 5.

166

Chapter 2, Figure 6.

167

Chapter 2, Figure 7.

168

Chapter 2, Figure 8.

169

Chapter 2, Figure 9.

170

Table 1.
Mean Fault Slip and Surface Slip Ratios

Minimum Preferred Maximum
Mean slip using geologic measurements (m) 3.23 4.18 5.22
Mean slip using COSI-Corr measurements (m) 6.33 6.69 7.04
Surface slip ratio (SSR), after Dolan and
Haravitch (2014)
0.41 0.56 0.73
Surface slip to total slip along structural simple
fault segments

0.74

Surface slip to total slip along structurally
complex fault segments

0.44

Local surface slip ratio (LSSR) for structurally
simple fault segments

0.81

Local surface slip ratio (LSSR) for structurally
complex fault segments

0.40

171

Chapter 3 Figures
Chapter 3, Figure 1.

172

Chapter 3, Figure 2.

173

Chapter 3, Figure 3.

174

Chapter 3, Figure 4.

175

Chapter 3, Figure 5.

176

Chapter 4 Figures
Chapter 4, Figure 1.

177

Chapter 4, Figure 2.

178

Chapter 4, Figure 3.

179

Chapter 4, Figure 4.

180

Chapter 5 Figures
Chapter 5, Figure 1.

181

Chapter 5, Figure 2.

182

Chapter 5, Figure 3.

183

Chapter 5, Figure 5.

184

Chapter 6 Figures
Chapter 6, Figure 1.

185

Chapter 6, Figure 2.

186

Chapter 6, Figure 3.

187

Chapter 6, Figure 4.

188

Appendices
Appendix A:
Surface slip and off-fault deformation patterns in the 2013 MW 7.7 Balochistan, Pakistan
earthquake: Implications for controls on the distribution of near-surface coseismic slip

189

Table S1.
Images used for visual analysis
Platf
orm
WorldView image ID
a

Acquis
ition
date
Incid
ence
angle
Lat-
itude
Long-
itude
Reference image
b

Final
resolu
tion
(m)
WV1
WV01N26_591785E065_10739020
13100100000000PN00
1-Oct-
13
14.7
26.591
45896
65.106
80495
LC815404220132
85LGN00B8
0.528
WV1
WV01N26_502044E065_10714820
13100100000000PN01
1-Oct-
13
14.5
26.502
29076
65.107
36016
LC815404220132
85LGN00B8
0.527
WV1
WV01N26_646693E065_27765520
13100100000000PN00
1-Oct-
13
22.1
26.647
4993
65.278
54192
LC815404220132
85LGN00B8
0.569
WV1
WV01N26_732610E065_27726920
13100100000000PN01
1-Oct-
13
23.3
26.733
33334
65.277
98547
LC815404220132
85LGN00B8
0.577
WV1
WV01N26_965358E065_51037020
13100100000000PN02
1-Oct-
13
12.6
26.965
27687
65.510
83432
LC815404120132
85LGN00B8
0.52
WV1
WV01N27_042777E065_51088220
13100100000000PN00
1-Oct-
13
12.4
27.042
84684
65.511
11226
LC815404120132
85LGN00B8
0.519
WV1
WV01N27_076559E065_56755420
13092700000000PN00
27-
Sep-
13
5.6
27.076
66667
65.567
5004
LC815404120132
85LGN00B8
0.503
WV1
WV01N27_165266E065_51118520
13100100000000PN01
1-Oct-
13
12.2
27.165
41755
65.511
04165
LC815404120132
85LGN00B8
0.518
WV1
WV01N27_207909E065_67861120
13100100000000PN01
1-Oct-
13
17.1
27.209
02799
65.680
13836
LC815404120132
85LGN00B8
0.539
WV1
WV01N27_292379E065_67976320
13100100000000PN01
1-Oct-
13
17.9
27.292
98602
65.680
4171
LC815404120132
85LGN00B8
0.543
WV2
WV02N26_060219E064_15369020
13100700000000PN01
7-Oct-
13
29.4
26.061
04274
64.154
02715
LC815504220132
92LGN00B8
0.594
WV2
WV02N26_155929E064_31954420
13110900000000PN00
9-Nov-
13
24.5
26.155
97131
64.319
58254
LC815404220132
85LGN00B8
0.55
WV2
WV02N26_173637E064_48904920
13110900000000PN01
9-Nov-
13
28.6
26.174
7212
64.490
0685
LC815404220132
85LGN00B8
0.59
WV2
WV02N26_184108E064_58461320
13101000000000PN01
10-
Oct-13
20.9
26.184
37456
64.584
86134
LC815404220132
85LGN00B8
0.522
WV2
WV02N26_283102E064_74981820
13101000000000PN00
10-
Oct-13
18.8
26.283
75077
64.750
41624
LC815404220132
85LGN00B8
0.511
WV2
WV02N26_295457E064_58337320
13101000000000PN00
10-
Oct-13
20.9
26.295
62438
64.583
54264
LC815404220132
85LGN00B8
0.522
WV2
WV02N26_393639E064_74936520
13101000000000PN00
10-
Oct-13
19.2
26.394
23552
64.749
30434
LC815404220132
85LGN00B8
0.541
WV2
WV02N26_405985E064_91335020
13101000000000PN00
10-
Oct-13
18.2
26.406
04281
64.913
47226
LC815404220132
85LGN00B8
0.506
190

WV2
WV02N26_511860E064_91318720
13101000000000PN00
10-
Oct-13
18.1
26.511
94352
64.912
84672
LC815404220132
85LGN00B9
0.505
WV1
WV01_14JAN240615215-P1BS-
1020010029868B00
24-
Jan-14
24.96 26.883 65.401
LC815404220132
85LGN00_B8
0.536
WV1
WV01_14JAN240615226-P1BS-
1020010029868B00
24-
Jan-14
24.96 26.751 65.398
LC815404120132
85LGN00_B8
0.526
a
Image identifiers correspond to the image names given on the USGS EarthExplorer website or the
DigitalGlobe website
b
Image identification code for Landsat-8 imagery given on the
USGS EarthExplorer website

191

Table S2.
Displacement measurements from COSI-Corr analysis of Landsat-8 imagery
UTM easting
a

UTM
northing
a

Fault-parallel
offset (m)
b

Fault-parallel 1-
σ
standard
deviation (m)
Fault-
perpendicular
offset (m)
c

Fault-
perpendicular
1-σ
standard
deviation (m)
610920 2885160 -0.007382 0.109955 0.012735 0.177171
615720 2886840 -0.816071 0.195436 0.013913 0.232702
620520 2888520 -2.107589 0.168727 0.010024 0.157632
625320 2890200 -4.961906 0.222355 -0.011642 0.607202
630120 2891880 -6.660874 0.246868 -0.00953 0.286542
634920 2893560 -7.982923 0.262485 -0.010207 0.427032
639720 2895480 -7.832119 0.172357 0.014283 0.412362
644520 2897400 -7.867393 0.341354 -1.237864 0.235014
649320 2900040 -8.475148 0.384392 -1.097364 0.320688
654120 2902680 -7.968896 0.362444 -0.722541 0.353237
658920 2905560 -8.062494 0.415377 -1.00416 0.244607
663720 2908200 -7.955677 0.333771 -1.054056 0.446553
668520 2910600 -7.652456 0.300065 -1.682398 0.194718
673320 2913240 -8.245299 0.405144 -2.226035 0.221481
678120 2916360 -8.897124 0.474426 -2.77077 0.298466
682920 2919480 -9.116838 0.400002 -3.145753 0.324308
687720 2922840 -9.082077 0.334158 -2.11902 0.211464
692520 2925960 -8.893019 0.295095 -2.057332 0.36013
697320 2929080 -9.340609 0.516109 -0.943051 0.218064
702120 2932920 -10.148104 0.403701 -1.716011 0.274221
706920 2936760 -10.260591 0.423568 -3.983963 0.346797
711720 2940600 -10.512568 0.302823 -1.706963 0.277403
716280 2945400 -10.382464 0.339683 -1.856427 0.35154
720840 2950200 -10.073428 0.432902 -1.486586 0.225199
725400 2955000 -10.227895 0.617806 -0.771164 0.456224
729240 2959800 -9.362946 0.686159 0.020116 0.352224
731880 2964600 -9.736654 0.571951 -0.854114 0.336292
734520 2969400 -9.573082 0.94423 -0.723365 0.52464
736680 2974200 -9.510165 0.670006 -0.753277 0.23747
738360 2979000 -7.551348 0.51471 0.006643 0.405864
74028 2983800 -5.504624 0.138038 -0.334494 0.179593
742200 2988600 -3.709428 0.162871 0.009543 0.148922
743880 2993400 -3.778764 0.195392 -0.345441 0.123718
745800 2998200 -2.924096 0.190527 -0.014335 0.262609
748440 3003000 -2.773388 0.252618 -0.979661 0.200347
192

751560 3007800 -2.563148 0.153291 -1.234103 0.287298
754680 3012600 -2.905395 0.209272 -0.803534 0.232817
757800 3017400 -2.49247 0.435325 0.017829 0.220279
760440 3022200 -1.849934 0.249625 -0.010489 0.231565
763320 3027000 -0.76058 0.240263 0.029162 0.296531
765720 3031800 -0.219355 0.221525 -0.007289 0.082466
767880 3036600 0.007138 0.147903 0.022913 0.259462
a
Easting and northing values are given for UTM Zone 41 N, using the WGS84 datum.

b
Negative values indicate left-lateral offset

c
Negative values represent compression

193

Figure S3.

194

Table S4.
Visual measurements of offset.

UTM
E
a

UTM
N
a

Min.
offset
(m)
Pref.
offset
(m)
Max.
offset
(m)
Dist.
(m)
b

Surface
material
Quality
Bedrck
dist.
(m)
c

Struct.
complex
?
d

619593
28882
76
0.00 0.00 0.00 12281 bedrock B 0 no
619759
28885
33
0.84 1.33 1.33 12566
active
wash
B 0 no
619961
28887
19
1.33 2.97 4.64 12839 bedrock B 0 no
620335
28890
10
0.84 1.68 2.97 13315 bedrock B 0 no
620377
28890
40
1.68 2.97 4.20 13374 bedrock C 0 no
620681
28892
19
0.00 0.00 0.00 13715
active
wash
B 0 no
620798
28892
72
2.14 2.52 3.80 13843
active
wash
B 30 no
620933
28893
36
1.33 2.97 3.80 13990 bedrock B 0 no
621068
28894
05
0.00 1.33 2.14 14140 bedrock B 0 no
621323
28894
75
2.38 3.61 4.79 14403 bedrock B 0 no
621528
28894
92
2.38 3.56 4.32 14604 bedrock B 0 no
621740
28895
11
1.19 1.33 1.88 14812 bedrock B 0 no
622385
28897
49
1.88 2.66 3.03 15500 bedrock C 0 no
622492
28897
74
1.19 1.78 3.03 15608
young
fan
B 0 no
622696
28898
23
3.69 4.19 4.92 15818 bedrock B 0 no
622782
28898
47
2.27 2.96 4.19 15907 older fan A 0 no
622840
28898
64
1.98 2.96 3.48 15968 bedrock B 0 no
623236
28899
89
1.23 1.74 2.80 16384
young
fan
A 20 no
623549
28901
02
1.98 2.96 4.43 16716 bedrock B 0 no
623820
28902
07
3.97 5.19 5.66 17004 bedrock A 0 no
195

624038
28902
97
2.75 3.21 4.19 17240
active
wash
A 20 no
624399
28904
17
4.43 5.42 6.41 17620 bedrock B 0 no
624584
28904
78
3.21 3.69 4.73 17816 bedrock B 0 no
625060
28906
28
2.75 3.69 5.22 18314 bedrock B 0 no
625242
28906
85
6.60 7.17 7.78 18505 older fan B 10 no
625731
28907
84
2.75 3.69 4.30 19000 older fan B 0 no
626017
28908
41
4.19 4.92 6.44 19291 bedrock B 0 no
626045
28908
48
4.43 5.74 6.44 19320 bedrock B 0 no
626178
28908
76
4.70 5.74 6.96 19456 older fan B 0 no
626811
28910
06
5.22 5.74 6.96 20101 bedrock A 0 yes
627238
28910
94
1.56 1.56 1.98 20537 bedrock C 0 yes
627256
28910
99
2.46 3.69 4.19 20555 bedrock B 0 yes
627297
28911
06
5.92 6.44 7.15 20597 older fan B 5 yes
627442
28911
35
4.70 5.22 5.74 20746 bedrock B 0 yes
627890
28912
37
3.69 4.19 5.42 21205 bedrock A 0 yes
627981
28912
60
4.00 5.22 6.44 21297 bedrock A 0 yes
628568
28914
29
2.46 3.69 5.66 21908
young
fan
B 10 yes
629003
28915
76
2.46 4.00 5.74 22376 bedrock C 0 yes
629299
28916
75
6.15 6.89 7.66 22669 older fan B 10 yes
629635
28918
08
5.19 5.95 7.17 23048
active
wash
A 50 no
630092
28919
93
3.48 4.70 5.42 23542 bedrock B 0 no
630337
28921
00
4.73 5.66 6.65 23808 bedrock B 0 no
630376
28921
20
3.89 3.52 5.07 23853
active
wash
C 20 no
196

631284
28925
35
5.50 6.41 6.89 24851
young
fan
A 70 no
631589
28926
87
6.15 7.38 9.35 25190
active
wash
B 10 no
631892
28928
22
7.15 6.65 8.38 25522 older fan C 20 no
632512
28930
49
4.19 5.61 6.80 26182
active
wash
B 10 no
632556
28930
62
4.43 5.07 6.27 26228
active
wash
B 30 no
632744
28931
24
7.48 8.54 9.75 26425 bedrock A 0 no
633087
28932
15
7.34 7.87 8.54 26783
active
wash
A 5 no
633568
28933
62
6.96 8.70 10.62 27285 older fan B 10 no
633722
28934
12
6.80 7.48 8.01 27445
young
fan
C 10 no
636064
28943
80
0.00 0.00 0.78 29995 older fan C 30 yes
636717
28946
83
4.73 5.95 6.27 30706 bedrock B 0 yes
637334
28949
01
4.00 4.43 6.44 31361 bedrock B 0 yes
637372
28949
14
4.43 5.19 6.15 31401 bedrock B 0 yes
638160
28951
90
4.70 6.15 8.38 32236 bedrock B 0 yes
638459
28952
97
4.92 6.96 7.38 32554 bedrock B 0 yes
638575
28953
43
4.73 5.66 7.64 32674 bedrock C 0 yes
638608
28953
60
3.52 5.07 6.27 32705
active
wash
B 30 yes
639041
28954
94
7.30 7.87 9.89 33158 older fan C 10 yes
639368
28956
11
3.96 5.04 5.81 33503 older fan B 40 yes
639964
28958
33
4.17 5.34 6.52 34136
active
wash
C 10 yes
640510
28959
17
6.60 7.87 9.03 34686 bedrock A 0 no
640590
28959
31
4.30 5.60 6.16 34766 bedrock B 0 no
640761
28959
64
5.60 6.91 9.73 34939
active
wash
A 10 no
197

641210
28960
53
4.49 5.04 7.46 35397 bedrock A 0 no
641534
28961
54
5.04 6.16 6.91 35735
active
wash
A 10 no
642066
28964
20
3.01 6.91 7.67 36373
active
wash
C 5 no
642148
28964
65
7.13 8.45 9.23 36467 bedrock B 0 no
642465
28966
23
1.87 4.25 6.73 36820 older fan B 10 no
642561
28966
74
5.04 6.35 7.13 36929 bedrock B 0 no
642600
28966
93
7.92 8.71 10.03 36972 older fan B 20 no
642796
28967
98
0.83 1.32 1.77 37195
active
wash
B 40 no
643050
28969
17
2.13 4.17 4.61 37476 older fan B 20 no
644018
28973
73
1.32 2.13 2.50 38544 older fan B 50 yes
644451
28975
80
6.07 6.60 7.39 39025 bedrock A 0 yes
644602
28976
45
1.87 3.44 3.96 39188
active
wash
C 30 yes
644847
28977
78
0.83 1.32 2.64 39467 bedrock B 0 yes
644957
28978
34
0.00 0.00 0.59 39591
active
wash
A 30 yes
645001
28978
72
2.13 2.95 4.25 39648
active
wash
C 10 yes
645623
28981
55
0.00 1.67 2.13 40330
active
wash
C 50 yes
645993
28983
68
1.32 2.13 2.95 40758
active
wash
C 40 yes
646345
28985
42
1.18 1.87 3.18 41150
young
fan
B 30 yes
646398
28985
73
3.18 3.96 5.28 41212 older fan B 10 yes
647328
28990
88
1.18 2.95 3.78 42273 older fan C 40 yes
647356
28991
01
0.00 1.67 2.95 42305 older fan C 40 yes
647715
28992
39
2.95 3.44 5.08 42686
active
wash
C 50 yes
647847
28993
24
0.00 0.00 0.00 42844
active
wash
B 130 yes
198

648325
28995
75
1.67 2.95 4.25 43383
active
wash
C 120 yes
648333
28995
78
1.32 2.13 3.96 43452
active
wash
C 100 yes
648448
28996
09
0.00 0.83 1.32 43508
active
wash
B 80 yes
648975
28999
68
0.00 1.32 1.32 44142
active
wash
A 240 yes
649650
29000
89
0.00 0.00 0.00 44797
active
wash
B 200 yes
649927
29002
64
2.95 3.78 5.57 45137
active
wash
C 120 yes
650011
29003
41
0.00 0.00 0.00 45251
active
wash
B 120 yes
651418
29012
93
2.43 2.64 4.25 47043
active
wash
C 200 yes
651781
29015
07
2.13 3.44 3.96 47462
active
wash
A 110 yes
652784
29021
19
0.00 0.00 0.00 48635
active
wash
B 130 yes
653205
29025
00
0.00 2.13 2.95 49199 older fan B 410 yes
654339
29033
39
0.00 0.00 0.00 50518 older fan B 410 yes
654429
29033
77
3.44 4.76 6.38 50700 older fan B 400 yes
654665
29035
50
2.95 3.78 4.61 50993 older fan C 310 yes
656343
29045
67
2.61 3.34 4.49 52953
active
wash
C 270 yes
656525
29047
03
3.80 4.46 5.37 53181
active
wash
B 160 yes
657711
29055
46
3.04 3.76 5.22 54622 older fan B 160 yes
658092
29058
21
1.88 2.61 2.95 55086 older fan B 80 yes
658576
29059
72
1.88 2.61 3.04 55585
young
fan
B 60 yes
659434
29064
45
5.84 6.54 7.71 56531 bedrock C 0 yes
659953
29065
17
0.00 1.65 3.04 57052
young
fan
C 140 yes
660505
29065
58
0.00 0.00 0.00 57598
young
fan
C 120 yes
661336
29068
67
0.00 0.00 1.17 58479
young
fan
C 80 yes
199

661963
29069
46
2.21 2.61 2.95 59106
young
fan
C 100 yes
663328
29078
92
0.00 0.74 1.17 60798 older fan C 110 yes
663432
29080
39
0.00 0.00 0.00 60964 older fan B 140 yes
663592
29081
73
0.00 0.00 1.17 61173 older fan B 130 yes
664038
29084
79
0.00 0.00 0.00 61676
active
wash
C 40 yes
664444
29086
65
0.00 0.00 0.00 62155 older fan C 20 yes
664646
29086
88
0.00 1.88 4.43 62336
young
fan
C 30 yes
665042
29089
27
0.00 0.00 0.00 62799
active
wash
B 30 yes
666832
29095
39
0.00 0.00 1.48 64679 older fan B 120 yes
666950
29096
24
2.21 2.95 3.69 64821 older fan B 100 yes
669213
29110
56
7.32 8.46 9.60 67484 bedrock B 0 yes
670451
29118
24
1.14 2.98 3.43 68972
young
fan
B 70 yes
670496
29118
84
1.14 1.84 2.98 69047
young
fan
B 140 yes
671564
29131
59
0.00 2.89 3.61 70695
active
wash
B 10 yes
672062
29135
94
0.00 0.72 1.45 71338
active
wash
B 30 yes
672908
29139
89
0.00 0.00 0.00 72271
active
wash
B 50 yes
673634
29143
32
0.00 0.00 0.00 73074 older fan C 50 yes
673934
29145
79
0.00 3.27 4.71 73474
young
fan
C 20 yes
674521
29149
18
0.72 0.72 1.45 74151 bedrock B 0 yes
674787
29151
45
2.98 4.40 5.83 74454 bedrock B 0 yes
675588
29154
07
0.00 0.00 0.00 75428
active
wash
B 70 yes
675730
29155
01
2.11 3.72 5.03 75597 older fan C 140 yes
677116
29162
98
2.91 3.15 3.46 77175 bedrock B 0 yes
200

677611
29165
95
1.21 1.95 3.06 77754
active
wash
B 150 yes
677797
29167
19
2.42 3.46 3.83 77975
active
wash
A 100 yes
678178
29167
36
5.13 7.36 8.40 78307
active
wash
B 10 yes
678981
29173
66
4.23 6.12 7.28 79323
active
wash
B 20 yes
679054
29174
50
0.00 0.00 0.00 79430
active
wash
B 90 yes
679546
29176
78
0.00 1.21 2.30 79968 bedrock C 0 yes
680180
29180
39
0.00 0.00 0.00 80652
young
fan
B 40 yes
683681
29202
45
5.36 6.51 7.28 84835 bedrock B 0 yes
684415
29208
07
3.58 4.29 5.38 85769 bedrock C 0 yes
684513
29208
35
1.82 2.86 3.58 85864 bedrock B 0 yes
685131
29214
07
0.00 0.72 0.51 86699
active
wash
B 60 yes
685616
29216
36
2.53 3.65 4.35 87227
young
fan
C 5 yes
686334
29220
82
2.26 5.45 7.24 88081
active
wash
B 10 yes
686659
29222
24
1.13 2.26 4.08 88417 bedrock A 0 yes
687119
29226
42
3.24 3.95 4.67 89036
active
wash
C 40 yes
688233
29235
18
0.00 0.00 0.00 90452
young
fan
B 20 yes
688979
29240
79
3.58 5.01 6.44 91384
young
fan
C 50 yes
689260
29242
84
7.87 7.90 8.95 91732 bedrock B 0 yes
690122
29248
47
3.95 4.67 5.47 92761
young
fan
B 20 yes
691732
29259
69
0.00 0.00 0.00 94703
active
wash
A 70 yes
692051
29260
88
0.00 2.09 2.72 95041
young
fan
B 20 yes
692246
29261
83
0.00 0.00 0.00 95255
young
fan
C 10 yes
692440
29262
64
0.00 0.00 0.00 95517
young
fan
A 40 yes
201

692706
29264
41
0.00 0.00 1.43 95836
young
fan
B 70 yes
693565
29270
80
0.00 0.51 1.13 96907
active
wash
C 30 yes
694323
29275
50
7.72 8.85 10.53 97795 bedrock B 0 yes
694543
29277
00
7.59 8.30 9.72 98061
young
fan
B 10 yes
694669
29277
91
4.35 5.38 6.81 98216
young
fan
B 10 yes
699361
29302
47
6.75 7.91 10.97
10383
0
active
wash
C 130 yes
700923
29315
02
5.60 6.35 7.45
10583
1
older fan B 250 yes
701022
29315
92
4.86 7.09 8.97
10596
0
older fan A 170 yes
701863
29323
54
5.60 7.09 8.97
10708
3
bedrock B 0 yes
703633
29336
68
8.04 8.77 10.39
10929
7
bedrock B 0 yes
704092
29340
72
2.36 3.54 4.97
10990
9
young
fan
C 30 yes
704821
29347
22
0.00 0.00 0.00
11088
3
active
wash
B 50 yes
705555
29350
94
0.00 3.33 4.50
11168
7
young
fan
C 60 yes
706266
29358
59
1.49 2.36 2.64
11272
4
young
fan
B 40 yes
707472
29363
94
0.00 0.00 0.00
11398
9
young
fan
B 100 yes
708561
29373
83
0.00 0.75 0.75
11545
7
bedrock B 0 yes
708663
29374
51
2.64 3.08 4.72
11557
9
young
fan
B 70 yes
710108
29387
60
2.36 3.54 5.44
11752
8
active
wash
B 20 yes
711033
29397
27
7.61 8.25 9.82
11886
0
bedrock B 0 yes
711999
29407
26
5.23 5.61 7.10
12024
5
bedrock B 0 yes
712279
29410
05
2.64 4.12 5.23
12064
1
active
wash
B 30 no
713077
29417
67
7.92 9.40 10.88
12174
6
bedrock B 0 no
713567
29421
95
9.40 9.82 10.56
12239
6
older fan B 25 no
202

717858
29467
65
4.02 5.40 6.66
12876
5
older fan B 10 yes
718241
29472
47
2.35 3.06 3.82
12925
3
active
wash
B 40 yes
718218
29472
59
2.54 3.82 5.37
12937
5
active
wash
B 30 yes
718228
29472
54
1.27 2.35 3.06
12937
8
active
wash
B 30 yes
718466
29474
30
0.00 0.80 1.61
12966
7
bedrock B 0 yes
718555
29475
21
2.05 3.22 4.89
12979
5
older fan B 10 yes
719351
29483
88
3.64 5.69 7.29
13097
3
older fan A 10 yes
720169
29493
69
5.37 6.49 7.74
13224
6
bedrock B 0 no
721111
29505
01
8.46 10.87 12.07
13371
8
bedrock B 0 no
721593
29510
75
0.00 0.00 0.00
13446
7
active
wash
B 70 yes
722771
29522
09
6.05 7.66 9.26
13609
9
bedrock B 0 no
723859
29534
63
5.69 8.54 10.13
13775
9
young
fan
B 50 no
723911
29535
27
5.37 7.13 9.95
13785
4
bedrock C 0 no
724506
29541
44
3.22 5.63 7.24
13870
6
bedrock B 0 no
724862
29545
31
8.09 10.49 11.73
13923
0
bedrock C 0 no
725335
29550
45
6.85 8.85 10.07
13989
0
older fan B 200 no
725537
29553
47
4.59 6.15 7.42
14029
0
active
wash
B 20 no
725851
29556
82
9.39 10.21 11.42
14074
8
young
fan
B 60 no
726051
29559
73
8.21 10.26 11.05
14110
1
young
fan
A 10 no
726596
29566
45
8.89 10.13 12.18
14196
2
bedrock C 0 no
727060
29570
90
9.30 11.38 12.67
14260
5
older fan B 30 no
727355
29573
98
6.58 7.39 10.64
14303
0
bedrock B 0 no
727607
29577
43
8.02 9.12 10.89
14345
5
bedrock C 0 no
203

727944
29581
03
8.16 9.39 11.84
14394
8
bedrock C 0 no
728641
29590
71
3.69 5.77 7.04
14515
1
young
fan
B 10 no
728980
29595
73
9.30 10.09 10.89
14575
7
bedrock B 0 no
730147
29613
53
3.79 5.36 5.80
14789
0
bedrock B 0 yes
730926
29626
49
4.39 5.68 6.45
14940
0
older fan A 20 no
731290
29633
08
8.26 9.55 11.88
15015
3
older fan B 0 no
731365
29634
36
8.47 9.38 10.57
15030
0
bedrock B 70 no
730914
29626
52
9.84 10.79 11.99
15046
4
older fan A 20 no
731468
29636
32
7.50 10.61 10.08
15052
1
bedrock A 0 no
731464
29636
43
9.59 11.04 11.51
15061
2
bedrock A 0 no
732427
29655
02
8.66 10.35 11.05
15262
1
older fan A 180 no
732886
29664
04
4.32 5.52 6.72
15363
4
active
wash
B 80 yes
733734
29681
35
3.26 4.94 5.08
15557
4
bedrock B 0 yes
734203
29692
33
4.32 4.32 5.80
15676
6
bedrock C 0 no
735191
29712
33
5.81 6.51 7.15
15899
5
young
fan
B 20 no
735408
29717
16
7.53 8.56 9.19
15952
4
bedrock B 0 no
735635
29721
28
8.05 9.19 9.70
15999
3
bedrock C 0 no
736123
29731
33
3.20 4.76 5.29
16111
2
bedrock B 0 no
736348
29736
14
8.82 9.48 10.65
16164
4
old
alluvium
A 10 no
736452
29738
41
8.32 8.99 9.98
16189
2
bedrock B 0 no
736700
29744
25
4.34 4.85 6.65
16253
0
bedrock B 0 no
736823
29747
17
0.53 1.18 1.66
16284
6
active
wash
B 30 no
736847
29747
93
3.33 4.85 6.00
16292
4
bedrock C 0 no
204

737055
29753
40
8.32 9.98 10.65
16351
2
young
fan
B 10 no
737194
29757
36
7.66 8.82 9.48
16392
8
bedrock A 0 no
737362
29762
15
7.02 7.66 8.82
16443
8
old
alluvium
A 60 no
737686
29772
98
7.15 8.82 9.48
16556
4
bedrock C 0 no
737802
29776
04
8.01 8.82 9.67
16590
2
active
wash
C 150 no
738052
29781
25
7.66 8.82 9.48
16648
1
older fan B 150 no
738977
29803
46
0.00 1.66 2.35
16888
5
young
fan
B 50 yes
739130
29806
90
5.67 6.36 6.84
16926
1
older fan A 5 no
739458
29813
86
2.83 3.07 4.01
17003
1
bedrock A 0 no
739701
29818
61
4.99 5.67 6.36
17056
4
bedrock B 0 no
740095
29824
46
4.19 5.35 6.06
17126
2
active
wash
A 20 no
740121
29824
78
5.53 6.26 7.00
17130
5
older fan B 50 no
740167
29825
43
5.60 6.76 7.92
17137
2
active
wash
B 20 no
740231
29827
23
3.68 4.19 4.71
17156
1
older fan C 40 yes
740627
29840
82
1.16 1.64 2.33
17297
6
bedrock B 0 yes
740697
29842
90
3.16 4.19 6.33
17319
6
active
wash
B 20 yes
741288
29862
40
3.67 4.18 4.78
17523
3
old
alluvium
B 760 yes
741404
29867
24
0.52 1.04 1.64
17572
7
old
alluvium
A 610 yes
741439
29870
73
0.00 2.08 3.11
17607
4
old
alluvium
C 370 yes
741716
29878
42
2.32 2.32 3.03
17688
8
old
alluvium
A 240 yes
741764
29879
40
1.04 1.64 2.14
17699
9
old
alluvium
C 250 yes
741898
29881
36
1.16 1.47 1.87
17722
5
old
alluvium
B 330 yes
742368
29890
93
1.64 2.14 2.79
17828
1
active
wash
B 540 no
205

742549
29896
20
1.04 1.64 2.79
17883
8
active
wash
B 500 no
742748
29908
57
3.11 3.63 5.22
18007
4
older fan B 30 no
743245
29916
19
2.79 3.48 4.64
18095
6
old
alluvium
B 60 no
743391
29922
99
1.64 2.32 3.28
18164
5
older fan A 70 no
743433
29924
17
1.64 3.28 3.95
18177
0
older fan A 10 no
743615
29928
39
3.16 4.28 4.78
18223
3
old
alluvium
B 70 no
743624
29928
56
4.78 4.43 4.43
18224
7
active
wash
B 50 no
743667
29929
39
0.00 1.16 2.32
18234
4
old
alluvium
C 30 no
743895
29934
84
3.48 4.18 4.90
18293
2
active
wash
B 10 no
744022
29937
56
1.47 3.03 4.46
18323
0
bedrock B 0 no
744062
29938
37
1.16 2.60 3.67
18332
2
bedrock C 0 no
744271
29943
02
1.87 2.60 3.74
18382
8
old
alluvium
B 10 no
744518
29949
23
0.00 1.16 1.64
18449
8
old
alluvium
B 150 no
745538
29975
18
1.64 2.32 2.60
18729
7
bedrock B 0 no
745773
29980
76
0.00 2.14 4.43
18790
0
older fan C 50 no
745839
29982
35
0.00 1.04 1.56
18807
3
bedrock B 10 no
745961
29985
07
1.64 2.32 2.79
18837
0
active
wash
B 20 no
746235
29990
68
2.65 3.16 3.78
18899
4
older fan B 60 no
746344
29992
85
0.00 0.52 1.04
18923
7
bedrock B 0 no
746583
29997
76
1.04 2.14 3.28
18978
4
bedrock B 10 no
746821
30002
32
1.64 2.65 4.28
19029
9
bedrock C 0 no
747122
30008
06
1.64 2.79 3.47
19094
4
active
wash
C 40 no
752742
30095
44
0.00 1.16 1.64
20133
5
bedrock C 0 no
206

754922
30124
59
1.16 1.16 2.32
20497
6
active
wash
B 60 no
755151
30128
84
0.00 0.73 1.16
20545
9
bedrock B 0 no
757757
30167
88
1.72 1.96 2.43
21015
3
bedrock B 0 no
758509
30179
15
0.00 1.09 2.24
21150
8
old
alluvium
B 0 no
758921
30185
12
0.00 0.54 1.09
21223
3
bedrock C 0 no
758956
30186
36
1.63 2.17 2.72
21235
5
old
alluvium
B 10 no
761487
30214
37
0.00 0.00 0.00
21604
4
old
alluvium
B 40 no
a
Easting and northing values are given for UTM Zone 41 N, using the WGS84 datum.
b
Horizontal distance measured from the SW tip of a generalized fault trace located at E608505,
N2883357 (UTM Zon e41 N).
c
Significant sources of error are reported for each measurement: "Diffuse deformation" indicates
visible distributed deformation across the fault zone; "original geometry" indicates uncertaintiy in the
pre-offset geometry of the geomorphic feature; "obliquity" indicates that the piercing lines used to
determine offset are at high angle to the fault trace; "topographic effects" may lead to
misrepresentation of the true offset due to satellite look angle.
d
Distance to nearest bedrock outcrop rounded to the nearest 10 m.
e
Indicates whether the fault zone at the measurement is considered "structurally complex", as
defined in the main text.

207

Appendix B:
3D surface deformation in the 2016 MW 7.8 Kaikōura, New Zealand earthquake from
optical image correlation: Implications for strain localization and long-term evolution of
the Pacific-Australian plate boundary

208

Table S1.
WorldView image identification codes, look angles, and acquisition dates.
PreA ID
Acq.
date
Off-
nadi
r PreB ID
Acq.
date
Off-
nadi
r PostA ID
Acq.
date
Off-
nadi
r PostB ID
Acq.
date
Off-
nadi
r
102001003
F606400
08/05
/2015
28.4
102001003
9774100
08/05
/2015
28.4
103001006
6155900
19/02
/2017
17.6
103001006
3065900
19/02
/2017
24.5
102001003
F606400
08/05
/2015
28.4
102001003
9774100
08/05
/2015
28.4
102001005
887B300
09/12
/2016
29.2
102001005
726F300
09/12
/2016
25.1
104001000
75DDE00
07/02
/2015
26.3
104001000
709BB00
07/02
/2015
19.5
102001005
887B300
09/12
/2016
29.2
102001005
726F300
09/12
/2016
25.1
104001000
75DDE00
07/02
/2015
26.3
104001000
709BB00
07/02
/2015
19.5
103001006
0BE6B00
04/01
/2017
26.7
103001006
27CFE00
04/01
/2017
14.4
104001000
95EBF00
23/03
/2015
25.9
104001000
9B54A00
23/03
/2015
18.3
103001006
0BE6B00
04/01
/2017
26.7
103001006
0AE4100
02/12
/2016
17.3
103001003
C435200
10/02
/2015
27.6
103001003
F568800
10/02
/2015
19.2
103001006
0BE6B00
04/01
/2017
26.7
103001006
0AE4100
02/12
/2016
17.3
103001003
C435200
10/02
/2015
27.6
103001003
F568800
10/02
/2015
19.2
104001002
78F9A00
29/12
/2016
17.9
103001006
0AE4100
02/12
/2016
17.3
103001003
C435200
10/02
/2015
27.6
103001003
F568800
10/02
/2015
19.2
104001002
78F9A00
29/12
/2016
17.9
103001006
01E0400
14/11
/2016
33.8
103001003
C435200
10/02
/2015
27.6
103001003
F568800
10/02
/2015
19.2
104001002
854D000
29/01
/2017
12.8
104001002
7CC3300
29/01
/2017
26.6
104001000
70E2100
18/02
/2015
27.0
104001000
7226C00
18/02
/2015
27.8
104001002
854D000
29/01
/2017
12.8
104001002
7CC3300
29/01
/2017
26.6
104001000
70E2100
18/02
/2015
27.0
104001000
7226C00
18/02
/2015
27.8
102001005
B907D00
13/01
/2017
15.7
103001006
105D900
02/12
/2016
19.1
104001000
70E2100
18/02
/2015
27.0
104001000
7226C00
18/02
/2015
27.8
102001005
B907D00
13/01
/2017
15.7
103001005
F596A00
14/11
/2016
32.3
104001000
70E2100
18/02
/2015
27.0
104001000
7226C00
18/02
/2015
27.8
104001002
45BA200
14/11
/2016
42.7
102001005
B601500
15/11
/2016
44.0
103001003
DB08C00
12/02
/2015
26.7
103001003
E3E5600
12/02
/2015
27.2
102001005
B907D00
13/01
/2017
15.7
103001005
F596A00
14/11
/2016
32.3
103001003
DB08C00
12/02
/2015
26.7
103001003
E3E5600
12/02
/2015
27.2
103001005
FC4EB00
22/11
/2016
28.4
103001005
EC90C00
22/11
/2016
28.9
103001003
DB08C00
12/02
/2015
26.7
103001003
E3E5600
12/02
/2015
27.2
102001005
B470100
05/03
/2017
14.7
102001005
CBEB100
05/03
/2017
26.4
103001001
E50BD00
03/01
/2013
16.2
102001002
1DF4900
01/04
/2013
5.1
102001005
B907D00
13/01
/2017
15.7
103001005
F596A00
14/11
/2016
32.3
103001001
81DCE00
06/05
/2012
13.8
103001000
B571300
12/06
/2011
10.4
102001005
B470100
05/03
/2017
14.7
102001005
CBEB100
05/03
/2017
26.4
104001000
8D3B000
12/02
/2015
17.7
103001003
FD1D900
12/03
/2015
27.3
103001006
8769100
18/03
/2017
6.7
103001006
0D16E00
05/12
/2016
15.6
103001000
A3C3F00
21/04
/2011
7.3
103001001
B29B900
16/09
/2012
18.6
104001002
732A500
23/12
/2016
16.1
102001005
DC43900
25/02
/2017
24.9
103001003
FD1D900
12/03
/2015
27.3
104001000
8D3B000
12/02
/2015
17.7
103001005
FC4EB00
22/11
/2016
28.4
103001005
EC90C00
22/11
/2016
28.9
103001003
FD1D900
12/03
/2015
27.3
104001000
8D3B000
12/02
/2015
17.7
103001005
FAD0000
14/11
/2016
29.6
104001002
4D7E300
21/11
/2016
26.6
104001000
8D3B000
12/02
/2015
17.7
104001000
79F4900
12/02
/2015
26.8
103001005
FAD0000
14/11
/2016
29.6
104001002
4D7E300
21/11
/2016
26.6
103001003
CA2BD00
26/12
/2014
23.2
103001003
B832B00
26/12
/2014
28.5
103001005
FAD0000
14/11
/2016
29.6
104001002
4D7E300
21/11
/2016
26.6
103001003
CA2BD00
26/12
/2014
23.2
103001003
B832B00
26/12
/2014
28.5
104001002
9042E00
23/02
/2017
24.8
104001002
8559D00
23/02
/2017
11.8
209

103001003
CA2BD00
26/12
/2014
23.3
103001003
B832B00
26/12
/2014
28.5
104001002
4920500
21/11
/2016
29.1
103001005
E092200
27/11
/2016
27.7
104001002
306A800
03/10
/2016
20.8
104001002
32DF300
08/10
/2014
29.1
104001002
4920500
21/11
/2016
29.1
103001005
E092200
27/11
/2016
27.7
104001002
306A800
03/10
/2016
20.8
104001002
32DF300
08/10
/2014
29.1
103001006
02BD400
19/11
/2016
10.0
104001002
6623700
05/12
/2016
21.6
103001003
827F900
08/10
/2014
17.3
103001003
8010900
08/10
/2014
28.1
103001006
0C20600
05/12
/2016
20.5
103001006
1BC3F00
05/12
/2016
16.7
103001005
F1A6C00
09/10
/2016
22.9
103001005
B78C200
09/10
/2016
18.5
103001006
0CF7300
22/11
/2016
25.9
103001006
0C9DE00
22/11
/2016
28.1
ID's are DigitalGlobe image
identification numbers

Acq. dates are image acquisition dates
in DD/MM/YYYY format

Off-nadir angles are degrees
from nadir

210

Text S2.
Image Correlation Steps and Parameters
Computing the 3D surface deformation field involves two key steps(following the
methodology of Avouac and Leprince, 2015; see especially their Fig. 10).

S2.1a. Image selection and pre-processing
First, we manually identified overlapping stereo-image pairs based primarily on the date
of image acquisition, the spatial coverage with respect to the fault zone, and the orientation and
position of the satellite when the images were acquired. In the case where in-track stereo images
were unavailable (mostly in the pre-earthquake period), we used mutli-temporal mono images with
moderately different incidence angles to optimize the stereoscopic parallax. For our analyses, we
used a total of 64 images (29 pre-earthquake; 35 post-earthquake), collected between 1 to 66
months before the earthquake, and 1 to 124 days after the earthquake. All images were acquired
by the WorldView-1, WorldView-2, and WorldView-3 satellites, with nominal pixel resolutions
of 0.46m, 0.46 m, and 0.31 m, respectively [images © DigitalGlobe, 2018]. Full image details are
provided in the Image Details section of the supplemental documentation.
Before continuing with the correlation workflow, we found it necessary to apply minor
corrections to the WorldView imagery. The WorldView-1 and WorldView-2 images contained
subpixel artifacts resulting from the charge-coupled device (CCD) array configuration. We
corrected for those artifacts using the Ames Stereo Pipeline (ASP) tool suite (Shean et al., 2016).
Worldview-3 images did not contain CCD array artifacts. The WorldView images were delivered
to us as tiles (segments of a single acquisition). For processing efficiency, we mosaicked the tiles
211

into a single image with the original acquisition dimensions using the mosaicking tool of ASP.
During mosaicking, we found it was necessary to invoke an optional routine in ASP (“fix-seams”)
to account for camera inconsistencies between tiles, which otherwise produced significant artifacts
during the correlation process.

S2.1b. Image orthorectification and 2D correlation
We organized the stereo pre- and post-earthquake images into overlapping groups
(consisting of four images) to ensure complete coverage of the fault rupture, resulting a total of 30
sets of images. In each case, the pre-earthquake image with an incidence angle closest to nadir was
selected as the master, to maximize subsequent correlation with the two post-earthquake images.
We then orthorectified, coregistered, and computed the 2D displacement field between a master
(pre-earthquake) image and three slave images (one pre-earthquake, and two post-earthquake).
These steps were performed using the free COSI-Corr software package, which makes use of a
phase correlator that is highly optimized for sub-pixel precision (~1/10
th
of a pixel, (e.g., Leprince
et al., 2007a, 2007b, 2008[). First, we downsampled the images to 1×1 m resolution, which
reduced high-frequency landscape noise, and allowed for WorldView-1, -2, and -3 images to all
be sampled at the same resolution. We then orthorectified all four images using the same 8 m
resolution digital elevation model (DEM) provided by Land Information New Zealand (LINZ;
https://data.linz.govt.nz/). This step removed the long-wavelength stereo component from each
image, thereby reducing the distance over which the correlator had to search to find a
corresponding feature between the master and slave images. We then correlated the three master-
slave image pairs using a multi-scale sliding window and the phase correlator (initial window size
212

of 128×128 pixels, final window size of 32×32 pixels, with a step size of 8 pixels) which yielded
displacement values every 8 m, and truly independent displacements every 32 m. Because all
WorldView images were acquired with a non-vertical look angle, the disparity map between the
pre-earthquake master-slave pair contains only topographic (stereo) information, whereas the two
pre-/post-earthquake master-slave pairs contain both stereo and tectonic displacement. It is
important to note that because we orthorectify all the images with a lower resolution 8 m DEM,
any remaining stereo component in each correlation represents the high-frequency (< 8 m)
topographic variability not removed during the initial orthorectification process.

S2.2. Determination of 3D deformation
The second step involves computing sight vectors linking the satellite position (given in
the raw image XML data) with each pixel in the correlation maps (latitude and longitude are read
directly from the correlations, while elevation is interpolated from the 8 m DEM), so that we may
triangulate the high-resolution 3D ground positions at each pixel location. Because no tectonic
deformation is present in the pre-earthquake master-slave correlation, the pre-/pre-earthquake
disparity map reflects only the topographic stereo component remaining in the pre-earthquake
images (i.e. frequency < 8 m), which is small enough to measure with the COSI-Corr correlator
window. These horizontal disparities amount to a horizontal error in the triangulated XYZ
positions for a given pixel seen in both master and slave images; when all the stereo component is
removed from both images, the difference in triangulated XYZ pixel positions between the two
images will be minimal (i.e. no disparity). Therefore, we simply solve for the nearest point of
intersection between the pre-earthquake master and slave sight vectors (i.e. minimize the
213

difference in XYZ values), and record this new XYZ location for each pixel (grid) location. To
retrieve the 3D displacement, we then follow a similar process by computing the sight vectors for
the post-earthquake slave images, making sure to use the ground position for each pixel given
relative to the master pre-earthquake image in each case (which must all be on the same grid). The
post-earthquake XYZ positions at each pixel location are then calculated by solving for the closest
point of intersection between the two post-earthquake sight vectors. The final 3D displacement at
each pixel location is given by the difference between the triangulated pre- and post-earthquake
XYZ positions. This simple ray-tracing approach (which is implemented in IDL) is essentially the
same as that employed by many stereo photogrammetry software packages (e.g. Ames Stereo
Pipeline, MicMac). While the method doesn’t directly output a pre- or post-earthquake DEM, the
XYZ positions could be gridded for such a purpose (albeit at the lower resolution of the correlation
grid spacing, 4 m in this case). The proposed methodology differs from an alternative approach
for computing 3D displacements described by Kuo et al. [2018], which involves: (1) co-registering
pre- and post-earthquake imagery, (2) computing the pre- and post-DEMs, (3) orthorectifying the
raw pre- and post-imagery with the corresponding DEM, (4) correlating the ortho-images to give
horizontal displacements, and (5) differencing the DEMs. Although the two techniques should
yield similar results, the latter involves a more interactive (and longer) processing, while care must
also be taken to correctly account for the horizontal displacement field when differencing the
DEMs, especially in mountainous environments (e.g. Oskin, et al., 2012).

S2.3. Post-processing
214

The 3D correlations were processed through several steps to remove spurious values and
image ramps. The first post-processing steps focused on removing spurious values, or “de-noising”
the correlations:
First, outliers in each of the east-west, north-south, and vertical displacement fields were
removed using a specialized noise filter (provided by F. Ayoub, 2017). The filter identified
spurious values in the correlations by comparing each value to its neighbors within a search
window (window dimensions were set to 128 pixels; step size set to 8 pixels). Pixels determined
to be too dissimilar to their neighbors were replaced by null values. We also used this routine to
diminish isolated patches of pixels (e.g., ocean waves and clouds that were successfully correlated
by coincidence).
We then detrended the correlations using the Detrend tool in COSI-Corr (by fitting a 1
st

order polynomial and using two fit iterations). This step allowed us to visually identify any
unremoved outlying values, which were typically outside the two- or three-standard deviation
range of the majority of correlation values. Finally, we applied a 3×3-pixel-wide median filter to
the correlations, which smoothed the correlation values.
These steps left us with robust correlations in which noise was greatly reduced. However,
due to subtle inaccuracies in the orthorectification process and steps taken within the above post-
processing workflow itself, the correlations were typically left with linear (planar) ramps. Because
of these ramps, the correlations represent relative variations in surface deformation, but are
meaningless in an absolute sense. We removed the ramps in the east-west, and north-south
correlations by correcting our high-resolution, WorldView-based correlations to Landsat8
correlations produced by Holllingworth et al. (2017). The Landsat 8 correlations do not contain
215

ramps because the pre- and post-earthquake Landsat 8 images were collected on-nadir (looking
straight down), and the images were precisely georeferenced and orthorectified as Level-1
products courtesy of the USGS. To correct the WorldView east-west and north-south correlations
to the Landsat 8 correlations, we first downsampled the WorldView correlations to the resolution
of the Landsat 8 correlations (120 m), then subtracted the WorldView correlations from the
Landsat 8 correlations. This resulted in “difference maps” for the east-west and north-south
correlations. We fit each difference map with a plane, representing the linear ramp within the east-
west or north-south correlation. Finally, we removed the ramp by subtracting the plane-of-
difference from the east-west or north-south WorldView correlation (at its original, 8 m
resolution).
Though this method was effective for removing ramps from the horizontal (east-west and
north-south) correlations, we did not have access to a continuous vertical deformation field as a
reference to which we could correct our vertical WorldView correlations. Instead, removing ramps
from the vertical correlations was a two-step process: (1) we first subtracted relative differences
between overlapping adjacent images so that all vertical correlations had the same (unknown)
linear ramp; (2) we then mosaicked the adjusted vertical correlations and corrected the entire
mosaic to GPS reference offsets published in Hamling et al. (2017). In the first step, we began by
subtracting the difference between the southwest-most vertical correlation and the neighboring,
overlapping correlation immediately to the northeast. Once the neighboring correlation was
detrended relative to the southwestern correlation, we then used it as a reference for detrending the
next, more northeastern correlation. We continued this process until the most northeastern
correlation was detrended relative to the most southwestern correlation. Thus, all the vertical
correlations were affected by the same linear ramp. We then mosaicked those vertical correlations
216

into a single map of vertical displacement plus some unknown image ramp. In the second step, we
detrended the mosaic relative to the GPS measurements of coseismic vertical displacement
throughout the northeastern South Island reported by Hamling et al. (2017). To make our
correction more robust to noise in the vertical correlation, we used the median value of the
correlation map within a 15×15 m window around each GPS station location. We used a total of
28 GPS stations in all (see table below). After fitting the vertical correlation, the root mean squared
(RMS) value of the residual misfit is 1.2 m. The map below shows the distribution of the GPS
stations, and the residual misfits for each of those stations. This final step established the
WorldView-derived vertical deformation field in an absolute reference frame with regard to
vertical displacement.

217

Figure S2a. (Left) Locations of GPS stations; color bar shows the corrected vertical deformation
field from correlation of WorldView imagery in meters. (Right) residual misfits between the
corrected vertical deformation field and coseismic vertical GPS displacements; color bar shows
the magnitude of the misfit in meters.

Table S2a. GPS stations from Hamling et al. (2017) used
to correct our vertical deformation field
UTM easting UTM northing Residual misfit (m)
645487 5275218 0.514
647184 5287283 1.724
708434 5299873 0.389
767219 5373094 0.232
665681 5260952 -4.009
664684 5271937 -1.117
721881 5300012 1.203
218

715260 5312395 -0.069
715546 5349390 0.697
749729 5379190 -0.530
750087 5378062 -1.804
647497 5272160 -1.069
645771 5283929 1.134
744519 5362435 -0.130
762544 5393860 -1.199
753784 5380174 -1.019
751722 5370034 -0.655
749436 5359284 1.225
700432 5306696 0.966
700060 5304016 0.757
684970 5301586 0.768
680310 5293678 0.823
760513 5365167 0.343
741600 5328793 0.514
679682 5277727 -0.887
694663 5279115 -0.518
754639 5377467 -0.682
749293 5345493 2.400
Mean of residuals: 0.000 ± 1.270 m
RMS of residuals: 1.247 m

219

Figure S3a.

220

Figure S3b.

221

Figure S3c.

222

Table S4.
Displacement measurements along all faults, collected at 500 m spacing
Fault Strike Upar
a
Upar err
b
Uper
c
Uper err
d
Uver
e
Uver err
f
Unet
g
Unet err
h

Conway
Charwell
42 0.67 0.3 0.34 0.43 1.82 0.58 1.97 0.78
Conway
Charwell
48 0.57 0.3 0.1 0.37 2.36 0.53 2.43 0.71
Conway
Charwell
12 0.3 0.36 0.34 0.33 1.51 0.53 1.58 0.72
Conway
Charwell
45 0.49 0.23 0.17 0.23 1.05 0.32 1.17 0.45
Conway
Charwell
65 0.56 0.29 0.25 0.36 0.85 0.28 1.05 0.54
Conway
Charwell
55 0.57 0.21 0.55 0.28 0.41 0.34 0.89 0.49
Conway
Charwell
50 0.61 0.18 0.44 0.24 0.38 0.29 0.85 0.42
Conway
Charwell
70 0.43 0.11 0.46 0.16 0.58 0.25 0.85 0.32
Conway
Charwell
60 0.3 0.08 0.17 0.16 0.08 0.21 0.35 0.28
Conway
Charwell
58 0.28 0.15 0.02 0.17 0.23 0.24 0.37 0.33
Conway
Charwell
68 -0.02 0.12 -0.26 0.24 0.09 0.33 0.27 0.42
Conway
Charwell
57 0.12 0.13 -0.13 0.17 0.17 0.29 0.25 0.36
Conway
Charwell
53 0.05 0.1 0.19 0.12 0.29 0.15 0.35 0.22
Corner Hill 3 -0.2 0.22 -1.61 0.43 0.01 0.45 1.62 0.66
Corner Hill 16 -0.34 0.21 -0.36 0.33 0.13 0.43 0.51 0.58
Corner Hill 18 0.29 0.12 -0.11 0.19 0.37 0.53 0.48 0.58
Fidget 89 1.29 0.26 0.13 0.25 0.18 0.52 1.31 0.63
Fidget 79 1.17 0.37 0.1 0.39 0.27 0.88 1.2 1.03
Fidget 65 0.82 0.33 0.54 0.38 0.24 1.06 1.02 1.17
Fidget 78 1.16 0.38 0.18 0.43 0.42 1.12 1.25 1.26
Fidget 88 1.13 0.36 0.02 0.31 0.01 0.59 1.13 0.76
Fidget 83 1.2 0.44 0.36 0.39 0.41 0.83 1.32 1.02
Fidget 75 1.39 0.45 0.77 0.54 0.01 0.93 1.59 1.17
Fidget 86 1.09 0.42 0.66 0.34 0.28 0.93 1.31 1.07
Fidget 93 0.69 0.48 0.48 0.32 0.4 1.09 0.93 1.23
223

Fidget 73 0.57 0.38 0.68 0.31 0.07 0.92 0.89 1.04
Fidget 64 0.29 0.46 0.73 0.27 0.36 1.06 0.86 1.19
Fidget 68 0.42 0.42 0.86 0.23 0.72 1.06 1.19 1.16
Fidget 76 0.31 0.23 0.29 0.12 0.27 0.93 0.5 0.96
Fidget 78 0.38 0.28 1.43 0.26 0.54 0.86 1.57 0.94
Fidget 81 0.1 0.34 0.91 0.25 0.7 1.13 1.15 1.21
Fidget 83 0.26 0.47 0.77 0.18 2.11 1.49 2.26 1.58
Fidget 82 0.37 0.37 0.49 0.12 1.12 1.14 1.27 1.21
Fidget 72 0.13 0.55 0.22 0.18 1.32 1.76 1.35 1.85
Fidget 69 0.02 0.54 0 0.14 2.33 1.47 2.33 1.57
Fidget 65 0.02 0.39 0.12 0.17 0.25 1.46 0.28 1.52
Fidget 60 0.11 0.31 0.7 0.32 0.22 1.03 0.74 1.12
Fidget 67 0.19 0.36 0.78 0.14 1.14 1.29 1.4 1.35
Fidget 71 0.19 0.31 0.44 0.13 0.26 1.12 0.54 1.17
Fidget 67 0.1 0.3 0.75 0.23 0.17 0.97 0.78 1.04
Fidget 65 0.16 0.38 0.62 0.15 1.61 1.1 1.74 1.17
Fidget 73 0.29 0.37 0.79 0.2 1.51 1.11 1.73 1.19
Fidget 77 0.55 0.36 0.87 0.48 2.18 1.14 2.41 1.29
Fidget 80 0.03 0.37 1.29 0.2 0.07 1.4 1.3 1.46
Fidget 79 0.34 0.36 1.21 0.25 0.27 1.46 1.29 1.52
Fidget 79 0.14 0.32 1.45 0.14 0.73 1.08 1.63 1.14
Fidget 85 0.07 0.29 1.34 0.19 0.75 1.16 1.54 1.21
Fidget 88 0.14 0.31 1.36 0.25 0.08 1.21 1.37 1.27
Fidget 93 0.05 0.3 1.27 0.3 1.03 1.16 1.63 1.24
Fidget 90 -0.29 0.28 0.98 0.2 0.89 0.95 1.36 1.01
Fidget 93 1.2 0.21 0.04 0.23 1.63 0.77 2.02 0.83
HumpsE 94 0.34 0.55 0.18 0.34 0.35 0.76 0.52 1
HumpsE 75 1.11 0.61 0.01 0.32 0.42 0.78 1.19 1.04
HumpsE 86 1.17 0.54 -0.24 0.46 0.81 0.77 1.44 1.05
HumpsE 71 0.95 0.37 0.04 0.43 0.48 0.58 1.06 0.81
HumpsE 60 0.93 0.4 0.45 0.25 0.5 0.49 1.14 0.68
HumpsE 97 1.46 0.22 0.01 0.7 0.05 0.4 1.46 0.84
HumpsE 91 0.77 0.18 -0.06 0.19 0.25 0.24 0.81 0.36
HumpsE 95 1.27 0.16 -0.05 0.31 0.14 0.33 1.28 0.48
HumpsE 79 1.55 0.18 0.16 0.42 0.49 0.29 1.63 0.54
HumpsE 58 1.07 0.24 0.16 0.18 1.11 0.41 1.54 0.51
HumpsE 48 0.83 0.18 0.01 0.19 1.36 0.45 1.6 0.52
HumpsE 75 0.9 0.17 0.07 0.17 0.95 0.43 1.31 0.5
HumpsE 68 0.74 0.23 -0.16 0.31 1.84 0.42 1.99 0.57
HumpsE 72 1.51 0.17 -1.35 0.17 1.76 0.35 2.68 0.42
HumpsE 78 1.75 0.1 -1.21 0.11 1.23 0.2 2.46 0.25
HumpsE 78 1.8 0.25 -1.09 0.23 1.8 0.37 2.77 0.5
224

HumpsE 63 2.65 0.24 -0.59 0.23 1.31 0.5 3.01 0.6
HumpsE 50 2.29 0.23 1.00 0.20 1.75 0.41 3.05 0.51
HumpsE 64 2.52 0.19 0.03 0.20 2.18 0.35 3.33 0.45
HumpsE 55 2.07 0.20 0.09 0.16 1.37 0.41 2.48 0.49
HumpsE 56 3.48 0.28 0.19 0.21 1.30 0.46 3.72 0.58
HumpsE 69 2.94 0.22 -0.64 0.23 1.22 0.36 3.24 0.48
HumpsE 94 2.42 0.16 -0.92 0.23 1.50 0.4 2.99 0.49
HumpsE 97 2.25 0.17 -1.59 0.25 0.49 0.48 2.80 0.56
HumpsE 80 2.86 0.12 -0.83 0.17 0.45 0.35 3.01 0.40
HumpsE 67 2.95 0.15 -0.41 0.19 0.33 0.39 3.00 0.46
HumpsE 66 3.08 0.13 -0.25 0.24 0.03 0.36 3.09 0.45
HumpsE 62 1.32 0.83 -0.74 0.63 0.13 0.88 1.52 1.37
HumpsE 54 1.54 0.40 -0.62 0.30 0.50 0.50 1.73 0.71
HumpsE 48 1.24 0.44 0.11 0.37 0.04 0.55 1.25 0.79
HumpsE 54 1.27 0.59 -0.04 0.59 0.61 0.50 1.41 0.97
HumpsE 55 1.57 0.19 -0.08 0.13 0.02 0.41 1.57 0.47
HumpsE 50 1.59 0.25 0.51 0.21 0.02 0.31 1.67 0.45
HumpsE 53 1.38 0.29 0.38 0.25 0.11 0.46 1.44 0.6
HumpsE 65 1.40 0.29 -0.34 0.28 0.53 0.49 1.53 0.63
HumpsE 50 1.88 0.24 -0.14 0.26 0.20 0.51 1.90 0.62
HumpsE 48 1.49 0.26 0.36 0.24 0.29 0.46 1.56 0.58
HumpsE 64 1.08 0.28 -0.06 0.28 0.74 0.44 1.31 0.59
HumpsE 59 1.32 0.26 0.41 0.15 0.28 0.42 1.41 0.52
HumpsE 61 1.09 0.24 -0.26 0.26 0.42 0.33 1.20 0.48
HumpsE 68 0.70 0.31 -0.06 0.23 0.81 0.64 1.08 0.74
HumpsE 75 0.67 0.21 0.00 0.26 0.39 0.35 0.78 0.48
HumpsE 73 0.28 0.23 -0.18 0.21 0.85 0.31 0.91 0.44
HumpsE 55 0.64 0.18 0.27 0.15 0.11 0.32 0.71 0.39
HumpsE 66 0.22 0.15 0.43 0.23 0.37 0.27 0.61 0.39
HumpsE 41 0.19 0.31 0.46 0.18 0.29 0.47 0.58 0.59
HumpsE 48 0.12 0.29 0.76 0.20 0.91 0.53 1.20 0.64
HumpsE 51 0.46 0.12 -0.51 0.13 0.40 0.17 0.80 0.25
HumpsE 36 0.70 0.17 -0.89 0.30 0.00 0.25 1.13 0.43
HumpsE 22 0.70 0.33 -0.37 0.28 0.06 0.36 0.79 0.56
HumpsE 19 0.33 0.26 -0.06 0.26 0.03 0.46 0.34 0.58
HumpsW 73 0.20 0.24 -0.18 0.37 0.04 0.79 0.27 0.91
HumpsW 62 0.30 0.25 -0.17 0.38 0.52 0.73 0.63 0.86
HumpsW 44 0.48 0.34 -0.13 0.42 0.35 0.76 0.61 0.93
HumpsW 60 0.12 0.32 0.04 0.43 0.65 0.77 0.67 0.94
HumpsW 85 0.17 0.33 -0.36 0.40 0.52 0.69 0.66 0.87
HumpsW 93 0.35 0.26 -0.70 0.29 0.57 0.60 0.97 0.72
HumpsW 90 0.49 0.39 -0.72 0.34 0.28 0.66 0.92 0.83
225

HumpsW 91 0.67 0.24 -0.65 0.31 0.44 0.44 1.03 0.59
HumpsW 85 0.69 0.28 -0.64 0.40 0.23 0.46 0.97 0.67
HumpsW 97 0.83 0.43 -0.33 0.52 0.25 0.63 0.92 0.93
HumpsW 75 0.66 0.67 -0.04 0.60 0.06 1.23 0.66 1.52
HumpsW 82 1.59 0.71 0.64 0.54 0.71 0.91 1.86 1.27
HumpsW 95 1.37 0.65 0.31 0.67 0.08 1.12 1.41 1.46
HumpsW 92 1.13 0.60 -0.13 0.51 0.79 0.87 1.38 1.17
HumpsW 89 1.88 0.53 -0.20 0.48 0.63 0.76 2.00 1.04
HumpsW 54 0.69 0.61 0.32 0.68 0.30 0.86 0.81 1.25
HumpsW 63 0.28 0.63 -0.29 0.72 0.63 1.47 0.75 1.76
HumpsW 76 1.93 0.55 0.38 0.5 0.80 0.85 2.12 1.13
HumpsW 87 2.30 0.61 -0.21 0.52 0.38 0.78 2.34 1.12
HumpsW 109 1.83 0.78 -1.05 0.57 0.13 0.87 2.11 1.30
HumpsW 103 1.50 0.76 -0.69 0.51 0.16 0.93 1.66 1.31
HumpsW 89 1.58 0.70 -0.39 0.46 0.06 0.95 1.63 1.27
HumpsW 98 1.69 0.52 -0.86 0.48 0.16 0.93 1.91 1.17
HumpsW 81 2.39 0.62 -0.69 0.52 0.93 1.15 2.65 1.41
HumpsW 77 1.71 0.76 0.00 0.49 0.30 1.09 1.73 1.42
HumpsW 85 2.17 0.64 -0.34 0.44 0.43 1.01 2.24 1.27
HumpsW 77 1.97 0.55 0.16 0.39 0.40 0.82 2.01 1.07
HumpsW 75 1.84 0.48 0.06 0.42 0.13 1.01 1.84 1.20
HumpsW 75 1.92 0.60 0.12 0.49 0.20 0.86 1.93 1.16
HumpsW 70 2.12 0.53 0.19 0.42 0.08 0.87 2.13 1.10
HumpsW 82 1.78 0.75 -0.28 0.48 0.44 1.14 1.85 1.45
HumpsW 36 1.17 0.70 1.08 0.51 0.22 1.02 1.61 1.34
HumpsW 69 1.93 0.41 -0.07 0.20 1.19 0.49 2.27 0.67
HumpsW 73 1.25 0.35 -0.39 0.18 0.23 0.45 1.33 0.60
HumpsW 77 1.62 0.87 -0.55 0.28 0.34 0.66 1.74 1.13
HumpsW 95 1.28 0.41 -0.87 0.32 0.03 0.55 1.55 0.75
Hundalee 63 -0.15 0.25 1.68 0.30 0.35 0.58 1.72 0.70
Hundalee 55 -0.46 0.52 1.58 0.36 1.12 0.90 1.99 1.10
Hundalee 54 -1.36 0.51 1.72 0.31 1.07 1.13 2.44 1.28
Hundalee 64 -0.28 0.22 1.92 0.16 0.5 0.67 2.00 0.72
Hundalee 67 -0.48 0.36 2.07 0.23 0.59 0.69 2.21 0.81
Hundalee 85 0.82 0.39 2.10 0.23 0.46 0.89 2.30 1.00
Hundalee 86 0.92 0.69 2.37 0.33 0.62 1.66 2.62 1.83
Hundalee 40 -0.91 0.41 2.40 0.45 0.74 0.98 2.67 1.15
Hundalee 26 -1.63 0.29 2.71 0.59 1.76 0.79 3.62 1.03
Hundalee 7 -2.73 0.28 1.84 0.56 1.14 0.99 3.49 1.17
Hundalee 1 -2.97 0.24 1.51 0.57 1.99 0.91 3.88 1.1
Hundalee 26 -1.83 0.40 2.86 0.57 2.34 1.03 4.13 1.24
Hundalee 72 1.08 0.65 4.08 0.25 1.01 0.68 4.34 0.97
226

Hundalee 7 -2.8 0.35 1.41 0.58 0.47 1.29 3.17 1.46
Hundalee 5 -2.47 0.21 1.76 0.54 0.41 1.08 3.06 1.23
Hundalee 157 -3.12 0.35 0.9 0.57 0.6 1.35 3.3 1.5
Hundalee 133 -3 0.47 -0.22 0.54 0.44 1.35 3.03 1.52
Hundalee 4 -2.32 0.36 1.19 0.44 1.72 0.73 3.13 0.92
Hundalee 34 -1.3 0.58 2.78 0.46 1.45 1.25 3.39 1.46
Hundalee 113 -0.08 0.49 0.61 0.4 0.07 0.57 0.61 0.85
Hundalee 138 0.58 0.28 0.69 0.34 0.39 0.58 0.98 0.73
Hundalee 84 -0.2 0.62 0.96 0.21 0.52 0.89 1.11 1.10
Hundalee 64 0.61 0.57 1.15 0.42 1.75 0.62 2.18 0.94
Hundalee 109 1.47 0.63 0.57 0.32 0.98 0.77 1.86 1.05
Hundalee 55 0.15 0.68 1.26 0.47 1.35 0.81 1.86 1.16
Hundalee 74 0.68 0.49 1.5 0.2 0.55 0.63 1.74 0.82
Hundalee 46 -0.26 0.67 2.18 0.67 1.54 0.81 2.68 1.25
Hundalee 26 -1.71 0.4 1.53 0.74 0.57 0.53 2.36 1.00
Hundalee 44 -1.01 0.45 2.04 0.54 0.28 0.61 2.29 0.93
Hundalee 68 0.83 0.84 2.26 0.47 1.48 0.9 2.82 1.32
Hundalee 43 -0.62 0.6 2.51 0.91 0.91 0.92 2.74 1.42
Hundalee 36 0.33 0.37 3.52 0.7 0.05 0.76 3.54 1.1
Hundalee 62 1.82 0.75 2.34 0.53 0.67 0.8 3.04 1.22
Hundalee 67 1.64 0.68 2.44 0.43 0.88 1.28 3.07 1.51
Jordan
Thrust
29 3.55 0.33 0.27 0.64 0.76 1.51 3.64 1.67
Jordan
Thrust
46 3.78 0.37 -0.36 0.35 1.19 1.51 3.98 1.59
Jordan
Thrust
41 3.41 0.51 1.05 1.05 0.60 1.57 3.62 1.96
Jordan
Thrust
36 3.42 0.30 2.21 0.50 1.42 1.51 4.32 1.62
Jordan
Thrust
31 3.53 0.23 -0.03 0.23 1.39 1.16 3.79 1.20
Jordan
Thrust
22 4.13 0.20 0.20 0.22 0.80 1.31 4.21 1.34
Jordan
Thrust
29 3.95 0.55 0.68 0.44 1.08 2.20 4.15 2.31
Jordan
Thrust
41 4.39 0.36 0.00 0.38 0.84 2.06 4.47 2.13
Jordan
Thrust
39 4.38 0.31 0.22 0.30 0.78 1.21 4.46 1.29
Jordan
Thrust
52 4.44 0.26 -1.65 0.18 0.13 0.94 4.74 0.99
227

Jordan
Thrust
51 5.08 0.32 -0.24 0.29 0.21 1.31 5.09 1.38
Jordan
Thrust
29 5.15 0.28 1.7 0.28 0.27 1.29 5.43 1.34
Jordan
Thrust
41 5.17 0.36 -0.26 0.32 0.69 1.61 5.22 1.68
Jordan
Thrust
33 4.76 0.3 0.31 0.31 1.91 1.45 5.14 1.51
Jordan
Thrust
40 5.09 0.38 -0.35 0.19 0.73 1.24 5.16 1.31
Jordan
Thrust
31 5.87 0.26 -0.07 0.21 0.09 1.14 5.87 1.19
Jordan
Thrust
23 6.5 0.21 0.95 0.27 0.34 1.2 6.58 1.25
Jordan
Thrust
33 6.68 0.32 0.2 0.23 1.56 1.18 6.86 1.25
Jordan
Thrust
36 7.49 0.29 -0.4 0.23 2.09 1.28 7.79 1.33
Jordan
Thrust
32 8.22 0.38 0.5 0.24 2.95 1.49 8.75 1.55
Jordan
Thrust
54 8.17 0.29 -2.4 0.26 3.21 1.72 9.1 1.76
Jordan
Thrust
49 8.94 0.35 -2 0.29 4.12 1.12 10.04 1.21
Jordan
Thrust
41 9.27 0.34 -0.84 0.26 4.37 1.03 10.29 1.12
Jordan
Thrust
44 10.12 0.26 -1.96 0.21 5.78 1.4 11.82 1.44
Jordan
Thrust
43 9.38 0.37 -0.18 0.32 6.55 1.36 11.45 1.44
Jordan
Thrust
54 7.84 0.32 -1.79 0.28 6.93 1.07 10.62 1.15
Jordan
Thrust
61 5.56 0.33 -1.79 0.3 3.07 0.64 6.6 0.78
Jordan
Thrust
63 4.25 0.27 -1.28 0.2 1.39 0.68 4.65 0.76
Jordan
Thrust
40 2.4 0.32 0.51 0.33 2.11 0.53 3.24 0.7
Kekerengu 74 1.55 0.26 -1.2 0.3 0.91 1.09 2.16 1.16
Kekerengu 53 2.17 0.35 -0.71 0.19 2.3 1.05 3.24 1.12
Kekerengu 44 1.58 0.25 -0.72 0.27 1.81 1.13 2.51 1.19
Kekerengu 30 2.92 0.28 -1.00 0.21 1.61 1.63 3.48 1.67
228

Kekerengu 45 7.34 0.27 0.18 0.28 2.88 1.26 7.89 1.32
Kekerengu 58 8.42 0.47 0.53 0.4 0.86 1.09 8.48 1.25
Kekerengu 43 8.9 0.43 3.39 0.48 1.15 1.06 9.59 1.25
Kekerengu 38 8.38 0.61 3.86 0.38 2.57 1.02 9.57 1.25
Kekerengu 39 8.66 0.36 3.97 0.3 3.94 1.07 10.31 1.17
Kekerengu 50 9.1 0.6 2.3 0.35 3.7 1.27 10.09 1.44
Kekerengu 54 9.3 0.36 1.76 0.29 3.21 1.08 9.99 1.18
Kekerengu 43 9.23 0.28 3.17 0.4 2.83 1.12 10.16 1.22
Kekerengu 54 9.6 0.27 0.99 0.4 3.27 0.93 10.19 1.05
Kekerengu 52 9.8 0.28 1.72 0.3 2.65 1.02 10.29 1.1
Kekerengu 42 9.87 0.34 3.6 0.33 2.98 1.04 10.92 1.15
Kekerengu 45 10.39 0.39 2.77 0.58 3.3 1.2 11.24 1.39
Kekerengu 48 10.1 0.27 2.72 0.42 2.66 0.62 10.79 0.8
Kekerengu 49 10.47 0.34 2.86 0.33 1.96 0.79 11.02 0.92
Kekerengu 52 11.28 0.24 1.86 0.37 2.33 0.72 11.66 0.84
Kekerengu 53 11.03 0.22 1.08 0.35 1.44 0.72 11.18 0.83
Kekerengu 52 10.9 0.18 0.97 0.25 1.1 0.55 11.00 0.63
Kekerengu 48 10.9 0.21 1.6 0.23 1.3 0.53 11.09 0.61
Kekerengu 38 10.23 0.14 3.69 0.16 0.92 0.44 10.91 0.49
Kekerengu 43 10.96 0.18 2.73 0.25 1.78 0.6 11.44 0.68
Kekerengu 39 10.6 0.22 2.94 0.3 2.37 0.52 11.25 0.64
Kekerengu 53 11.08 0.37 -0.06 0.39 3.34 0.88 11.57 1.03
Kekerengu 63 10.39 0.39 -2.02 0.35 3.81 0.6 11.25 0.8
Kekerengu 58 10.44 0.24 -1.06 0.25 3.96 0.52 11.22 0.62
Kekerengu 56 11.52 0.23 0.22 0.19 3.29 0.5 11.98 0.58
Kekerengu 39 11.55 0.26 3.17 0.18 1.16 0.65 12.04 0.72
Kekerengu 42 10.89 0.32 1.68 0.27 2.21 0.76 11.23 0.87
Kekerengu 61 10.75 0.21 -1.88 0.32 0.6 0.7 10.93 0.8
Kekerengu 58 11.17 0.28 -1.22 0.31 0.63 0.73 11.25 0.84
Kekerengu 54 10.92 0.29 -0.37 0.43 0.22 1.07 10.93 1.19
Kekerengu 50 11.15 0.29 -0.18 0.31 0.38 0.7 11.16 0.82
Kekerengu 58 10.7 0.28 -0.85 0.51 0.17 0.92 10.74 1.09
Kekerengu 55 10.43 0.28 0.12 0.37 0.64 0.93 10.45 1.04
Kekerengu 58 10.46 0.31 0.25 0.33 1.16 0.7 10.53 0.84
Kekerengu 69 10.58 0.23 -1.32 0.35 1.32 0.55 10.74 0.69
Kekerengu 62 10.68 0.25 0.2 0.4 1.26 0.81 10.75 0.94
Kekerengu 62 10.52 0.22 0.63 0.43 0.85 0.67 10.57 0.83
Kekerengu 65 10.67 0.25 0.07 0.45 1.10 0.76 10.73 0.92
Kekerengu 64 10.65 0.27 0.5 0.52 2.39 0.90 10.93 1.08
Kekerengu 65 10.06 0.2 1.06 0.25 1.51 0.45 10.23 0.56
Kekerengu 72 10.45 0.35 -0.26 0.24 1.07 0.44 10.51 0.61
Kekerengu 77 10.41 0.17 -1.41 0.24 1.23 0.51 10.58 0.59
229

Kekerengu 70 10.59 0.21 -0.18 0.24 1.80 0.42 10.74 0.53
Kekerengu 71 11.02 0.36 -0.36 0.30 1.76 0.43 11.17 0.64
Kekerengu 54 9.94 0.26 3.42 0.24 1.73 0.39 10.65 0.53
Kekerengu 72 8.23 0.15 -0.05 0.21 2.29 0.42 8.55 0.49
Kekerengu 62 8.2 0.11 0.83 0.13 1.77 0.25 8.43 0.31
Kekerengu 72 7.16 0.12 -0.23 0.16 1.67 0.29 7.35 0.35
Kekerengu 73 7.84 0.14 -1.43 0.34 1.79 0.55 8.17 0.66
Manakau 49 0.6 0.71 -2.33 0.63 3.49 1.70 4.24 1.95
Manakau 68 -0.97 0.65 -1.93 0.85 0.16 1.72 2.17 2.03
Manakau 46 1.79 0.70 -1.90 0.87 1.21 2.17 2.88 2.44
Manakau 47 1.25 0.57 -1.18 0.72 0.12 1.76 1.72 1.99
Manakau 42 2.9 0.36 -1.89 0.70 1.44 1.39 3.75 1.6
Manakau 55 0.83 0.48 -1.98 0.64 1.96 1.42 2.91 1.63
Manakau 76 0.51 0.56 -0.84 0.65 0.89 1.49 1.33 1.72
Manakau 61 1.22 0.58 -0.36 0.68 1.81 1.50 2.21 1.75
Manakau 85 0.5 0.53 -0.42 0.60 0.81 1.91 1.04 2.07
Manakau 51 0.73 0.55 -0.35 0.70 1.67 2.12 1.86 2.3
Manakau 67 0.32 0.12 -0.08 0.25 0.17 0.49 0.37 0.56
Manakau 67 0.27 0.41 -0.78 0.48 1.71 0.99 1.90 1.17
Manakau 41 1.18 0.55 -0.91 0.51 1.17 1.72 1.89 1.88
Manakau 58 1.45 0.71 -1.38 0.68 1.77 2.37 2.67 2.57
Manakau 76 0.02 0.34 -0.85 0.32 1.92 1.19 2.10 1.28
Manakau 72 0.55 0.42 -0.59 0.25 2.04 1.47 2.19 1.55
Manakau 46 1.65 0.45 -0.17 0.60 4.08 1.76 4.41 1.92
Manakau 22 0.74 0.35 -0.46 0.50 2.81 1.34 2.94 1.47
Manakau 60 0.52 0.47 -1.28 0.36 1.37 1.51 1.95 1.62
Manakau 68 -0.19 0.46 -0.92 0.54 1.54 1.02 1.80 1.24
Manakau 64 0.18 0.74 -0.74 0.51 0.7 1.57 1.04 1.80
Manakau 55 -1.02 0.41 -0.04 0.32 2.93 1.29 3.11 1.39
Manakau 37 -0.78 0.56 -0.39 0.47 2.79 2.08 2.92 2.20
Manakau 53 0.35 0.32 -0.16 0.17 0.45 1.40 0.59 1.44
Manakau 54 0.45 0.33 -0.26 0.25 0.05 1.44 0.52 1.50
Manakau 35 0.66 0.31 -0.14 0.28 0.04 1.23 0.68 1.30
Manakau 46 0.62 0.45 -0.28 0.28 0.02 1.77 0.68 1.84
Manakau 51 0.45 0.49 -0.40 0.21 0.48 1.82 0.77 1.90
Manakau 65 0.17 0.79 -1.17 0.77 2.33 2.14 2.61 2.41
Manakau 92 -1.36 0.57 -0.85 0.43 2.47 1.61 2.95 1.76
NLeader 43 -0.85 0.29 0.81 0.25 0.58 0.54 1.31 0.66
NLeader 65 -0.36 0.14 2.05 0.24 0.36 0.33 2.11 0.44
NLeader 71 0.95 0.33 1.49 0.26 0.42 0.36 1.82 0.55
NLeader 62 0.45 0.12 1.67 0.20 0.51 0.41 1.8 0.47
NLeader 50 0.21 0.17 1.24 0.20 0.19 0.34 1.27 0.43
230

NLeader 51 0.62 0.18 0.47 0.20 0.00 0.41 0.78 0.49
NLeader 16 -1.9 0.22 1.53 0.21 0.23 0.51 2.45 0.59
NLeader 23 -1.49 0.2 2.09 0.16 0.17 0.45 2.57 0.52
NLeader 1 -2.31 0.27 1.48 0.18 0.14 0.44 2.74 0.55
NLeader 29 -1.27 0.24 2.30 0.35 0.91 0.55 2.78 0.70
NLeader 175 -2.17 0.26 1.34 0.25 1.03 0.51 2.75 0.63
NLeader 41 0.18 0.19 2.83 0.18 0.41 0.38 2.87 0.46
NLeader 176 -1.83 0.21 2.36 0.28 0.07 0.39 2.99 0.52
NLeader 129 -3.10 0.21 0.47 0.19 0.67 0.33 3.21 0.44
NLeader 123 -2.95 0.38 -0.49 0.33 0.48 0.42 3.03 0.66
NLeader 147 -3.80 0.29 1.26 0.16 0.55 0.39 4.04 0.51
NLeader 175 -2.87 0.25 3.94 0.18 0.23 0.32 4.88 0.44
NLeader 157 -3.06 0.35 3.15 0.18 0.41 0.6 4.41 0.72
NLeader 134 0.68 0.30 0.50 0.22 0.21 0.6 0.87 0.70
NLeader 83 0.15 0.10 1.15 0.24 0.16 0.35 1.17 0.44
NLeader 59 -0.62 0.13 1.15 0.18 1.19 0.36 1.77 0.42
NLeader 69 0.45 0.19 1.92 0.29 1.08 0.44 2.25 0.56
NLeader 64 0.33 0.16 1.86 0.18 1.43 0.37 2.37 0.44
NLeader 50 -0.42 0.20 2.10 0.20 0.83 0.44 2.30 0.52
NLeader 13 1.11 0.27 2.20 0.27 1.55 0.34 2.91 0.51
NLeader 10 0.85 0.23 2.09 0.15 1.46 0.35 2.69 0.44
NLeader 18 0.97 0.23 1.51 0.35 0.82 0.39 1.98 0.57
NLeader 19 1.33 0.24 1.22 0.23 0.51 0.41 1.88 0.53
NLeader 25 0.95 0.15 1.48 0.20 0.11 0.35 1.76 0.43
NLeader 26 0.76 0.21 1.04 0.20 0.22 0.40 1.30 0.50
NLeader 21 0.56 0.17 1.07 0.23 0.55 0.37 1.33 0.47
NLeader 8 0.06 0.23 1.04 0.24 0.69 0.48 1.24 0.58
NLeader 39 0.37 0.24 0.59 0.24 0.93 0.54 1.16 0.64
NLeader 49 0.47 0.28 0.04 0.39 0.37 0.79 0.60 0.92
NLeader 118 -0.17 0.13 0.44 0.14 0.32 0.28 0.57 0.34
NLeader 129 -1.66 0.22 -0.05 0.18 0.00 0.48 1.66 0.56
NLeader 25 -2.04 0.09 2.50 0.26 0.24 0.17 3.24 0.32
NLeader 121 -1.3 0.34 0.59 0.28 0.14 0.51 1.43 0.67
NLeader 169 -2.63 0.27 1.50 0.22 1.51 0.53 3.38 0.64
NLeader 175 -1.97 0.28 1.41 0.24 1.06 0.42 2.64 0.56
NLeader 157 -2.51 0.18 0.79 0.12 0.37 0.31 2.66 0.38
NLeader 154 -2.74 0.16 0.43 0.12 0.41 0.26 2.81 0.33
NLeader 162 -2.79 0.14 0.45 0.13 0.8 0.2 2.94 0.28
NLeader 23 -1.99 0.16 0.05 0.16 2.05 0.3 2.86 0.37
NLeader 27 -1.84 0.20 0.42 0.13 2.15 0.25 2.87 0.34
NLeader 32 -1.89 0.13 0.44 0.2 2.44 0.2 3.12 0.32
NLeader 27 -1.79 0.27 0.47 0.26 2.44 0.39 3.07 0.54
231

NLeader 17 -1.47 0.22 0.21 0.21 2.2 0.57 2.65 0.64
NLeader 8 -1.30 0.23 0.24 0.23 2.57 0.41 2.89 0.52
NLeader 27 -0.61 0.26 1.09 0.25 1.74 0.47 2.14 0.59
NLeader 42 0.33 0.19 0.59 0.19 1.18 0.34 1.36 0.43
NLeader 87 0.13 0.15 0.51 0.28 0.12 0.35 0.54 0.47
NLeader 70 0.55 0.12 0.33 0.36 0.07 0.34 0.65 0.51
NLeader 56 0.91 0.17 -0.19 0.26 0.53 0.37 1.07 0.49
NLeader 72 1.10 0.19 0.28 0.44 0.94 0.47 1.47 0.68
NLeader 57 1.01 0.19 1.41 0.28 1.66 0.49 2.40 0.59
NLeader 33 0.66 0.23 2.00 0.31 0.53 0.59 2.17 0.70
NLeader 30 0.24 0.27 2.40 0.3 0.67 0.58 2.50 0.70
NLeader 20 0.51 0.28 2.64 0.23 0.63 0.55 2.76 0.66
NLeader 52 1.88 0.23 1.48 0.29 0.49 0.44 2.45 0.57
NLeader 50 1.85 0.39 1.46 0.44 0.36 0.53 2.38 0.79
NLeader 51 1.5 0.28 0.77 0.34 0.44 0.48 1.74 0.65
NLeader 67 1.68 0.23 1.52 0.29 1.37 0.74 2.64 0.82
NLeader 57 0.9 0.32 0.9 0.29 0.12 0.43 1.28 0.61
NLeader 27 0.04 0.52 0.22 0.22 0.33 0.43 0.4 0.71
NLeader 175 -1.39 0.24 0.66 0.14 0.10 0.33 1.54 0.43
NLeader 72 1.39 0.17 4.35 0.2 2.06 0.32 5.01 0.41
NLeader 82 3.35 0.17 3.08 0.25 3.07 0.37 5.49 0.48
NLeader 108 0.95 0.13 -0.38 0.22 0.31 0.35 1.07 0.43
NLeader 104 0.59 0.11 -0.53 0.29 0.29 0.39 0.85 0.50
NLeader 42 0.38 0.14 -0.51 0.22 1.04 0.21 1.22 0.34
NLeader 46 0.75 0.09 -0.48 0.2 0.79 0.25 1.19 0.33
NLeader 42 -0.19 0.16 -0.53 0.17 0.24 0.34 0.61 0.41
Papatea 156 -6.44 0.23 4.66 0.46 2.29 0.83 8.27 0.98
Papatea 167 -5.87 0.25 5.12 0.33 4.81 0.98 9.15 1.07
Papatea 175 -4.7 0.3 5.45 0.52 3.36 0.93 7.94 1.11
Papatea 168 -5.41 0.31 4.98 0.45 5.18 1.00 8.99 1.14
Papatea 0 -4.36 0.4 5.49 0.38 5.32 1.36 8.8 1.47
Papatea 162 -6.29 0.42 4.34 0.32 5.17 1.37 9.22 1.47
Papatea 155 -7.27 0.31 3.26 0.45 6.99 1.19 10.6 1.31
Papatea 169 -6.37 0.29 3.73 0.31 6.44 1.08 9.8 1.16
Papatea 3 -4.7 0.32 4.03 0.31 6.71 1.15 9.13 1.24
Papatea 164 -5.7 0.34 2.47 0.34 7.38 1.00 9.65 1.11
Papatea 167 -5.39 0.39 2.00 0.28 7.43 1.71 9.4 1.77
Papatea 3 -4.03 0.26 3.30 0.39 8.81 0.88 10.23 0.99
Papatea 21 -2.07 0.16 4.35 0.29 7.76 0.71 9.13 0.78
Papatea 6 -2.68 0.21 3.76 0.31 8.1 0.70 9.32 0.79
Papatea 177 -3.15 0.26 5.44 0.27 9.04 0.63 11.01 0.73
Papatea 164 -4.11 0.47 2.25 1.20 1.56 2.06 4.94 2.43
232

Papatea 150 -4.54 0.26 2.16 0.48 7.74 0.88 9.22 1.04
Papatea 134 -4.61 0.23 0.1 0.30 6.7 0.78 8.13 0.87
Papatea 149 -4.19 0.19 1.18 0.22 6.84 0.87 8.11 0.92
Papatea 129 -4.15 0.18 0.14 0.17 7.87 0.68 8.9 0.72
Papatea 122 -3.69 0.15 -1.26 0.18 7.77 0.6 8.69 0.65
Papatea 122 -3 0.28 -0.92 0.18 6.82 0.76 7.5 0.83
Papatea 104 -3.06 0.37 -1.43 0.26 5.92 1.85 6.81 1.91
Papatea 98 -0.6 0.31 -3.62 0.34 1.35 2.9 3.91 2.94
Papatea 115 0.25 0.36 -3.34 0.4 1.82 3.08 3.82 3.13
SLeader 40 -0.2 0.16 0.20 0.10 0.21 0.22 0.35 0.29
SLeader 40 -0.36 0.16 0.53 0.09 0.1 0.33 0.65 0.38
SLeader 40 -0.64 0.17 0.69 0.10 0.46 0.28 1.05 0.34
SLeader 48 -0.38 0.17 1.02 0.12 1.26 0.31 1.66 0.38
SLeader 9 -0.68 0.16 0.82 0.13 0.46 0.34 1.16 0.4
SLeader 17 -0.53 0.14 0.91 0.13 0.00 0.3 1.05 0.35
SLeader 24 -0.45 0.18 1.44 0.15 0.34 0.34 1.55 0.41
SLeader 17 -2.56 0.2 2.35 0.27 1.36 0.55 3.74 0.64
SLeader 24 -2.37 0.24 1.38 0.25 2.72 0.58 3.86 0.68
SLeader 5 -2.29 0.23 0.19 0.27 3.43 0.41 4.13 0.54
SLeader 7 -2.25 0.21 -0.17 0.21 2.49 0.5 3.36 0.58
SLeader 22 -1.72 0.11 0.15 0.14 3.58 0.31 3.98 0.36
SLeader 156 -0.06 0.18 -0.73 0.2 1.09 0.28 1.31 0.38
SLeader 80 0.14 0.19 0.22 0.28 0.27 0.41 0.37 0.54
SLeader 90 -0.01 0.14 0.41 0.17 0.11 0.27 0.42 0.35
SLeader 84 -0.01 0.15 0.33 0.12 0.37 0.3 0.5 0.35
SLeader 84 -0.17 0.13 0.38 0.15 0.69 0.38 0.8 0.43
SLeader 61 -0.32 0.17 0.43 0.24 0.36 0.36 0.64 0.46
SLeader 61 -0.05 0.23 0.74 0.3 0.24 0.4 0.78 0.55
SLeader 36 -0.51 0.38 0.84 0.24 1.34 0.66 1.67 0.8
SLeader 68 -0.96 0.23 0.64 0.26 1.07 0.41 1.57 0.54
SLeader 106 -0.02 0.19 1.31 0.29 1.48 0.48 1.98 0.59
SLeader 32 -0.08 0.17 0.37 0.21 0.11 0.22 0.4 0.35
SLeader 35 -0.79 0.14 0.73 0.11 0.29 0.23 1.11 0.29
SLeader 43 -0.6 0.12 0.98 0.13 0.94 0.28 1.48 0.33
SLeader 33 -0.26 0.7 1.44 0.61 1.96 0.51 2.45 1.06
Snowflake
Spur
30 0.15 0.49 -1.36 0.83 0.10 0.9 1.37 1.32
Snowflake
Spur
10 0.36 0.31 -0.41 0.72 0.40 0.87 0.68 1.17
Snowflake
Spur
28 0.56 0.4 -0.54 0.46 0.09 0.87 0.79 1.06
233

Snowflake
Spur
27 1.08 0.43 -0.78 0.58 0.37 1.03 1.38 1.26
Snowflake
Spur
30 0.7 0.48 -0.52 0.59 0.58 0.98 1.05 1.24
Snowflake
Spur
35 0.88 0.52 0.41 0.64 0.29 1.11 1.01 1.38
Snowflake
Spur
30 0.49 0.36 -0.1 0.55 1.77 0.97 1.84 1.17
Snowflake
Spur
44 0.49 0.46 0.17 0.63 0.72 1.08 0.88 1.33
Snowflake
Spur
30 1.08 0.56 0.68 0.68 0.47 1.09 1.36 1.4
Snowflake
Spur
32 1.86 0.47 0.13 0.75 0.95 1.19 2.09 1.48
Snowflake
Spur
30 1.63 0.47 0.16 0.5 0.27 1.04 1.66 1.25
Snowflake
Spur
38 1.33 0.42 1.03 0.7 0.78 0.98 1.85 1.28
Snowflake
Spur
38 1.35 0.28 0.78 0.63 0.11 0.74 1.56 1.01
Snowflake
Spur
21 1.82 0.33 1.41 0.6 1.19 0.96 2.6 1.18
Snowflake
Spur
40 1.72 0.37 0.44 0.58 1.00 1.34 2.03 1.51
Snowflake
Spur
35 2.12 0.38 1.49 0.54 0.39 1.14 2.61 1.32
Snowflake
Spur
32 2.03 0.27 0.65 0.45 0.66 1.01 2.23 1.14
Snowflake
Spur
48 2.21 0.27 0.05 0.35 0.07 0.68 2.21 0.81
StoneJug 112 0.07 0.59 0.41 0.32 0.16 0.62 0.45 0.91
StoneJug 112 0.1 0.36 0.76 0.26 0.06 0.48 0.76 0.66
StoneJug 114 0.64 0.66 -0.93 0.3 0.1 0.92 1.14 1.17
StoneJug 131 -1.16 0.47 -1.04 0.42 1.28 0.96 2.02 1.14
StoneJug 141 -1.25 0.53 -0.18 0.58 0.19 1.35 1.28 1.57
StoneJug 154 -0.54 0.2 -0.48 0.44 0.54 1.04 0.91 1.15
StoneJug 161 -0.36 0.33 -0.61 0.58 1.12 1.54 1.32 1.68
StoneJug 155 -0.51 0.35 -0.55 0.47 0.00 1.01 0.75 1.16
StoneJug 159 -0.73 0.23 -0.37 0.44 1.31 1.06 1.54 1.17
StoneJug 174 -1.12 0.22 -0.11 0.31 1.33 0.98 1.75 1.06
StoneJug 175 -1.14 0.19 -0.18 0.35 0.44 1.07 1.24 1.14
StoneJug 165 -1.22 0.25 -0.68 0.42 0.76 1.14 1.59 1.24
234

StoneJug 164 -1.55 0.22 -0.37 0.51 0.27 0.88 1.62 1.04
StoneJug 167 -1.59 0.18 -0.71 0.42 0.59 0.98 1.84 1.08
StoneJug 172 -1.68 0.22 -0.11 0.37 0.47 0.96 1.75 1.05
StoneJug 6 -1.57 0.20 0.00 0.37 0.49 0.86 1.65 0.96
StoneJug 2 -1.68 0.20 0.25 0.43 0.85 1.11 1.9 1.21
StoneJug 170 -0.42 0.09 -0.11 0.24 0.67 0.45 0.8 0.52
StoneJug 156 -0.65 0.13 -0.71 0.24 0.59 0.63 1.13 0.69
StoneJug 154 -0.6 0.12 -0.45 0.3 1.57 0.72 1.74 0.79
StoneJug 153 -0.51 0.18 -0.84 0.34 0.71 0.84 1.21 0.93
StoneJug 135 0.08 0.08 -0.12 0.14 0.09 0.23 0.17 0.28
StoneJug 134 -0.1 0.12 -0.12 0.17 0.09 0.35 0.18 0.4
StoneJug 143 -0.1 0.07 -0.2 0.11 0.09 0.25 0.24 0.28
StoneJug 147 -0.24 0.09 0.03 0.08 0.05 0.19 0.25 0.22
StoneJug 164 -0.63 0.08 0.00 0.13 0.42 0.22 0.76 0.27
StoneJug 162 -1.19 0.07 0.02 0.09 0.71 0.14 1.39 0.18
StoneJug 160 -2.31 0.14 0.04 0.23 0.45 0.49 2.35 0.56
StoneJug 164 -2.31 0.25 -0.16 0.28 1.02 0.61 2.53 0.72
StoneJug 161 -2.2 0.2 -0.06 0.22 0.64 0.36 2.29 0.46
StoneJug 162 -2.14 0.41 0.19 0.22 0.6 0.59 2.23 0.75
StoneJug 149 -1.01 0.15 -0.13 0.19 0.89 0.31 1.35 0.39
StoneJug 168 -0.81 0.15 -0.42 0.18 0.95 0.33 1.32 0.4
StoneJug 159 -0.28 0.12 0.39 0.21 0.29 0.36 0.56 0.43
StoneJug 134 -0.11 0.26 -0.14 0.4 0.00 0.44 0.18 0.65
StoneJug 155 -0.78 0.22 0.17 0.37 0.33 0.43 0.87 0.6
StoneJug 149 -1.09 0.23 0.21 0.22 0.10 0.46 1.11 0.56
StoneJug 137 -1.48 0.20 0.38 0.21 0.90 0.37 1.78 0.47
StoneJug 128 -0.92 0.31 -0.22 0.23 0.52 0.43 1.08 0.58
StoneJug 117 -0.72 0.36 -0.24 0.23 0.13 0.39 0.77 0.58
StoneJug 101 -0.53 0.25 -0.14 0.24 0.01 0.40 0.54 0.53
StoneJug 103 -0.41 0.16 -0.05 0.17 0.01 0.31 0.41 0.39
StoneJug 120 -0.12 0.26 -0.43 0.22 0.21 0.48 0.50 0.59
StoneJug 132 -0.42 0.22 -0.02 0.20 0.6 0.46 0.73 0.54
StoneJug 142 -0.62 0.13 -0.10 0.14 0.17 0.35 0.65 0.4
StoneJug 156 -0.44 0.13 -0.18 0.14 0.33 0.26 0.58 0.32
StoneJug 173 -0.45 0.16 0.07 0.20 0.15 0.30 0.48 0.4
Tinline
Downs
43 1.49 0.25 0.91 0.24 3.23 0.39 3.67 0.52
Tinline
Downs
71 2.72 0.17 -1.25 0.47 3.41 0.55 4.54 0.75
Tinline
Downs
70 2.26 0.18 -0.33 0.28 3.3 0.43 4.01 0.54
235

Tinline
Downs
51 1.54 0.54 -0.12 0.44 1.95 1.28 2.49 1.45
Tinline
Downs
38 1.79 0.34 1.2 0.23 1.97 0.5 2.92 0.65
Tinline
Downs
50 1.7 0.14 0.45 0.16 1.6 0.34 2.38 0.4
Upper
Kowhai
66 0.54 0.47 -0.53 1.00 0.76 1.76 1.07 2.08
Upper
Kowhai
71 0.48 0.96 0.97 0.63 1.41 1.95 1.78 2.27
Upper
Kowhai
64 0.43 0.64 -0.01 0.84 1.72 1.86 1.77 2.14
Upper
Kowhai
50 0.07 0.5 -0.04 0.78 1.83 1.75 1.83 1.98
Upper
Kowhai
41 2.96 0.63 -1.98 1.03 1.91 1.99 4.04 2.33
Upper
Kowhai
61 0.87 0.62 -2.26 1 1.31 1.7 2.75 2.07
Upper
Kowhai
63 1.82 0.81 -2.59 0.75 1.46 2.4 3.48 2.64
Upper
Kowhai
73 2.17 0.5 -2.71 0.49 1.45 1.73 3.76 1.86
Upper
Kowhai
55 2.98 0.3 -1.27 0.29 2.39 1.08 4.03 1.16
Upper
Kowhai
65 2.6 0.36 -1.85 0.32 1.01 1.04 3.35 1.15
Upper
Kowhai
67 2.3 0.35 -1.07 0.35 2.53 1.04 3.59 1.15
Upper
Kowhai
63 3.14 0.28 -0.68 0.31 4.89 1.04 5.86 1.12
Upper
Kowhai
50 2.53 0.51 -0.55 0.44 3.69 1.74 4.51 1.87
Upper
Kowhai
68 3.36 0.5 -0.63 0.22 2.11 1.78 4.02 1.86
Upper
Kowhai
64 1.63 0.64 -0.46 0.54 3.7 1.54 4.07 1.75
Upper
Kowhai
47 3.55 0.56 -1.71 0.45 2.28 1.52 4.56 1.68
Upper
Kowhai
65 3.71 0.63 -2.38 0.28 1.13 1.65 4.55 1.79
Upper
Kowhai
37 4.57 0.25 -0.39 0.21 0.75 0.91 4.65 0.97
236

Upper
Kowhai
1 3.81 0.32 0.63 0.77 0.27 1.24 3.87 1.5
Waiautoa 173 -0.23 0.19 0.39 0.33 0.96 0.52 1.06 0.64
Waiautoa 168 -0.24 0.11 0 0.29 0.29 0.51 0.37 0.6
Waiautoa 157 0.12 0.15 0.23 0.29 0.22 0.8 0.34 0.86
Waiautoa 162 0.28 0.51 0.39 0.46 0.36 0.85 0.59 1.09
Wharekiri 165 -0.98 0.27 0.56 0.32 0.52 0.85 1.25 0.95
Wharekiri 157 -1.00 0.36 -0.14 0.39 1.26 0.96 1.62 1.09
Wharekiri 157 -1.23 0.33 -0.06 0.3 0.29 1.18 1.27 1.27
Wharekiri 171 -1.83 0.26 0.39 0.45 1.35 0.88 2.31 1.03
Wharekiri 169 -2.16 0.23 0.09 0.24 0.21 0.99 2.17 1.05
Wharekiri 141 -2.05 0.2 -0.77 0.21 0.75 1.00 2.32 1.04
Wharekiri 155 -1.46 0.2 -1.09 0.27 0.97 0.82 2.06 0.89
Wharekiri 167 -1.00 0.36 -0.26 0.23 0.07 1.21 1.04 1.28
Whites 8 -0.37 0.25 -0.63 0.68 0.46 0.67 0.86 0.99
Whites 169 -0.56 0.29 0.34 0.75 0.67 0.67 0.94 1.05
Whites 18 -0.42 0.24 1.00 0.8 0.54 0.71 1.22 1.1
Whites 163 -1.07 0.32 0.69 0.68 0.72 0.64 1.46 0.99
Whites 159 -1.77 0.39 0.3 0.68 0.72 0.75 1.93 1.09
Whites 4 -1.29 0.2 0.44 0.51 0.93 0.55 1.65 0.78
Whites 179 -1.57 0.21 0.08 0.49 0.51 0.6 1.65 0.8
Whites 3 -1.87 0.23 0.62 0.6 0.67 0.56 2.08 0.85
Whites 178 -1.81 0.25 0.13 0.56 0.63 0.8 1.92 1.01
Whites 7 -1.65 0.2 0.17 0.57 1.04 0.64 1.95 0.88
Whites 13 -1.87 0.24 0.52 0.69 1.04 0.75 2.2 1.04
Whites 16 -1.73 0.2 1.00 0.57 0.71 0.56 2.12 0.82
Whites 171 -1.75 0.17 0.00 0.66 1.00 0.61 2.01 0.91
Whites 4 -1.71 0.19 0.27 0.55 0.59 0.59 1.83 0.83
Whites 9 -1.41 0.43 0.26 0.93 1.03 1.07 1.76 1.48
Whites 17 -1.08 0.31 1.13 0.87 1.56 0.85 2.21 1.25
Whites 22 -1.15 0.24 0.3 0.58 0.88 0.62 1.48 0.88
Whites 4 -1.07 0.18 -0.16 0.74 0.54 0.62 1.21 0.99
Whites 177 -0.88 0.17 -0.02 0.85 0.18 0.67 0.9 1.1
Whites 175 -1.06 0.18 0.37 0.86 0.01 0.68 1.12 1.11
Whites 171 -0.45 0.38 0.03 1.22 0.44 1.07 0.63 1.67
a
Upar is fault-parallel displacement
b
Upar err is the 1-standard deviation error of fault-parallel deformation values
c
Uper is fault-perpendicular displacement
d
Uper err is the 1-standard deviation error of fault-perpendicular deformation values
e
Uver is vertical displacement
f
Uver err is the 1-standard deviation error of vertical deformation values
g
Unet is net deformation
h
Unet err is the 1-standard deviation error of net deformation values
237

Table S5.
Fault zone width measurements, collected every 500 m
Fault name Strike Measurement aperture
a
Fault zone width
b

ConwayCharwell 42 500 500
ConwayCharwell 48 500 500
ConwayCharwell 12 500 500
ConwayCharwell 45 250 NoData
ConwayCharwell 65 350 350
ConwayCharwell 55 250 250
ConwayCharwell 50 400 400
ConwayCharwell 70 450 450
ConwayCharwell 60 250 250
ConwayCharwell 58 300 300
ConwayCharwell 68 300 300
ConwayCharwell 57 250 250
ConwayCharwell 53 200 NoData
CornerHill 3 150 NoData
CornerHill 16 150 NoData
CornerHill 18 100 NoData
Fidget 89 150 150
Fidget 79 50 50
Fidget 65 300 300
Fidget 78 150 150
Fidget 88 100 100
Fidget 83 150 NoData
Fidget 75 200 NoData
Fidget 86 400 400
Fidget 93 350 350
Fidget 73 150 150
Fidget 64 450 450
Fidget 68 1850 NoData
Fidget 76 400 400
Fidget 78 850 NoData
Fidget 81 350 NoData
Fidget 83 750 750
Fidget 82 2150 NoData
Fidget 72 400 NoData
Fidget 69 400 NoData
Fidget 65 200 NoData
Fidget 60 150 NoData
Fidget 67 950 NoData
238

Fidget 71 350 NoData
Fidget 67 100 NoData
Fidget 65 500 NoData
Fidget 73 300 NoData
Fidget 77 400 NoData
Fidget 80 350 NoData
Fidget 79 250 NoData
Fidget 79 500 NoData
Fidget 85 700 NoData
Fidget 88 750 NoData
Fidget 93 350 NoData
Fidget 90 200 NoData
Fidget 93 150 NoData
HumpsE 94 350 NoData
HumpsE 75 300 NoData
HumpsE 86 150 NoData
HumpsE 71 400 NoData
HumpsE 60 750 750
HumpsE 97 700 NoData
HumpsE 91 300 300
HumpsE 95 350 NoData
HumpsE 79 350 350
HumpsE 58 400 400
HumpsE 48 250 250
HumpsE 75 300 NoData
HumpsE 68 400 NoData
HumpsE 72 250 NoData
HumpsE 78 200 NoData
HumpsE 78 500 NoData
HumpsE 63 1800 NoData
HumpsE 50 1550 NoData
HumpsE 64 1250 NoData
HumpsE 55 1100 1100
HumpsE 56 1300 NoData
HumpsE 69 1150 1150
HumpsE 94 1050 1050
HumpsE 97 500 500
HumpsE 80 350 350
HumpsE 67 420 420
HumpsE 66 550 550
HumpsE 62 50 NoData
HumpsE 54 550 NoData
239

HumpsE 48 900 NoData
HumpsE 54 450 450
HumpsE 55 400 400
HumpsE 50 550 550
HumpsE 53 350 350
HumpsE 65 250 250
HumpsE 50 150 150
HumpsE 48 250 250
HumpsE 64 0 NoData
HumpsE 59 1450 1450
HumpsE 61 350 350
HumpsE 68 250 250
HumpsE 75 650 650
HumpsE 73 1200 NoData
HumpsE 55 650 650
HumpsE 66 300 300
HumpsE 41 200 200
HumpsE 48 200 200
HumpsE 51 250 250
HumpsE 36 450 450
HumpsE 22 500 500
HumpsE 19 200 200
HumpsW 73 1000 1000
HumpsW 62 1000 1000
HumpsW 44 650 650
HumpsW 60 350 350
HumpsW 85 600 600
HumpsW 93 1050 NoData
HumpsW 90 850 NoData
HumpsW 91 1200 NoData
HumpsW 85 1100 NoData
HumpsW 97 850 NoData
HumpsW 75 650 NoData
HumpsW 82 800 NoData
HumpsW 95 550 550
HumpsW 92 250 250
HumpsW 89 1350 NoData
HumpsW 54 150 NoData
HumpsW 63 200 200
HumpsW 76 700 NoData
HumpsW 87 950 950
HumpsW 109 750 750
240

HumpsW 103 850 850
HumpsW 89 700 700
HumpsW 98 850 850
HumpsW 81 400 400
HumpsW 77 300 300
HumpsW 85 550 550
HumpsW 77 250 250
HumpsW 75 250 NoData
HumpsW 75 400 400
HumpsW 70 450 450
HumpsW 82 300 NoData
HumpsW 36 1200 NoData
HumpsW 69 450 NoData
HumpsW 73 1150 NoData
HumpsW 77 700 NoData
HumpsW 95 850 NoData
Hundalee 63 450 450
Hundalee 55 250 NoData
Hundalee 54 1100 1100
Hundalee 64 650 650
Hundalee 67 650 650
Hundalee 85 850 NoData
Hundalee 86 250 250
Hundalee 40 300 300
Hundalee 26 250 250
Hundalee 7 350 350
Hundalee 1 600 600
Hundalee 26 550 550
Hundalee 72 550 NoData
Hundalee 7 200 200
Hundalee 5 750 750
Hundalee 157 550 550
Hundalee 133 300 300
Hundalee 4 350 350
Hundalee 34 500 500
Hundalee 113 250 250
Hundalee 138 200 200
Hundalee 84 300 300
Hundalee 64 200 200
Hundalee 109 250 250
Hundalee 55 350 350
Hundalee 74 150 150
241

Hundalee 46 850 NoData
Hundalee 26 350 350
Hundalee 44 550 550
Hundalee 68 600 600
Hundalee 43 700 NoData
Hundalee 36 700 NoData
Hundalee 62 750 NoData
Hundalee 67 950 NoData
JordanThrust 29 300 300
JordanThrust 46 350 350
JordanThrust 41 250 250
JordanThrust 36 300 300
JordanThrust 31 300 300
JordanThrust 22 150 NoData
JordanThrust 29 300 300
JordanThrust 41 500 500
JordanThrust 39 450 NoData
JordanThrust 52 1550 NoData
JordanThrust 51 500 NoData
JordanThrust 29 400 NoData
JordanThrust 41 400 NoData
JordanThrust 33 400 NoData
JordanThrust 40 950 950
JordanThrust 31 900 NoData
JordanThrust 23 300 300
JordanThrust 33 200 200
JordanThrust 36 700 700
JordanThrust 32 300 300
JordanThrust 54 250 250
JordanThrust 49 300 300
JordanThrust 41 150 150
JordanThrust 44 700 700
JordanThrust 43 100 NoData
JordanThrust 54 200 NoData
JordanThrust 61 100 NoData
JordanThrust 63 100 NoData
JordanThrust 40 50 NoData
Kekerengu 74 100 NoData
Kekerengu 53 50 NoData
Kekerengu 44 50 NoData
Kekerengu 30 0 NoData
Kekerengu 45 400 NoData
242

Kekerengu 58 1300 NoData
Kekerengu 43 300 NoData
Kekerengu 38 50 NoData
Kekerengu 39 400 NoData
Kekerengu 50 150 NoData
Kekerengu 54 200 NoData
Kekerengu 43 250 NoData
Kekerengu 54 150 NoData
Kekerengu 52 150 NoData
Kekerengu 42 150 NoData
Kekerengu 45 500 NoData
Kekerengu 48 100 NoData
Kekerengu 49 600 NoData
Kekerengu 52 600 NoData
Kekerengu 53 600 NoData
Kekerengu 52 650 NoData
Kekerengu 48 700 NoData
Kekerengu 38 1000 NoData
Kekerengu 43 1050 NoData
Kekerengu 39 1100 NoData
Kekerengu 53 600 NoData
Kekerengu 63 600 NoData
Kekerengu 58 450 NoData
Kekerengu 56 2000 NoData
Kekerengu 39 2000 NoData
Kekerengu 42 1400 NoData
Kekerengu 61 500 500
Kekerengu 58 400 400
Kekerengu 54 100 100
Kekerengu 50 700 NoData
Kekerengu 58 300 300
Kekerengu 55 200 200
Kekerengu 58 100 100
Kekerengu 69 200 200
Kekerengu 62 150 150
Kekerengu 62 250 250
Kekerengu 65 200 200
Kekerengu 64 350 350
Kekerengu 65 350 350
Kekerengu 72 500 500
Kekerengu 77 450 450
Kekerengu 70 450 450
243

Kekerengu 71 400 400
Kekerengu 54 450 450
Kekerengu 72 100 NoData
Kekerengu 62 400 NoData
Kekerengu 72 250 NoData
Kekerengu 73 250 NoData
Manakau 49 350 350
Manakau 68 400 NoData
Manakau 46 300 300
Manakau 47 250 250
Manakau 42 350 350
Manakau 55 400 400
Manakau 76 700 NoData
Manakau 61 450 450
Manakau 85 300 NoData
Manakau 51 650 650
Manakau 67 200 200
Manakau 67 200 200
Manakau 41 250 250
Manakau 58 150 150
Manakau 76 200 200
Manakau 72 250 250
Manakau 46 900 NoData
Manakau 22 200 200
Manakau 60 200 200
Manakau 68 100 NoData
Manakau 64 100 NoData
Manakau 55 850 NoData
Manakau 37 1100 NoData
Manakau 53 150 NoData
Manakau 54 250 NoData
Manakau 35 200 NoData
Manakau 46 150 NoData
Manakau 51 250 NoData
Manakau 65 200 NoData
Manakau 92 150 NoData
NLeader 43 250 250
NLeader 65 200 200
NLeader 71 300 300
NLeader 62 200 200
NLeader 50 200 200
NLeader 51 450 450
244

NLeader 16 550 550
NLeader 23 600 600
NLeader 1 500 500
NLeader 29 350 350
NLeader 175 200 200
NLeader 41 900 NoData
NLeader 176 200 200
NLeader 129 450 450
NLeader 123 0 0
NLeader 147 400 400
NLeader 175 500 500
NLeader 157 400 400
NLeader 134 350 350
NLeader 83 400 400
NLeader 59 350 350
NLeader 69 650 650
NLeader 64 950 950
NLeader 50 450 450
NLeader 13 450 450
NLeader 10 150 150
NLeader 18 250 250
NLeader 19 500 500
NLeader 25 1100 1100
NLeader 26 600 NoData
NLeader 21 650 NoData
NLeader 8 400 400
NLeader 39 200 200
NLeader 49 100 100
NLeader 118 100 100
NLeader 129 400 400
NLeader 25 450 NoData
NLeader 121 900 NoData
NLeader 169 950 950
NLeader 175 500 500
NLeader 157 500 500
NLeader 154 700 700
NLeader 162 500 500
NLeader 23 500 500
NLeader 27 550 550
NLeader 32 950 NoData
NLeader 27 650 NoData
NLeader 17 400 400
245

NLeader 8 400 400
NLeader 27 100 100
NLeader 42 2100 NoData
NLeader 87 150 150
NLeader 70 300 300
NLeader 56 250 NoData
NLeader 72 400 400
NLeader 57 400 400
NLeader 33 200 200
NLeader 30 700 700
NLeader 20 550 550
NLeader 52 100 100
NLeader 50 450 NoData
NLeader 51 400 400
NLeader 67 900 900
NLeader 57 200 200
NLeader 27 300 NoData
NLeader 175 400 400
NLeader 72 1000 NoData
NLeader 82 1050 NoData
NLeader 108 250 250
NLeader 104 150 150
NLeader 42 100 100
NLeader 46 200 200
NLeader 42 250 250
Papatea 156 950 NoData
Papatea 167 750 NoData
Papatea 175 500 NoData
Papatea 168 450 NoData
Papatea 0 700 NoData
Papatea 162 1000 NoData
Papatea 155 600 NoData
Papatea 169 550 NoData
Papatea 3 850 NoData
Papatea 164 1600 NoData
Papatea 167 700 NoData
Papatea 3 350 NoData
Papatea 21 150 150
Papatea 6 150 150
Papatea 177 250 NoData
Papatea 164 0 NoData
Papatea 150 250 250
246

Papatea 134 350 350
Papatea 149 50 50
Papatea 129 700 NoData
Papatea 122 300 300
Papatea 122 250 250
Papatea 104 250 NoData
Papatea 98 600 NoData
Papatea 115 700 NoData
SLeader 40 200 200
SLeader 40 350 350
SLeader 40 450 450
SLeader 48 300 300
SLeader 9 500 500
SLeader 17 500 NoData
SLeader 24 350 350
SLeader 17 200 200
SLeader 24 400 400
SLeader 5 200 200
SLeader 7 400 400
SLeader 22 400 400
SLeader 156 150 150
SLeader 80 200 NoData
SLeader 90 300 NoData
SLeader 84 250 250
SLeader 84 300 300
SLeader 61 400 400
SLeader 61 50 NoData
SLeader 36 550 NoData
SLeader 68 300 NoData
SLeader 106 700 NoData
SLeader 32 50 NoData
SLeader 35 750 750
SLeader 43 600 600
SLeader 33 800 NoData
SnowflakeSpur 30 300 300
SnowflakeSpur 10 200 200
SnowflakeSpur 28 300 300
SnowflakeSpur 27 350 350
SnowflakeSpur 30 250 250
SnowflakeSpur 35 350 350
SnowflakeSpur 30 250 250
SnowflakeSpur 44 200 200
247

SnowflakeSpur 30 300 300
SnowflakeSpur 32 200 200
SnowflakeSpur 30 200 200
SnowflakeSpur 38 350 350
SnowflakeSpur 38 250 250
SnowflakeSpur 21 200 200
SnowflakeSpur 40 300 300
SnowflakeSpur 35 250 250
SnowflakeSpur 32 150 150
SnowflakeSpur 48 1300 NoData
StoneJug 112 250 NoData
StoneJug 112 100 NoData
StoneJug 114 550 NoData
StoneJug 131 550 NoData
StoneJug 141 750 NoData
StoneJug 154 700 NoData
StoneJug 161 700 NoData
StoneJug 155 1650 NoData
StoneJug 159 550 NoData
StoneJug 174 1200 NoData
StoneJug 175 800 NoData
StoneJug 165 700 700
StoneJug 164 600 600
StoneJug 167 400 400
StoneJug 172 350 350
StoneJug 6 300 300
StoneJug 2 400 400
StoneJug 170 200 200
StoneJug 156 550 550
StoneJug 154 450 450
StoneJug 153 650 650
StoneJug 135 150 150
StoneJug 134 50 50
StoneJug 143 200 200
StoneJug 147 150 150
StoneJug 164 200 200
StoneJug 162 100 100
StoneJug 160 300 300
StoneJug 164 300 300
StoneJug 161 400 400
StoneJug 162 1350 NoData
StoneJug 149 250 NoData
248

StoneJug 168 550 NoData
StoneJug 159 950 NoData
StoneJug 134 50 50
StoneJug 155 150 150
StoneJug 149 600 NoData
StoneJug 137 400 400
StoneJug 128 300 300
StoneJug 117 200 200
StoneJug 101 650 NoData
StoneJug 103 350 NoData
StoneJug 120 200 200
StoneJug 132 200 200
StoneJug 142 150 150
StoneJug 156 200 200
StoneJug 173 200 200
TinlineDowns 43 200 NoData
TinlineDowns 71 250 NoData
TinlineDowns 70 325 NoData
TinlineDowns 51 250 NoData
TinlineDowns 38 300 NoData
TinlineDowns 50 700 NoData
UpperKowhai 66 0 NoData
UpperKowhai 71 50 NoData
UpperKowhai 64 450 NoData
UpperKowhai 50 150 NoData
UpperKowhai 41 350 NoData
UpperKowhai 61 500 NoData
UpperKowhai 63 300 NoData
UpperKowhai 73 600 NoData
UpperKowhai 55 200 NoData
UpperKowhai 65 300 NoData
UpperKowhai 67 550 NoData
UpperKowhai 63 1150 NoData
UpperKowhai 50 200 NoData
UpperKowhai 68 1400 NoData
UpperKowhai 64 900 NoData
UpperKowhai 47 100 NoData
UpperKowhai 65 550 NoData
UpperKowhai 37 350 NoData
UpperKowhai 1 300 NoData
Waiautoa 173 350 NoData
Waiautoa 168 350 350
249

Waiautoa 157 250 250
Waiautoa 162 100 NoData
Wharekiri 165 300 300
Wharekiri 157 150 150
Wharekiri 157 200 NoData
Wharekiri 171 300 300
Wharekiri 169 450 450
Wharekiri 141 500 500
Wharekiri 155 750 NoData
Wharekiri 167 250 250
Whites 8 100 NoData
Whites 169 100 NoData
Whites 18 150 150
Whites 163 200 200
Whites 159 300 300
Whites 4 200 200
Whites 179 700 700
Whites 3 350 350
Whites 178 200 200
Whites 7 150 150
Whites 13 150 150
Whites 16 150 150
Whites 171 150 150
Whites 4 400 400
Whites 9 200 200
Whites 17 150 150
Whites 22 150 150
Whites 4 500 500
Whites 177 300 300
Whites 175 350 350
Whites 171 300 NoData
a
Fault-perpendicular width of “rollover” in our displacement profiles
b
Fault-perpendicular width across which tectonic displacement is accommodated

250

Table S6.
Displacement measurements along selected faults, collected at 200 m spacing
Fault Strike Upar
a
Upar err
b
Uper
c
Uper err
d
Uver
e
Uver err
f
Unet
g
Unet err
h

Jordan
Thrust
28 3.31 0.5 -0.82 0.8 1.49 1.94 3.72 2.15
Jordan
Thrust
28 3.87 0.65 -0.04 0.79 0.91 2.67 3.97 2.86
Jordan
Thrust
39 3.49 0.51 -0.1 0.52 2.09 2.33 4.08 2.44
Jordan
Thrust
46 3.65 0.63 -0.96 0.44 0.86 2.75 3.87 2.86
Jordan
Thrust
47 4.25 0.76 -0.68 0.54 0.75 3.43 4.37 3.56
Jordan
Thrust
45 3.37 1.1 1.86 1.69 2.22 2.8 4.44 3.45
Jordan
Thrust
39 3.92 0.66 0.5 0.42 1.17 2.54 4.13 2.66
Jordan
Thrust
33 4.03 0.6 0.26 0.47 0.05 2.29 4.04 2.42
Jordan
Thrust
37 3.66 0.47 3.7 0.84 1.96 2.18 5.56 2.38
Jordan
Thrust
36 3.81 0.41 0.59 0.38 0.12 1.86 3.86 1.94
Jordan
Thrust
28 2.76 0.46 0.23 0.46 0.52 2.11 2.82 2.21
Jordan
Thrust
35 3.92 0.48 -0.23 0.45 0.1 2.29 3.93 2.38
Jordan
Thrust
27 4.14 0.3 0.41 0.34 0.75 1.47 4.23 1.54
Jordan
Thrust
21 4.65 0.36 0.61 0.41 0.88 2.12 4.78 2.19
Jordan
Thrust
24 4.22 0.61 0.48 0.49 0.21 2.82 4.25 2.93
Jordan
Thrust
22 3.85 0.62 0.55 0.55 1.82 3.06 4.29 3.17
Jordan
Thrust
32 3.96 0.98 1.1 1.22 1.69 3.64 4.44 3.96
Jordan
Thrust
38 4.48 0.63 -0.15 0.68 1.44 2.71 4.71 2.86
Jordan
Thrust
49 4.38 0.68 -0.68 0.52 0.84 2.77 4.51 2.9
Jordan
Thrust
33 4.18 0.6 0.58 0.63 1.75 2.85 4.57 2.98
Jordan
Thrust
46 4.23 0.6 -0.45 0.45 0.64 2.55 4.31 2.66
Jordan 34 4.84 0.55 0.88 0.54 1.16 2.52 5.06 2.63
251

Thrust
Jordan
Thrust
48 3.86 0.83 0.39 0.73 0.24 2.19 3.89 2.46
Jordan
Thrust
57 4.81 0.65 -0.16 0.49 0.8 1.92 4.88 2.09
Jordan
Thrust
42 4.83 0.51 1.4 0.59 0.15 1.94 5.03 2.09
Jordan
Thrust
45 5.28 0.64 0.86 0.43 0.17 2.06 5.36 2.2
Jordan
Thrust
55 4.82 0.55 -0.95 0.35 0.04 2.32 4.92 2.41
Jordan
Thrust
60 4.57 0.78 -1.68 0.42 0.51 2.85 4.89 2.98
Jordan
Thrust
37 5.2 0.44 0.7 0.39 0.35 2.04 5.25 2.13
Jordan
Thrust
3 3.58 0.42 3.86 0.76 0.91 1.94 5.34 2.12
Jordan
Thrust
37 5.22 0.51 0.26 0.45 0.55 2.33 5.25 2.43
Jordan
Thrust
43 5.24 0.62 -0.27 0.43 0.59 2.62 5.28 2.73
Jordan
Thrust
35 5.11 0.49 0.45 0.48 1.6 2.43 5.38 2.53
Jordan
Thrust
38 4.74 0.43 0.12 0.35 2.66 1.94 5.44 2.02
Jordan
Thrust
34 4.84 0.39 0.44 0.45 1.15 1.87 5 1.97
Jordan
Thrust
25 5.11 0.51 0.82 0.51 0.15 2.81 5.18 2.9
Jordan
Thrust
49 5.32 0.49 -1.5 0.33 0.02 2.13 5.53 2.21
Jordan
Thrust
54 5.72 0.39 -2.09 0.3 0.08 1.79 6.09 1.86
Jordan
Thrust
36 6.2 0.36 -0.25 0.32 0.18 1.79 6.21 1.85
Jordan
Thrust
13 5.3 0.45 2.01 0.54 1.3 2.01 5.81 2.13
Jordan
Thrust
27 6.55 0.3 0.54 0.39 0.07 1.81 6.57 1.87
Jordan
Thrust
22 6.6 0.32 0.97 0.46 0.27 1.8 6.67 1.88
Jordan
Thrust
28 6.4 0.41 0.73 0.41 0.49 2.3 6.46 2.38
Jordan
Thrust
33 6.68 0.37 0.19 0.34 1.35 1.83 6.82 1.9
Jordan
Thrust
31 6.81 0.31 0.37 0.28 1.57 1.59 7 1.65
252

Jordan
Thrust
37 7.25 0.42 -0.35 0.39 2.16 1.85 7.58 1.94
Jordan
Thrust
39 7.89 0.45 -0.74 0.37 1.21 1.95 8.01 2.03
Jordan
Thrust
22 8 0.35 1.67 0.38 1.04 1.86 8.24 1.93
Jordan
Thrust
24 8.33 0.37 1.46 0.39 1.69 1.85 8.62 1.93
Jordan
Thrust
47 8.07 0.47 -1.57 0.27 3 2.16 8.75 2.23
Jordan
Thrust
45 8.38 0.43 -1.02 0.28 3.44 2.06 9.12 2.12
Jordan
Thrust
59 8.31 0.41 -2.91 0.28 2.65 1.56 9.19 1.63
Jordan
Thrust
59 8.22 0.41 -2.95 0.24 3.65 1.61 9.46 1.67
Jordan
Thrust
43 8.95 0.43 -0.76 0.36 3.19 1.49 9.53 1.59
Jordan
Thrust
51 8.32 0.4 -2.42 0.33 4.25 1.52 9.65 1.6
Jordan
Thrust
41 8.9 0.42 -0.79 0.3 4.5 1.19 10 1.3
Jordan
Thrust
37 9.36 0.45 -0.21 0.35 4.07 1.57 10.21 1.67
Jordan
Thrust
49 9.31 0.44 -2.19 0.35 4.71 1.8 10.66 1.89
Jordan
Thrust
47 9.93 0.39 -1.87 0.39 4.83 1.82 11.2 1.9
Jordan
Thrust
39 9.99 0.55 0.68 0.48 7.3 2.05 12.39 2.17
Jordan
Thrust
43 9.56 0.54 -0.17 0.32 7.14 2.06 11.93 2.15
Jordan
Thrust
43 9.19 0.45 -0.03 0.36 6.13 1.88 11.05 1.97
Jordan
Thrust
43 8.65 0.43 0.26 0.32 6.6 1.38 10.89 1.49
Jordan
Thrust
55 7.85 0.41 -1.75 0.29 6.18 2.06 10.14 2.12
Jordan
Thrust
58 7.7 0.38 -2.92 0.38 6.46 1.4 10.46 1.5
Jordan
Thrust
67 6.29 0.38 -2.81 0.23 4.21 1.62 8.07 1.68
Jordan
Thrust
63 5.55 0.23 -1.85 0.29 1.94 1.08 6.16 1.14
Jordan
Thrust
58 4.87 0.18 -1.84 0.21 3.39 0.75 6.21 0.8
Jordan 71 3.8 0.3 -1.98 0.27 2.23 0.56 4.82 0.69
253

Thrust
Jordan
Thrust
50 3.88 0.25 0.05 0.24 0.71 0.82 3.94 0.89
Jordan
Thrust
53 4.88 0.27 1.81 0.35 2.32 1.66 5.7 1.71
Jordan
Thrust
38 3.68 0.18 5.1 0.3 9.03 1.71 11.01 1.74
Jordan
Thrust
44 3.52 0.38 2.52 0.31 4.13 0.88 5.98 1.01
Jordan
Thrust
41 2.47 0.16 4.28 0.23 5.49 1.92 7.38 1.95
Jordan
Thrust
8 0.33 0.53 5.39 0.49 10.34 1.71 11.67 1.86
Jordan
Thrust
29 0.62 0.31 3.46 0.42 6.8 1.55 7.65 1.64
Jordan
Thrust
23 1.54 0.31 4 0.41 6.38 1.67 7.69 1.75
Kekerengu 82 1.52 0.31 -0.4 0.3 1.14 1.25 1.94 1.32
Kekerengu 69 1.72 0.38 -1.05 0.33 2.22 1.47 3 1.56
Kekerengu 63 2.56 0.38 -1.19 0.29 1.43 1.39 3.17 1.47
Kekerengu 54 2.49 0.54 -1.11 0.27 4.19 1.31 5 1.44
Kekerengu 49 2.55 0.24 0.11 0.29 1.68 1.44 3.06 1.49
Kekerengu 55 2.58 0.36 0.09 0.36 0.44 1.88 2.62 1.95
Kekerengu 44 1.62 0.26 -1.44 0.31 1.49 1.62 2.63 1.67
Kekerengu 27 2.48 0.27 -0.17 0.3 0.74 1.47 2.6 1.52
Kekerengu 36 2.19 0.36 -2.29 0.35 2.76 1.82 4.2 1.89
Kekerengu 23 5.24 0.36 1.24 0.32 2.74 1.86 6.04 1.92
Kekerengu 39 7.11 0.53 0.07 0.57 3.64 2.31 7.99 2.43
Kekerengu 47 8.93 0.49 0.95 0.57 1.94 2.29 9.19 2.41
Kekerengu 63 8.19 0.64 -0.68 0.44 1.89 1.54 8.43 1.72
Kekerengu 58 8.81 0.62 -0.07 0.49 1.14 1.24 8.88 1.47
Kekerengu 51 8.17 0.77 1.58 0.67 0.8 1.74 8.36 2.02
Kekerengu 46 8.96 0.62 2.71 0.63 0.98 1.66 9.42 1.88
Kekerengu 42 8.67 0.6 3.55 0.67 1.33 1.82 9.46 2.04
Kekerengu 36 8.57 0.71 3.88 0.73 2.24 1.92 9.67 2.17
Kekerengu 38 8.42 0.55 4.06 0.56 2.08 1.79 9.57 1.95
Kekerengu 41 8.76 0.62 3.66 0.41 3.05 1.46 9.97 1.64
Kekerengu 39 8.84 0.48 4.11 0.45 4.05 1.57 10.56 1.7
Kekerengu 40 8.69 0.46 3.85 0.51 2.64 2.1 9.87 2.21
Kekerengu 37 8.57 0.43 4.09 0.78 3.74 2 10.21 2.19
Kekerengu 48 9.18 0.47 2.42 0.6 3.27 1.66 10.04 1.82
Kekerengu 57 9.32 0.44 1.25 0.44 3.5 1.9 10.03 2
Kekerengu 54 9.11 0.71 1.69 0.51 3.88 1.77 10.05 1.98
Kekerengu 53 9.26 0.59 2.03 0.47 2.93 1.89 9.92 2.04
254

Kekerengu 55 9.39 0.45 1.57 0.43 3.02 1.21 9.99 1.36
Kekerengu 45 9.33 0.41 2.86 0.53 2.75 1.37 10.13 1.52
Kekerengu 35 8.74 0.37 4.14 0.61 2.77 1.42 10.06 1.59
Kekerengu 46 9.38 0.41 2.22 0.61 3.29 1.96 10.18 2.09
Kekerengu 59 9.75 0.44 0.3 0.48 3.37 1.47 10.32 1.6
Kekerengu 55 9.58 0.5 0.93 0.5 3.03 1.54 10.09 1.7
Kekerengu 65 10.15 0.66 -0.64 0.52 2.58 1.72 10.49 1.92
Kekerengu 38 9.08 0.33 4.15 0.41 2.26 1.27 10.23 1.37
Kekerengu 40 9.54 0.36 3.78 0.43 3.02 1.46 10.69 1.56
Kekerengu 41 10 0.5 3.83 0.57 3.44 1.68 11.25 1.84
Kekerengu 43 10.54 0.57 3.27 0.67 3.66 1.93 11.63 2.12
Kekerengu 45 10.34 0.54 2.55 0.63 3.42 1.86 11.19 2.03
Kekerengu 49 10.49 0.41 1.86 0.68 3.2 1.24 11.12 1.47
Kekerengu 49 10.06 0.52 2.02 0.59 2.55 1.3 10.57 1.52
Kekerengu 46 10.08 0.39 3.01 0.52 2.81 0.98 10.89 1.18
Kekerengu 51 10.14 0.43 2.51 0.48 2.26 1.13 10.69 1.3
Kekerengu 50 10.37 0.47 2.39 0.5 2.18 1.39 10.86 1.55
Kekerengu 45 10.61 0.41 3.31 0.48 2.11 1.36 11.31 1.5
Kekerengu 47 11.22 0.4 3.01 0.42 2.43 1.29 11.87 1.41
Kekerengu 59 11.2 0.45 0.18 0.57 1.76 1.06 11.34 1.29
Kekerengu 39 10.49 0.36 3.66 0.47 1.74 1.24 11.24 1.37
Kekerengu 56 10.95 0.37 0.1 0.45 0.89 1.31 10.99 1.43
Kekerengu 60 10.87 0.37 -0.96 0.4 0.56 1.12 10.93 1.25
Kekerengu 59 11.17 0.31 -0.22 0.48 1.62 0.92 11.29 1.08
Kekerengu 47 10.93 0.36 1.38 0.44 1.6 0.94 11.13 1.1
Kekerengu 47 11.04 0.42 1.11 0.57 0.82 1.29 11.12 1.47
Kekerengu 49 10.67 0.36 0.85 0.51 0.34 1.35 10.71 1.48
Kekerengu 49 10.9 0.37 1.23 0.75 0.69 1.34 10.99 1.58
Kekerengu 49 10.66 0.65 0.64 0.71 0.17 1.25 10.68 1.58
Kekerengu 35 9.98 0.26 4.02 0.4 1.61 0.95 10.88 1.07
Kekerengu 24 9.11 0.22 6.14 0.25 1.36 0.73 11.07 0.81
Kekerengu 40 10.64 0.46 2.69 0.57 1.17 1.39 11.03 1.57
Kekerengu 55 10.71 0.44 -0.36 0.47 1.43 1.18 10.81 1.34
Kekerengu 51 10.47 0.49 0.09 0.8 2.19 1.33 10.7 1.63
Kekerengu 32 10.25 0.3 4.53 0.48 2.72 1.16 11.53 1.29
Kekerengu 31 9.89 0.31 4.17 0.38 2.12 1.02 10.94 1.13
Kekerengu 53 11.07 0.52 0.18 0.71 3.96 1.64 11.76 1.86
Kekerengu 64 10.08 0.55 -1.95 0.61 2.56 1.48 10.58 1.69
Kekerengu 59 10.51 0.36 -0.82 0.33 3.86 0.84 11.23 0.97
Kekerengu 66 10.06 0.28 -2.28 0.34 3.99 0.79 11.06 0.91
Kekerengu 58 10.29 0.37 -0.79 0.42 4.14 1 11.12 1.15
Kekerengu 56 10.39 0.35 -0.64 0.46 3.37 1.21 10.94 1.34
255

Kekerengu 63 10.53 0.4 -2.23 0.62 4.46 1.43 11.65 1.61
Kekerengu 51 10.51 0.46 0.12 0.71 3.52 1.65 11.08 1.85
Kekerengu 61 10.79 0.33 -0.37 0.33 3.26 1.02 11.28 1.12
Kekerengu 54 10.92 0.3 -0.27 0.33 3.85 0.75 11.59 0.88
Kekerengu 56 11.3 0.34 -0.3 0.53 1.18 1.44 11.36 1.57
Kekerengu 16 9.49 0.5 4.58 0.77 1.56 1.63 10.65 1.87
Kekerengu 23 10.19 0.48 3.26 0.73 2.93 0.89 11.09 1.24
Kekerengu 54 11.72 0.41 -0.62 0.57 0.95 1.41 11.77 1.57
Kekerengu 58 11.19 0.62 -1.73 0.53 1.54 1.29 11.43 1.53
Kekerengu 58 10.82 0.4 -1.33 0.5 0.71 1.23 10.92 1.38
Kekerengu 63 10.65 0.37 -2.04 0.52 0.55 1.25 10.86 1.41
Kekerengu 47 11.12 0.39 1.08 0.39 0.79 1.24 11.2 1.36
Kekerengu 68 10.77 0.38 -3.13 0.56 0.73 1.47 11.24 1.62
Kekerengu 52 11.01 0.51 0 0.53 0.25 1.37 11.01 1.56
Kekerengu 55 10.75 0.51 -0.42 0.54 0.45 1.38 10.77 1.57
Kekerengu 59 10.97 0.36 -1.2 0.52 0.41 1.34 11.05 1.48
Kekerengu 44 10.98 0.57 1.46 0.62 0.9 1.35 11.11 1.59
Kekerengu 47 11.24 0.53 0.68 0.67 0.36 1.84 11.26 2.02
Kekerengu 61 10.77 0.39 -1.81 0.51 0.39 1.55 10.93 1.68
Kekerengu 63 10.5 0.37 -1.7 0.69 0.41 1.71 10.64 1.89
Kekerengu 58 10.48 0.37 -1.01 0.45 0.6 1.13 10.54 1.27
Kekerengu 55 10.43 0.43 0.06 0.55 0.01 1.54 10.43 1.69
Kekerengu 59 10.55 0.42 -0.53 0.6 0.98 1.35 10.61 1.53
Kekerengu 52 10.3 0.38 0.7 0.46 1.26 1.37 10.4 1.5
Kekerengu 61 10.55 0.43 -0.59 0.59 1.65 1.66 10.69 1.82
Kekerengu 55 10.62 0.44 1.04 0.53 1.31 1.56 10.75 1.71
Kekerengu 78 10.1 0.31 -3.27 0.59 1.06 1.49 10.66 1.63
Kekerengu 57 10.94 0.31 1.08 0.43 1.91 1.05 11.16 1.18
Kekerengu 69 10.39 0.29 -1.33 0.49 1.61 1.29 10.6 1.41
Kekerengu 63 10.81 0.35 -0.19 0.46 1.48 1.06 10.92 1.2
Kekerengu 60 10.52 0.36 0.64 0.53 1.25 1.45 10.62 1.59
Kekerengu 65 10.28 0.39 -0.4 0.61 1.34 1.57 10.37 1.73
Kekerengu 54 10.13 0.36 2.71 0.57 0.49 1.11 10.5 1.3
Kekerengu 79 10.77 0.31 -3.12 0.64 1.89 1.66 11.37 1.8
Kekerengu 56 10.61 0.41 1.26 0.57 1.22 1.21 10.76 1.39
Kekerengu 63 10.77 0.4 0.55 0.67 1.49 1.37 10.89 1.57
Kekerengu 53 10.27 0.51 2.43 0.78 1.32 1.52 10.64 1.78
Kekerengu 73 10.4 0.31 -1.27 0.65 2.79 1.61 10.84 1.76
Kekerengu 67 10.34 0.37 0.59 0.6 2.51 1.01 10.65 1.23
Kekerengu 65 9.98 0.4 0.94 0.39 1.41 0.93 10.12 1.09
Kekerengu 63 9.93 0.35 1.79 0.43 1.05 0.66 10.14 0.86
Kekerengu 66 10.15 0.41 1.37 0.4 1.09 0.74 10.3 0.94
256

Kekerengu 73 10.92 0.43 -0.26 0.51 1.24 1.06 11 1.25
Kekerengu 85 10.3 0.38 -2.55 0.44 1.04 1.21 10.66 1.35
Kekerengu 88 10.15 0.26 -3.18 0.51 1.43 1.29 10.73 1.41
Kekerengu 62 10.47 0.39 1.58 0.48 1.51 1.23 10.7 1.37
Kekerengu 59 10.36 0.35 1.77 0.51 2.31 1.3 10.76 1.44
Kekerengu 85 10.06 0.24 -2.67 0.37 1.42 0.64 10.51 0.78
Kekerengu 70 10.35 0.41 0.05 0.34 0.92 0.83 10.39 0.99
Kekerengu 66 11.05 0.41 0.85 0.4 1.69 0.99 11.21 1.15
Kekerengu 73 11.48 0.35 -0.64 0.31 2.05 0.6 11.68 0.76
Kekerengu 58 10.73 0.38 2.46 0.36 1.83 0.8 11.16 0.95
Kekerengu 53 9.69 0.37 3.68 0.38 1.42 0.76 10.46 0.93
Kekerengu 71 10.3 0.23 0.46 0.33 1.83 0.69 10.47 0.8
Kekerengu 77 8.43 0.13 -0.61 0.23 2.74 0.5 8.89 0.57
Kekerengu 55 7.69 0.2 2.51 0.18 2.53 0.62 8.48 0.68
Kekerengu 49 7.98 0.49 1.57 0.44 0.23 1.26 8.14 1.42
Kekerengu 70 7.29 0.2 -0.57 0.35 1.44 0.3 7.45 0.51
Kekerengu 58 7.46 0.15 1.52 0.14 1.53 0.55 7.76 0.58
Kekerengu 81 7.45 0.1 -1.03 0.17 0.27 0.36 7.53 0.41
Kekerengu 77 7.31 0.12 -0.19 0.21 1.48 0.52 7.46 0.57
Kekerengu 77 8.3 0.14 -3.92 0.3 3.14 1.12 9.7 1.17
Kekerengu 68 7.34 0.36 -1.27 1.11 0.78 3.07 7.49 3.28
Kekerengu 80 6.62 0.16 0.18 0.51 0.09 0.6 6.63 0.8
Kekerengu 85 7.16 0.09 -1.56 0.09 0.74 0.42 7.37 0.44
Papatea 143 -7 0.45 1.08 0.49 2.36 1.45 7.46 1.59
Papatea 163 -4.36 0.27 3.27 0.55 1.33 1.4 5.61 1.53
Papatea 172 -4.34 0.33 4.72 0.48 2.4 1.3 6.85 1.42
Papatea 174 -4.83 0.34 6.5 0.4 2.38 1.34 8.44 1.44
Papatea 158 -6.75 0.57 3.56 0.51 1.91 1.69 7.87 1.85
Papatea 160 -6.2 0.78 3.74 0.5 2.07 1.41 7.53 1.69
Papatea 9 -3.28 0.43 6.19 0.45 1.49 2.14 7.16 2.23
Papatea 166 -5.06 0.53 5.04 0.57 3.42 1.42 7.92 1.62
Papatea 166 -5.35 0.45 4.63 0.53 5.36 1.34 8.88 1.51
Papatea 175 -5.21 0.5 5.38 0.53 3.64 1.89 8.33 2.03
Papatea 175 -5.14 0.6 5.25 0.55 4.61 2.2 8.68 2.35
Papatea 3 -3.8 0.48 5.49 0.73 2.99 1.6 7.32 1.82
Papatea 180 -4.55 0.42 5.59 0.65 4.81 1.65 8.66 1.82
Papatea 171 -5.24 0.5 5.08 0.55 4.58 1.81 8.61 1.96
Papatea 147 -7.37 0.38 2.46 0.42 4.91 1.78 9.2 1.86
Papatea 160 -7 0.44 4 0.5 4.96 1.98 9.47 2.08
Papatea 149 -7.08 0.45 1.96 0.46 5.49 1.81 9.17 1.92
Papatea 157 -6.99 0.51 2.8 0.48 5.5 1.84 9.32 1.97
Papatea 169 -6.84 0.42 3.19 0.49 6.87 1.23 10.2 1.39
257

Papatea 175 -5.73 0.36 4.44 0.5 5.83 1.63 9.3 1.74
Papatea 3 -4.97 0.43 4.39 0.51 5.71 1.93 8.75 2.04
Papatea 2 -4.83 0.47 3.88 0.43 6.06 1.51 8.67 1.64
Papatea 179 -4.2 0.41 4.32 0.32 7.63 1.89 9.72 1.96
Papatea 159 -5.39 0.35 1.13 0.41 6.13 1.59 8.24 1.68
Papatea 162 -6.2 0.44 2.6 0.41 7.47 1.24 10.05 1.38
Papatea 162 -5.44 0.74 1.65 0.52 3.08 2 6.47 2.2
Papatea 169 -5.42 0.45 2.13 0.5 8.87 2.39 10.61 2.48
Papatea 177 -4.9 0.35 2.26 0.49 8.9 1.32 10.41 1.45
Papatea 6 -3.88 0.29 3.16 0.65 8.93 1.34 10.23 1.52
Papatea 3 -3.67 0.42 3.56 0.53 8.6 1.27 10 1.44
Papatea 10 -2.97 0.37 4.08 0.5 8.03 1.41 9.49 1.55
Papatea 26 -1.95 0.24 4.31 0.3 7.79 1.08 9.12 1.14
Papatea 25 -1.44 0.26 4.3 0.4 7.92 1.29 9.13 1.38
Papatea 3 -2.74 0.37 3.41 0.52 7.07 1.51 8.31 1.64
Papatea 2 -2.69 0.36 3.69 0.48 8.6 1.32 9.74 1.45
Papatea 3 -2.14 0.46 3.62 1.06 8.92 1.43 9.86 1.84
Papatea 179 -4.14 0.38 4.72 0.58 9.31 0.96 11.23 1.19
Papatea 163 -4.72 0.5 2.87 0.56 8.75 1 10.35 1.25
Papatea 165 -4.33 0.33 3.07 0.48 7.22 0.89 8.96 1.07
Papatea 164 -3.99 0.39 3.13 0.99 8.61 1.41 9.99 1.77
Papatea 143 -4.96 0.29 1.41 0.95 8.38 1.18 9.84 1.55
Papatea 161 -4.27 0.39 2.58 0.27 8.01 1.38 9.44 1.46
Papatea 136 -4.94 0.4 0.4 0.55 7.45 1.36 8.95 1.52
Papatea 138 -4.71 0.35 0.11 0.33 6.63 1.28 8.13 1.37
Papatea 130 -3.99 0.51 -0.7 0.54 6.43 1.63 7.6 1.79
Papatea 159 -4.06 0.33 1.5 0.49 6.24 1.57 7.59 1.68
Papatea 143 -3.99 0.24 0.73 0.26 6.75 1.07 7.88 1.12
Papatea 144 -4.59 0.41 1.03 0.43 6.88 2.12 8.33 2.2
Papatea 127 -4.78 0.41 -0.41 0.41 7.7 2.02 9.07 2.1
Papatea 124 -4.23 0.32 -0.76 0.31 6.47 1.13 7.76 1.22
Papatea 125 -4.19 0.32 -0.87 0.28 7.56 0.97 8.69 1.06
Papatea 115 -3.37 0.32 -1.45 0.25 7.35 0.98 8.22 1.06
Papatea 127 -3.66 0.31 -0.96 0.28 7.39 1.06 8.3 1.14
Papatea 124 -3.13 0.42 -0.6 0.29 7.81 0.92 8.44 1.05
Papatea 119 -3.47 0.71 -0.87 0.54 5.9 1.94 6.9 2.13
Papatea 105 -3.33 0.88 -1.68 0.48 5.4 1.58 6.56 1.87
Papatea 110 -2.49 0.42 -1.28 0.36 0.72 1.2 2.89 1.32
Papatea 90 -1.33 0.4 -3.53 0.26 2.58 1.1 4.57 1.2
Papatea 92 -0.47 0.46 -3.03 0.27 2.9 0.84 4.23 1
Papatea 110 -2.93 0.39 -3.03 0.41 2.19 1.28 4.75 1.4
Papatea 112 -0.3 0.11 -3.08 0.08 1.87 1.53 3.61 1.54
258

Papatea 117 -0.25 0.2 -3.15 0.24 3.65 1.48 4.83 1.51
Tinline
Downs
36 0.59 0.41 1.67 0.44 1.69 1.18 2.45 1.32
Tinline
Downs
47 1.28 0.34 1.4 0.31 1.97 0.58 2.73 0.74
Tinline
Downs
61 1.87 0.16 0.68 0.28 2.89 0.52 3.5 0.61
Tinline
Downs
78 2.27 0.24 -0.12 0.58 1.45 1.31 2.69 1.45
Tinline
Downs
64 2.56 0.24 0.48 0.43 2.98 0.82 3.96 0.95
Tinline
Downs
67 2.58 0.26 -1.02 0.45 1.96 0.72 3.4 0.89
Tinline
Downs
68 2.29 0.24 -0.1 0.34 3.46 0.68 4.15 0.79
Tinline
Downs
75 2.34 0.27 -0.69 0.33 2.91 0.66 3.8 0.79
Tinline
Downs
51 1.36 0.56 -0.23 0.5 2.33 0.69 2.71 1.02
Tinline
Downs
42 0.99 0.81 1.77 0.68 1.31 1.47 2.42 1.81
Tinline
Downs
40 2.22 0.39 0.62 0.3 1.47 0.6 2.73 0.78
Tinline
Downs
38 1.92 0.33 1.3 0.25 2.8 0.57 3.64 0.7
Tinline
Downs
44 1.9 0.41 0.9 0.3 2.52 0.57 3.28 0.77
Tinline
Downs
62 1.83 0.3 -0.18 0.39 2.36 0.75 2.99 0.9
Tinline
Downs
35 1.48 0.35 0.36 0.17 0.95 0.53 1.79 0.66
Upper
Kowhai
65 0.35 0.74 0.55 1.01 1.16 2.63 1.33 2.91
Upper
Kowhai
67 -0.74 0.65 -2.06 0.96 2.51 2.43 3.33 2.69
Upper
Kowhai
83 -0.27 1.16 -2.36 0.82 0.19 2.59 2.38 2.95
Upper
Kowhai
66 2.54 0.98 0.61 1.09 5.55 3.46 6.13 3.75
Upper
Kowhai
64 0.94 1.17 -3.37 0.87 0.33 4.15 3.52 4.4
Upper
Kowhai
63 2.38 0.88 -2.93 0.98 2.83 2.95 4.72 3.23
Upper
Kowhai
66 3.45 0.73 -2.65 0.89 0.69 2.74 4.41 2.97
Upper
Kowhai
79 2.38 1.04 -3.58 0.6 0.64 3.49 4.35 3.69
259

Upper
Kowhai
71 1.98 0.75 -1.89 0.64 1.62 2.04 3.18 2.27
Upper
Kowhai
68 2.55 0.72 -1.79 0.37 2.3 2.32 3.87 2.45
Upper
Kowhai
44 3.3 0.48 -0.53 0.43 1.97 1.79 3.88 1.9
Upper
Kowhai
56 3.31 0.55 -1.37 0.4 1.16 2.02 3.77 2.13
Upper
Kowhai
65 3 0.55 -1.75 0.5 0.6 2.05 3.53 2.18
Upper
Kowhai
66 2.77 0.6 -1.82 0.51 1.2 2.21 3.53 2.35
Upper
Kowhai
70 3.02 0.61 -2.66 0.68 0.23 1.75 4.03 1.97
Upper
Kowhai
62 2.21 0.75 -1.6 0.62 2.56 2.41 3.74 2.6
Upper
Kowhai
68 2.75 0.57 1.63 0.4 3.06 1.91 4.43 2.03
Upper
Kowhai
61 2.5 0.63 0.97 0.7 4.21 2.26 4.99 2.45
Upper
Kowhai
63 3.26 0.53 1.55 0.72 4.94 1.84 6.12 2.04
Upper
Kowhai
37 2.13 0.67 1.6 1.09 5.14 2.83 5.79 3.11
Upper
Kowhai
56 3.41 0.56 -0.52 0.41 2.33 2.25 4.16 2.36
Upper
Kowhai
67 3.24 0.59 -0.51 0.56 0.32 2.08 3.3 2.23
Upper
Kowhai
78 3.12 0.59 2.1 0.61 1.8 1.84 4.17 2.03
Upper
Kowhai
50 1.99 0.79 1.68 1.14 5.34 2.96 5.94 3.27
Upper
Kowhai
77 0.99 0.8 -3.83 0.93 2.29 3.3 4.57 3.52
Upper
Kowhai
44 2.26 0.76 -2.05 0.71 3.63 2.86 4.74 3.04
Upper
Kowhai
32 2.86 1 -1.04 0.8 4.23 3.75 5.22 3.96
Upper
Kowhai
59 2.55 0.72 -1.74 0.53 3.5 2.2 4.66 2.38
Upper
Kowhai
61 2.92 0.61 -2.63 0.38 1.38 2.32 4.16 2.43
Upper
Kowhai
35 3.76 0.64 -0.93 0.54 1.77 2.45 4.26 2.59
Upper
Kowhai
56 3.17 1.04 -1.94 0.61 2.75 3.41 4.62 3.62
Upper 58 3.67 0.58 -1.57 0.47 1.23 2.49 4.18 2.6
260

Kowhai
Upper
Kowhai
71 4.42 0.71 -2.08 0.47 0.86 2.24 4.96 2.39
Upper
Kowhai
42 4.34 0.49 -1.47 0.52 1.23 1.72 4.74 1.86
Upper
Kowhai
35 4.39 0.55 -0.83 0.57 0.09 1.94 4.47 2.09
Upper
Kowhai
23 5.36 0.61 0.24 0.46 0.16 2.41 5.37 2.53
Upper
Kowhai
164 4.04 0.39 1.91 0.72 0.23 1.44 4.47 1.66
Upper
Kowhai
11 4.25 0.51 -0.4 1.11 0.32 2.21 4.28 2.52
Upper
Kowhai
1 4.64 0.31 1.29 0.65 0.9 1.4 4.9 1.57
a
Upar is fault-parallel displacement
b
Upar err is the 1-standard deviation error of fault-parallel deformation values
c
Uper is fault-perpendicular displacement
d
Uper err is the 1-standard deviation error of fault-perpendicular deformation values
e
Uver is vertical displacement
f
Uver err is the 1-standard deviation error of vertical deformation values
g
Unet is net deformation
h
Unet err is the 1-standard deviation error of net deformation values

261

Table S7.
Fault zone width measurements, collected every 200 m
Fault Strike Measurement aperture
a
Fault zone width
b

JordanThrust 28 300 NoData
JordanThrust 28 600 NoData
JordanThrust 39 650 NoData
JordanThrust 46 700 NoData
JordanThrust 47 650 NoData
JordanThrust 45 200 NoData
JordanThrust 39 200 NoData
JordanThrust 33 300 NoData
JordanThrust 37 300 NoData
JordanThrust 36 350 350
JordanThrust 28 250 NoData
JordanThrust 35 300 NoData
JordanThrust 27 150 NoData
JordanThrust 21 200 200
JordanThrust 24 200 200
JordanThrust 22 100 100
JordanThrust 32 350 NoData
JordanThrust 38 400 NoData
JordanThrust 49 100 100
JordanThrust 33 250 250
JordanThrust 46 250 250
JordanThrust 34 300 300
JordanThrust 48 200 200
JordanThrust 57 200 NoData
JordanThrust 42 400 NoData
JordanThrust 45 400 NoData
JordanThrust 55 350 350
JordanThrust 60 300 300
JordanThrust 37 450 450
JordanThrust 3 300 300
JordanThrust 37 300 300
JordanThrust 43 250 250
JordanThrust 35 200 200
JordanThrust 38 100 100
JordanThrust 34 100 100
JordanThrust 25 350 350
JordanThrust 49 550 550
JordanThrust 54 500 500
JordanThrust 36 500 500
262

JordanThrust 13 200 200
JordanThrust 27 350 350
JordanThrust 22 350 350
JordanThrust 28 250 250
JordanThrust 33 150 150
JordanThrust 31 150 150
JordanThrust 37 200 200
JordanThrust 39 250 250
JordanThrust 22 200 200
JordanThrust 24 200 200
JordanThrust 47 50 50
JordanThrust 45 50 50
JordanThrust 59 100 100
JordanThrust 59 200 200
JordanThrust 43 50 50
JordanThrust 51 250 250
JordanThrust 41 150 150
JordanThrust 37 50 50
JordanThrust 49 150 150
JordanThrust 47 250 250
JordanThrust 39 150 150
JordanThrust 43 150 150
JordanThrust 43 100 100
JordanThrust 43 300 300
JordanThrust 55 250 250
JordanThrust 58 300 300
JordanThrust 67 150 150
JordanThrust 63 100 100
JordanThrust 58 100 100
JordanThrust 71 100 100
JordanThrust 50 100 100
JordanThrust 53 150 150
JordanThrust 38 200 200
JordanThrust 44 150 150
JordanThrust 41 150 150
JordanThrust 8 200 NoData
JordanThrust 29 100 NoData
JordanThrust 23 50 NoData
Kekerengu 82 50 NoData
Kekerengu 69 0 NoData
Kekerengu 63 50 NoData
Kekerengu 54 50 NoData
263

Kekerengu 49 50 NoData
Kekerengu 55 100 NoData
Kekerengu 44 50 NoData
Kekerengu 27 50 NoData
Kekerengu 36 50 NoData
Kekerengu 23 50 50
Kekerengu 39 150 150
Kekerengu 47 200 200
Kekerengu 63 300 300
Kekerengu 58 150 150
Kekerengu 51 100 100
Kekerengu 46 200 200
Kekerengu 42 150 150
Kekerengu 36 250 NoData
Kekerengu 38 150 NoData
Kekerengu 41 50 NoData
Kekerengu 39 400 NoData
Kekerengu 40 400 NoData
Kekerengu 37 400 NoData
Kekerengu 48 0 NoData
Kekerengu 57 250 NoData
Kekerengu 54 0 NoData
Kekerengu 53 300 NoData
Kekerengu 55 350 350
Kekerengu 45 250 250
Kekerengu 35 300 300
Kekerengu 46 150 150
Kekerengu 59 100 NoData
Kekerengu 55 200 200
Kekerengu 65 250 NoData
Kekerengu 38 200 200
Kekerengu 40 100 100
Kekerengu 41 200 NoData
Kekerengu 43 200 NoData
Kekerengu 45 100 NoData
Kekerengu 49 100 NoData
Kekerengu 49 100 100
Kekerengu 46 50 50
Kekerengu 51 200 NoData
Kekerengu 50 50 50
Kekerengu 45 200 NoData
Kekerengu 47 750 NoData
264

Kekerengu 59 250 NoData
Kekerengu 39 50 50
Kekerengu 56 100 100
Kekerengu 60 50 50
Kekerengu 59 150 150
Kekerengu 47 50 NoData
Kekerengu 47 0 NoData
Kekerengu 49 200 NoData
Kekerengu 49 450 NoData
Kekerengu 49 100 NoData
Kekerengu 35 550 NoData
Kekerengu 24 900 NoData
Kekerengu 40 400 NoData
Kekerengu 55 250 NoData
Kekerengu 51 150 NoData
Kekerengu 32 1100 NoData
Kekerengu 31 900 NoData
Kekerengu 53 500 NoData
Kekerengu 64 300 NoData
Kekerengu 59 200 NoData
Kekerengu 66 250 NoData
Kekerengu 58 500 NoData
Kekerengu 56 250 NoData
Kekerengu 63 350 NoData
Kekerengu 51 200 NoData
Kekerengu 61 2100 NoData
Kekerengu 54 1950 NoData
Kekerengu 56 1800 NoData
Kekerengu 16 0 NoData
Kekerengu 23 650 NoData
Kekerengu 54 950 NoData
Kekerengu 58 150 NoData
Kekerengu 58 400 NoData
Kekerengu 63 300 300
Kekerengu 47 200 200
Kekerengu 68 300 300
Kekerengu 52 150 150
Kekerengu 55 100 100
Kekerengu 59 250 250
Kekerengu 44 200 200
Kekerengu 47 250 250
Kekerengu 61 500 500
265

Kekerengu 63 200 200
Kekerengu 58 200 200
Kekerengu 55 50 NoData
Kekerengu 59 150 NoData
Kekerengu 52 300 NoData
Kekerengu 61 250 250
Kekerengu 55 150 150
Kekerengu 78 250 250
Kekerengu 57 100 100
Kekerengu 69 300 300
Kekerengu 63 200 200
Kekerengu 60 150 150
Kekerengu 65 300 300
Kekerengu 54 150 150
Kekerengu 79 400 400
Kekerengu 56 300 300
Kekerengu 63 350 350
Kekerengu 53 200 200
Kekerengu 73 450 450
Kekerengu 67 350 350
Kekerengu 65 200 200
Kekerengu 63 250 NoData
Kekerengu 66 200 NoData
Kekerengu 73 250 250
Kekerengu 85 200 200
Kekerengu 88 200 200
Kekerengu 62 250 250
Kekerengu 59 250 250
Kekerengu 85 150 150
Kekerengu 70 50 50
Kekerengu 66 250 250
Kekerengu 73 400 400
Kekerengu 58 350 350
Kekerengu 53 250 250
Kekerengu 71 300 300
Kekerengu 77 100 100
Kekerengu 55 150 150
Kekerengu 49 200 200
Kekerengu 70 250 250
Kekerengu 58 250 250
Kekerengu 81 300 300
Kekerengu 77 200 200
266

Kekerengu 77 200 200
Kekerengu 68 150 150
Kekerengu 80 200 NoData
Kekerengu 85 100 NoData
Papatea 143 1100 NoData
Papatea 163 450 NoData
Papatea 172 500 NoData
Papatea 174 750 NoData
Papatea 158 750 NoData
Papatea 160 850 NoData
Papatea 9 450 NoData
Papatea 166 350 NoData
Papatea 166 200 NoData
Papatea 175 0 NoData
Papatea 175 900 NoData
Papatea 3 1450 NoData
Papatea 180 1450 NoData
Papatea 171 800 NoData
Papatea 147 1000 NoData
Papatea 160 850 NoData
Papatea 149 900 NoData
Papatea 157 1050 NoData
Papatea 169 900 NoData
Papatea 175 700 NoData
Papatea 3 900 NoData
Papatea 2 900 NoData
Papatea 179 2800 NoData
Papatea 159 1850 NoData
Papatea 162 1600 NoData
Papatea 162 900 NoData
Papatea 169 850 NoData
Papatea 177 650 NoData
Papatea 6 350 350
Papatea 3 200 200
Papatea 10 200 200
Papatea 26 350 350
Papatea 25 200 200
Papatea 3 150 NoData
Papatea 2 700 NoData
Papatea 3 300 300
Papatea 179 350 350
Papatea 163 300 300
267

Papatea 165 100 100
Papatea 164 250 250
Papatea 143 250 250
Papatea 161 250 250
Papatea 136 250 250
Papatea 138 250 250
Papatea 130 150 150
Papatea 159 100 NoData
Papatea 143 150 150
Papatea 144 300 300
Papatea 127 50 50
Papatea 124 150 150
Papatea 125 150 150
Papatea 115 250 250
Papatea 127 250 250
Papatea 124 350 350
Papatea 119 250 NoData
Papatea 105 250 NoData
Papatea 110 150 NoData
Papatea 90 750 NoData
Papatea 92 750 750
Papatea 110 850 850
Papatea 112 1050 1050
Papatea 117 1050 1050
Tinline
Downs
36 200 200
Tinline
Downs
47 50 50
Tinline
Downs
61 250 NoData
Tinline
Downs
78 200 200
Tinline
Downs
64 200 NoData
Tinline
Downs
67 200 NoData
Tinline
Downs
68 350 350
Tinline
Downs
75 300 300
Tinline
Downs
51 250 250
Tinline
Downs
42 250 250
268

Tinline
Downs
40 450 450
Tinline
Downs
38 500 500
Tinline
Downs
44 450 450
Tinline
Downs
62 400 400
Tinline
Downs
35 500 500
Upper
Kowhai
61 0 NoData
Upper
Kowhai
69 0 NoData
Upper
Kowhai
68 0 NoData
Upper
Kowhai
69 0 NoData
Upper
Kowhai
74 0 NoData
Uppe
rKowhai
70 0 NoData
Upper
Kowhai
60 0 NoData
Upper
Kowhai
66 0 NoData
Upper
Kowhai
64 0 NoData
Upper
Kowhai
65 250 NoData
Upper
Kowhai
56 0 NoData
Upper
Kowhai
41 0 NoData
Upper
Kowhai
59 0 NoData
Upper
Kowhai
39 0 NoData
Upper
Kowhai
33 0 NoData
Upper
Kowhai
60 350 NoData
Upper
Kowhai
67 500 NoData
Upper
Kowhai
67 0 NoData
Upper 83 0 NoData
269

Kowhai
Upper
Kowhai
66 0 NoData
Upper
Kowhai
64 0 NoData
Upper
Kowhai
63 450 450
Upper
Kowhai
66 450 450
Upper
Kowhai
79 450 450
Upper
Kowhai
71 100 NoData
Upper
Kowhai
68 250 250
Upper
Kowhai
44 300 300
Upper
Kowhai
56 250 250
Upper
Kowhai
65 250 250
Upper
Kowhai
66 400 400
Upper
Kowhai
70 350 350
Upper
Kowhai
62 300 NoData
Upper
Kowhai
68 650 NoData
Upper
Kowhai
61 450 450
Upper
Kowhai
63 350 350
Upper
Kowhai
37 100 100
Upper
Kowhai
56 600 600
Upper
Kowhai
67 550 NoData
Upper
Kowhai
78 1100 NoData
Upper
Kowhai
57 1250 NoData
Upper
Kowhai
50 400 NoData
Upper
Kowhai
77 400 NoData
270

Upper
Kowhai
44 250 NoData
Upper
Kowhai
32 100 NoData
Upper
Kowhai
59 100 NoData
Upper
Kowhai
61 150 NoData
Upper
Kowhai
35 100 NoData
Upper
Kowhai
56 300 NoData
Upper
Kowhai
58 100 100
Upper
Kowhai
71 350 350
Upper
Kowhai
42 400 400
Upper
Kowhai
35 250 250
Upper
Kowhai
23 100 100
Upper
Kowhai
164 200 200
Upper
Kowhai
11 100 100
Upper
Kowhai
1 150 150
a
Fault-perpendicular width of “rollover” in our displacement profiles
b
Fault-perpendicular width across which tectonic displacement is accommodated

271

Figure S8.

272

Table S9.
Slip vector measurements
Fault Net Slip Strike Dip Trend Plunge
ConwayCharwell 1.97 222.1 79.4 249.1 67.6
ConwayCharwell 2.43 227.8 87.6 237.5 76.3
ConwayCharwell 1.58 192.2 77.3 240.7 73.2
ConwayCharwell 1.17 225.2 80.7 244.3 63.5
ConwayCharwell 1.05 244.8 73.5 269 54.1
ConwayCharwell 0.89 234.7 36.7 278.4 27.3
ConwayCharwell 0.85 230.2 40.6 266.1 26.7
ConwayCharwell 0.85 249.6 51.5 269.3 42.4
ConwayCharwell 0.35 240.2 26.7 269 13.6
ConwayCharwell 0.37 57.7 84.4 62.4 39.7
ConwayCharwell 0.27 248.2 19.1 333 19.1
ConwayCharwell 0.25 236.9 53.3 9.2 44.8
ConwayCharwell 0.35 232.9 56.6 126.9 55.5
CornerHill 1.62 183.4 0.5 266.3 0.5
CornerHill 0.51 16.2 20 63.4 15
CornerHill 0.48 18.4 73.6 177.4 50.5
Fidget 1.31 88.6 54.1 94.5 8
Fidget 1.2 78.8 69.3 83.7 12.8
Fidget 1.02 64.7 23.9 98.1 13.7
Fidget 1.25 77.9 66.6 86.8 19.7
Fidget 1.13 87.8 27.3 88.1 0.2
Fidget 1.32 82.8 48.8 99.3 18
Fidget 1.59 75.5 9.5 108.1 5.1
Fidget 1.31 86.5 28.1 117.5 15.4
Fidget 0.93 93.3 39.6 128 25.2
Fidget 0.89 253.4 6.1 303.5 4.7
Fidget 0.86 63.7 25.8 132.5 24.3
Fidget 1.19 67.5 39.9 131.6 37
Fidget 0.5 256.3 43.6 299.5 33.1
Fidget 1.57 77.5 2.2 164.1 2.2
Fidget 1.15 81.4 41.4 165.3 41.2
Fidget 2.26 83 69.9 154.3 69.9
Fidget 1.27 81.6 66.4 134.2 61.2
Fidget 1.35 72 80.5 132.4 79.1
Fidget 2.33 69.2 90 74.5 89.6
Fidget 0.28 65.3 65 144.2 64.6
Fidget 0.74 240 17.1 321.1 16.9
Fidget 1.4 67.4 33.5 173.4 32.5
Fidget 0.54 70.9 28.3 137.6 28.3
273

Fidget 0.78 247.3 12.6 329.5 12.5
Fidget 1.74 244.7 69 320.1 68.4
Fidget 1.73 252.8 62.2 323 60.8
Fidget 2.41 257.2 68.2 314.9 64.7
Fidget 1.3 259.6 3 348.1 3
Fidget 1.29 258.6 12.7 332.8 12.2
Fidget 1.63 259.1 26.5 343.6 26.4
Fidget 1.54 265 29.1 352.2 29.1
Fidget 1.37 268 3.5 352 3.4
Fidget 1.63 93.2 38.9 181.1 38.9
Fidget 1.36 90.4 42.1 197 40.9
Fidget 2.02 92.5 88.7 94.3 53.7
HumpsE 0.52 274.1 62.6 302 42
HumpsE 1.19 75.1 88.7 75.6 20.6
HumpsE 1.44 266 73.6 74.4 34.3
HumpsE 1.06 71.2 85.3 73.6 26.7
HumpsE 1.14 60.4 48 86.3 25.8
HumpsE 1.46 277.3 79.3 277.7 2
HumpsE 0.81 91.3 75.4 266.5 17.7
HumpsE 1.28 95 72 272.9 6.3
HumpsE 1.63 259.1 71.7 265 17.4
HumpsE 1.54 238.2 81.8 246.7 45.7
HumpsE 1.6 227.7 89.6 228.3 58.5
HumpsE 1.31 255.5 85.5 260.2 46.3
HumpsE 1.99 68.3 84.9 235.8 67.5
HumpsE 2.68 72.3 52.6 210.5 41.1
HumpsE 2.46 77.9 45.3 223.2 29.9
HumpsE 2.77 77.6 58.5 226.5 40.5
HumpsE 3.01 63.1 65.7 230.5 25.7
HumpsE 3.05 229.7 60.4 253.2 35.1
HumpsE 3.33 244.4 89.3 245 40.9
HumpsE 2.48 235.4 86.3 237.9 33.5
HumpsE 3.72 236.3 81.7 239.4 20.5
HumpsE 3.24 69.2 62.4 237 22.1
HumpsE 2.99 94.5 58.3 253.6 30
HumpsE 2.8 97.4 17.1 242.1 10.1
HumpsE 3.01 80.3 28.8 244.2 8.7
HumpsE 3 67.5 38.9 239.5 6.4
HumpsE 3.09 66.1 6.3 241.4 0.5
HumpsE 1.52 241.8 10 32.7 4.9
HumpsE 1.73 53.8 39.2 211.9 16.9
HumpsE 1.25 227.6 23 232.4 2.1
274

HumpsE 1.41 53.9 86.1 232 25.7
HumpsE 1.57 235 16.4 52.2 16.4
HumpsE 1.67 49.7 2.2 67.4 0.7
HumpsE 1.44 53.3 16.1 68.7 4.4
HumpsE 1.53 64.8 57.2 231.1 20.2
HumpsE 1.9 230.3 53.8 45.9 5.9
HumpsE 1.56 227.7 39 241.3 10.8
HumpsE 1.31 64.2 85.3 61 34.5
HumpsE 1.41 239.2 34.2 256.5 11.4
HumpsE 1.2 61.4 58 227.8 20.6
HumpsE 1.08 68.3 85.7 243.4 49
HumpsE 0.78 74.6 89.9 254.5 30.1
HumpsE 0.91 72.8 78.2 220.4 68.7
HumpsE 0.71 55.3 23.3 77.8 9.3
HumpsE 0.61 65.6 40.6 128.5 37.3
HumpsE 0.58 40.7 2.6 152.8 29.9
HumpsE 1.2 47.7 50.1 128.6 49.7
HumpsE 0.8 50.6 37.9 182.2 30.2
HumpsE 1.13 215.5 0.1 343.8 0.1
HumpsE 0.79 202.1 9.2 354 4.3
HumpsE 0.34 198.7 29.1 9.2 5.3
HumpsW 0.27 222.8 13.7 30.9 9.2
HumpsW 0.63 242.3 72.4 33.4 56.8
HumpsW 0.61 224.3 70.2 29.6 35.2
HumpsW 0.67 239.8 86.6 256.8 78.6
HumpsW 0.66 85.2 55.2 200.1 52.6
HumpsW 0.97 92.9 39.4 209.7 36.2
HumpsW 0.92 90 21.4 214.6 17.9
HumpsW 1.03 91.2 34.2 226.9 25.4
HumpsW 0.97 265.5 19.7 42.9 13.6
HumpsW 0.92 276.7 36.5 74.9 15.4
HumpsW 0.66 235.3 54 71.5 5.3
HumpsW 1.86 81.9 47.7 104 22.4
HumpsW 1.41 95.1 15.5 107.6 3.4
HumpsW 1.38 272.1 80.6 85.6 34.6
HumpsW 2 269.4 72.3 83.3 18.4
HumpsW 0.81 53.9 43.5 78.6 21.6
HumpsW 0.75 243.3 65.6 18.1 57.4
HumpsW 2.12 76.4 64.8 87.4 22.2
HumpsW 2.34 266.9 61.1 81.6 9.5
HumpsW 2.11 109 7 259.2 3.5
HumpsW 1.66 282.8 13.2 78 5.6
275

HumpsW 1.63 269.2 8.1 75.3 2
HumpsW 1.91 97.9 10.6 251 4.9
HumpsW 2.65 81.4 53.5 245.4 20.4
HumpsW 1.73 77.2 89.5 257.1 0
HumpsW 2.24 85.3 51.5 76.3 11.2
HumpsW 2.01 77.2 67.7 82 11.5
HumpsW 1.84 75.1 66.8 76.9 4.1
HumpsW 1.93 254.9 59 258.5 5.9
HumpsW 2.13 69.8 23.5 74.9 2.2
HumpsW 1.85 82.3 57.6 253.4 13.6
HumpsW 1.61 36.4 11.6 79.2 7.9
HumpsW 2.27 249.1 86.9 67.2 31.6
HumpsW 1.33 252.7 30 55.2 9.9
HumpsW 1.74 257.2 31.2 58.3 11.1
HumpsW 1.55 275.3 1.9 61.2 1.1
Hundalee 1.72 243.3 11.6 338.4 11.6
Hundalee 1.99 235.4 35.4 341.6 34.3
Hundalee 2.44 234.5 31.8 2.8 25.9
Hundalee 2 243.7 14.6 342.1 14.4
Hundalee 2.21 247 15.8 350 15.5
Hundalee 2.3 264.9 12.3 333.5 11.5
Hundalee 2.62 265.6 14.6 334.5 13.6
Hundalee 2.67 220.3 17.2 331.1 16.2
Hundalee 3.62 206.4 33.1 327.5 29.2
Hundalee 3.49 187.2 31.7 333.2 19.1
Hundalee 3.88 180.7 52.9 333.8 30.9
Hundalee 4.13 206 39.2 328.6 34.5
Hundalee 4.34 252.3 13.9 327.5 13.5
Hundalee 3.17 187 18.4 340.3 8.5
Hundalee 3.06 185.2 13.1 329.8 7.7
Hundalee 3.3 156.5 33.8 320.4 10.5
Hundalee 3.03 313.1 63.1 317.3 8.3
Hundalee 3.13 184.5 55.5 337.5 33.4
Hundalee 3.39 214.5 27.6 329.6 25.3
Hundalee 0.61 292.9 6.6 30.4 6.6
Hundalee 0.98 317.5 29.3 7.5 23.2
Hundalee 1.11 264.5 28.5 6 28
Hundalee 2.18 243.7 56.7 305.6 53.3
Hundalee 1.86 288.8 59.9 309.9 31.8
Hundalee 1.86 235.4 47 318.8 46.8
Hundalee 1.74 253.7 20.2 319.2 18.5
Hundalee 2.68 226.3 35.2 323.1 35
276

Hundalee 2.36 206.4 20.4 344.6 13.9
Hundalee 2.29 224 7.8 340.2 7
Hundalee 2.82 247.8 33.1 317.8 31.5
Hundalee 2.74 43 19.9 146.9 19.3
Hundalee 3.54 215.9 0.8 300.6 0.8
Hundalee 0.04 241.8 16 294 12.7
Hundalee 3.07 246.7 19.9 302.7 16.7
JordanThrust 3.64 209.4 70.7 213.7 12.1
JordanThrust 3.98 225.6 73 40.1 17.4
JordanThrust 3.62 220.6 30.2 32.8 4.5
JordanThrust 4.32 35.7 32.8 68.6 19.3
JordanThrust 3.79 211.4 88.7 30.9 21.4
JordanThrust 4.21 22.3 75.9 25.1 11
JordanThrust 4.15 29.1 57.8 38.9 57.8
JordanThrust 4.47 220.7 84.7 39.6 12
JordanThrust 4.46 38.8 74 41.7 10.1
JordanThrust 4.74 232.1 4.5 31.7 1.6
JordanThrust 5.09 230.8 20.5 32.6 6.7
JordanThrust 5.43 209 8.9 227.3 2.8
JordanThrust 5.22 221.3 69.2 38.4 7.6
JordanThrust 5.14 33.4 80.9 37.1 21.8
JordanThrust 5.16 220 64.5 36.1 8.2
JordanThrust 5.87 211.3 52.6 30.7 0.8
JordanThrust 6.58 203.2 19.9 211.6 3
JordanThrust 6.86 33.4 82.7 35.1 1.7
JordanThrust 7.79 215.9 79.2 32.8 15.6
JordanThrust 8.75 31.9 80.3 35.4 19.7
JordanThrust 9.1 234.1 53.2 37.7 20.6
JordanThrust 10.04 228.9 64.1 11.6 24.2
JordanThrust 10.29 221.3 79.1 36.1 25.2
JordanThrust 11.82 224.1 71.3 33.2 29.3
JordanThrust 11.45 222.6 88.4 41.5 34.9
JordanThrust 10.62 234.2 75.5 41.4 40.7
JordanThrust 6.6 240.7 59.8 42.8 27.8
JordanThrust 4.65 243.1 47.3 46.4 17.3
JordanThrust 3.24 219.6 76.4 231.6 40.8
Kekerengu 2.16 253.6 37.3 35.9 24.9
Kekerengu 3.24 233 72.8 34.8 45.3
Kekerengu 2.51 224 68.1 19.4 46.1
Kekerengu 3.48 30.4 58.3 191.6 27.6
Kekerengu 7.89 225.3 86.5 226.7 21.4
Kekerengu 8.48 238.3 58 241.9 5.8
277

Kekerengu 9.59 222.9 18.7 243.8 6.9
Kekerengu 9.57 218.3 33.6 243 15.5
Kekerengu 10.31 218.6 44.8 243.2 22.5
Kekerengu 10.09 229.8 58.1 244 21.5
Kekerengu 9.99 234.2 61.3 244.9 18.7
Kekerengu 10.16 222.8 41.8 241.7 16.2
Kekerengu 10.19 233.6 73.1 239.5 18.7
Kekerengu 10.29 232 57.1 241.9 14.9
Kekerengu 10.92 222.1 39.6 242.1 15.8
Kekerengu 11.24 225.4 50 240.3 17.1
Kekerengu 10.79 227.7 44.4 242.8 14.3
Kekerengu 11.02 229.1 34.4 244.4 10.2
Kekerengu 11.66 231.7 51.4 241 11.5
Kekerengu 11.18 232.9 53.2 238.5 7.4
Kekerengu 11 231.7 48.3 236.8 5.7
Kekerengu 11.09 228.5 39.2 236.8 6.8
Kekerengu 10.91 217.9 14 237.7 4.8
Kekerengu 11.44 223 33.1 237 9
Kekerengu 11.25 218.7 38.9 234.2 12.1
Kekerengu 11.57 53.2 88.9 232.9 16.8
Kekerengu 11.25 62.8 62 231.8 19.8
Kekerengu 11.22 58.1 75 232.3 20.7
Kekerengu 11.98 235.8 86.2 236.9 16
Kekerengu 12.04 219.4 20.1 234.7 5.5
Kekerengu 11.23 221.9 52.7 230.7 11.3
Kekerengu 10.93 60.9 17.7 231 3.1
Kekerengu 11.25 58.1 27.5 231.9 3.2
Kekerengu 10.93 234.3 30.7 52.4 1.1
Kekerengu 11.16 230.4 65.3 49.5 2
Kekerengu 10.74 238.4 11.3 53.8 0.9
Kekerengu 10.45 235.1 79.7 235.7 3.5
Kekerengu 10.53 237.8 77.7 239.2 6.3
Kekerengu 10.74 69.3 45.1 242.2 7.1
Kekerengu 10.75 241.9 81 243 6.7
Kekerengu 10.57 242.3 53.6 245.7 4.6
Kekerengu 10.73 244.6 86.3 245 5.9
Kekerengu 10.93 243.8 78.2 246.5 12.6
Kekerengu 10.23 244.8 54.9 250.8 8.5
Kekerengu 10.51 72.5 76.4 251.1 5.9
Kekerengu 10.58 77.2 41.2 249.5 6.7
Kekerengu 10.74 69.8 84.4 248.9 9.6
Kekerengu 11.17 71.2 78.3 249.3 9.1
278

Kekerengu 10.65 234.4 26.8 253.4 9.3
Kekerengu 8.55 251.7 88.8 71.4 15.6
Kekerengu 8.43 61.7 64.8 67.5 12.1
Kekerengu 7.35 252.3 82 70.4 13.1
Kekerengu 8.17 73.2 37.8 245.7 5.8
Manakau 4.24 49.3 56.2 153.8 55.3
Manakau 2.17 67.6 4.7 130.9 4.2
Manakau 2.88 46.5 32.5 179.8 24.9
Manakau 1.72 47.4 5.7 184 3.9
Manakau 3.75 41.8 37.4 188.7 22.6
Manakau 2.91 55.2 44.8 167.9 42.5
Manakau 1.33 75.7 46.5 197.1 42
Manakau 2.21 60.7 78.6 224 55
Manakau 1.04 85.5 62.6 225.7 51
Manakau 1.86 51.4 78.2 205.7 64.3
Manakau 0.37 67.3 80.2 243.7 19.9
Manakau 1.9 67.4 65.4 176.7 64.2
Manakau 1.89 41 52.1 183.3 38.2
Manakau 2.67 58.4 52 194.7 41.5
Manakau 2.1 75.9 66 167.5 66
Manakau 2.19 71.8 73.9 204.8 68.5
Manakau 4.41 45.7 87.6 219.7 67.8
Manakau 2.94 22.5 80.7 170.7 72.7
Manakau 1.95 60.5 47 172.6 44.8
Manakau 1.8 68 59 146.5 58.5
Manakau 1.04 63.6 43.4 167.6 42.6
Manakau 3.11 234.9 89.2 237.3 70.7
Manakau 2.92 216.9 82 243.4 72.6
Manakau 0.59 232.6 70.3 27.7 49.7
Manakau 0.52 54.5 10.7 204.8 5.3
Manakau 0.68 34.7 16.3 203.1 3.4
Manakau 0.68 46.2 4.6 202.1 1.9
Manakau 0.77 50.6 50 188.8 38.5
Manakau 2.61 65 63.3 163.5 63.3
Manakau 2.95 91.8 70.9 123.9 57
NLeader 1.31 223.4 35.8 359.8 26.5
NLeader 2.11 245 10 345.1 9.8
NLeader 1.82 71.1 15.8 308.7 13.5
NLeader 1.8 241.7 16.9 316.8 16.4
NLeader 1.27 229.8 8.5 310.4 8.4
NLeader 0.78 51.1 0.4 88.7 0.3
NLeader 2.45 196.1 8.5 337.3 5.3
279

NLeader 2.57 203.5 4.7 328.9 3.9
NLeader 2.74 180.5 5.3 147.9 2.9
NLeader 2.78 209.1 21.5 328 19.1
NLeader 2.75 175 37.4 323.3 21.9
NLeader 2.87 221 8.2 307.4 8.2
NLeader 2.99 356.2 1.6 124 1.3
NLeader 3.21 308.7 55.1 125.6 12.1
NLeader 3.03 302.9 44.5 312.3 9.1
NLeader 4.04 327.3 23.5 129 7.8
NLeader 4.88 174.9 3.4 301 2.7
NLeader 4.41 156.5 7.4 290.7 5.3
NLeader 0.87 313.6 22.8 349.9 14
NLeader 1.17 262.4 8 165.6 8
NLeader 1.77 239.3 46 357.5 42.2
NLeader 2.25 248.6 29.3 325.3 28.7
NLeader 2.37 259.9 37.5 324 37
NLeader 2.3 229.6 21.4 330.8 21.1
NLeader 2.91 192.8 35.1 256.1 32.2
NLeader 2.69 189.8 34.9 257.8 32.9
NLeader 1.98 198.1 28.5 255.4 24.6
NLeader 1.88 199.1 22.9 241.6 15.9
NLeader 1.76 205.2 4.1 262.5 3.4
NLeader 1.3 206 12.1 260 9.8
NLeader 1.33 200.6 27.1 262.9 24.4
NLeader 1.24 187.8 33.6 274.2 33.5
NLeader 1.16 218.5 57.5 276.7 53.1
NLeader 0.6 228.8 83.4 233.9 37.8
NLeader 0.57 298 36.4 229.9 34.4
NLeader 1.66 309 2.9 310.8 0.1
NLeader 3.24 204.5 5.6 333.8 4.3
NLeader 1.43 120.6 13.5 276.4 5.6
NLeader 3.38 168.9 45.1 319.2 26.4
NLeader 2.64 175.4 37 319.8 23.6
NLeader 2.66 157 25 319.4 8
NLeader 2.81 153.9 43.7 325 8.4
NLeader 2.94 162 60.6 332.8 15.8
NLeader 2.86 203.2 88.5 21.7 45.7
NLeader 2.87 206.8 78.9 13.9 48.7
NLeader 3.12 211.9 79.7 18.6 51.6
NLeader 3.07 206.6 79.1 11.8 52.9
NLeader 2.65 197.1 84.5 8.8 56.1
NLeader 2.89 188.3 84.7 357.9 62.8
280

NLeader 2.14 206.8 58 326.2 54.4
NLeader 1.36 221.9 63.5 282.9 60.3
NLeader 0.54 267.3 13.6 343 13.2
NLeader 0.65 249.6 11.5 281 6
NLeader 1.07 56.4 70.8 224.8 30
NLeader 1.47 251.9 73.1 266.5 39.6
NLeader 2.4 236.6 49.7 290.9 43.8
NLeader 2.17 212.9 14.9 284.7 14.2
NLeader 2.5 209.9 15.6 294.2 15.6
NLeader 2.76 259.1 13.3 279.2 13.1
NLeader 2.45 232.4 18.4 270.5 11.6
NLeader 2.38 229.6 14 267.9 8.8
NLeader 1.74 231.2 29.9 258.4 14.7
NLeader 2.64 247.4 42 278.6 31.2
NLeader 1.28 57.5 7.7 102.6 5.5
NLeader 0.4 206.6 32.4 292.1 32.3
NLeader 1.54 174.8 8.7 329.6 3.7
NLeader 5.01 72 25.4 144.3 24.3
NLeader 5.49 82.1 45 124.7 34
NLeader 1.07 108.1 38.4 266.1 16.6
NLeader 0.85 103.6 28.9 241.7 20.2
NLeader 1.22 222.3 63.6 348.6 58.4
NLeader 1.19 226.3 58.9 13.9 41.6
NLeader 0.61 250.3 24.8 292.7 23.5
Papatea 8.27 156.3 26.1 300.4 16
Papatea 9.15 167.5 43.2 306.4 31.7
Papatea 7.94 174.5 31.6 305.3 25
Papatea 8.99 168.4 46.1 305.8 35.2
Papatea 8.8 180.2 44.1 308.7 37.2
Papatea 9.22 162.2 50 307.6 34.1
Papatea 10.6 155.3 65 311.2 41.2
Papatea 9.8 168.7 59.9 318.4 41.1
Papatea 9.13 183.1 59 322.5 47.3
Papatea 9.65 163.6 71.5 320.2 49.9
Papatea 9.4 167.5 74.9 327.2 52.2
Papatea 10.23 183.4 69.5 324.1 59.4
Papatea 9.13 200.8 60.7 316.2 58.2
Papatea 9.32 185.8 65.1 311.2 60.3
Papatea 11.01 177.2 59 297.3 55.2
Papatea 4.94 164 34.8 315.3 18.5
Papatea 9.22 149.9 74.4 304.5 57
Papatea 8.13 133.8 89.1 312.5 55.4
281

Papatea 8.11 149.4 80.2 313.7 57.5
Papatea 8.9 128.9 89 306.9 62.2
Papatea 8.69 302 80.8 320.9 63.4
Papatea 7.5 302.2 82.3 319.2 65.3
Papatea 6.81 284.5 76.5 309.5 60.3
Papatea 3.91 278.3 20.5 358.8 20.2
Papatea 3.82 295.3 28.5 29.7 28.5
SLeader 0.35 220.1 47.3 355.6 37.2
SLeader 0.65 219.6 10.7 343.6 8.9
SLeader 1.05 220 33.4 352.6 25.9
SLeader 1.66 228.2 51 338.7 49.2
SLeader 1.16 189.1 29.1 318.6 23.2
SLeader 1.05 197.1 0.2 317.2 0.2
SLeader 1.55 204 13.2 311.4 12.6
SLeader 3.74 196.7 30.1 334.1 21.4
SLeader 3.86 204.3 63.1 354.2 44.7
SLeader 4.13 184.7 86.8 0 56.2
SLeader 3.36 7.1 86.1 11.4 47.8
SLeader 3.98 202.5 87.6 17.4 64.2
SLeader 1.31 336.5 56.3 62.1 56.2
SLeader 0.37 80 50.6 137.6 45.8
SLeader 0.42 270.2 15.3 1 15.3
SLeader 0.5 264.4 48.1 357 48
SLeader 0.8 263.6 61.2 17.9 58.9
SLeader 0.64 241.5 39.7 8.2 33.6
SLeader 0.78 240.6 17.9 334.1 17.9
SLeader 1.67 216.1 57.9 337.3 53.7
SLeader 1.57 248.4 58.9 34.6 42.7
SLeader 1.98 286.3 48.4 17.3 48.4
SLeader 0.4 32.1 16.6 134.8 16.2
SLeader 1.11 214.8 21.9 352.1 15.3
SLeader 1.48 223 43.8 344.5 39.2
SLeader 2.45 212.7 53.2 312.9 53.2
SnowflakeSpur 1.37 209.6 4.2 305.7 4.2
SnowflakeSpur 0.68 190 44.6 321.5 36.4
SnowflakeSpur 0.79 208.3 9.9 344.4 6.9
SnowflakeSpur 1.38 207.1 25.5 351.5 15.5
SnowflakeSpur 1.05 210 48.2 353.1 33.8
SnowflakeSpur 1.01 34.9 35.5 60.1 16.9
SnowflakeSpur 1.84 210.4 86.9 19.2 74.2
SnowflakeSpur 0.88 44.3 76.7 63.6 54.3
SnowflakeSpur 1.36 30.4 34.8 62.8 20.4
282

SnowflakeSpur 2.09 212 82.3 216 27
SnowflakeSpur 1.66 210.4 58.8 216.2 9.4
SnowflakeSpur 1.85 217.9 37.1 255.6 24.8
SnowflakeSpur 1.56 218.5 7.9 248.7 4
SnowflakeSpur 2.6 200.7 40.2 238.5 27.4
SnowflakeSpur 2.03 219.6 66.2 234 29.4
SnowflakeSpur 2.61 215 14.5 250.1 8.5
SnowflakeSpur 2.23 212.4 45.3 230.2 17.2
SnowflakeSpur 2.21 47.7 55.2 49 1.9
StoneJug 0.45 112.3 21.8 192.6 21.5
StoneJug 0.76 111.9 4.6 194.2 4.5
StoneJug 1.14 114.1 6.3 238.5 5.2
StoneJug 2.02 311.2 50.8 353.1 39.3
StoneJug 1.28 320.6 47.2 328.7 8.7
StoneJug 0.91 154.3 48.4 196.2 37
StoneJug 1.32 341.3 61.3 40.5 57.5
StoneJug 0.75 155.2 0.4 202.1 0.3
StoneJug 1.54 338.7 74.1 5.8 57.9
StoneJug 1.75 354.3 85.1 0.1 49.7
StoneJug 1.24 354.5 67.6 3.6 20.9
StoneJug 1.59 344.9 48.1 14 28.5
StoneJug 1.62 343.7 35.5 357.2 9.5
StoneJug 1.84 346.8 39.8 10.7 18.6
StoneJug 1.75 351.6 77 7.3 15.7
StoneJug 1.65 186 89.8 186 17.4
StoneJug 1.9 1.5 73.3 172.9 26.5
StoneJug 0.8 169.6 80 192.8 65.8
StoneJug 1.13 155.9 39.7 203.4 31.5
StoneJug 1.74 154.2 74 190.8 64.4
StoneJug 1.21 152.6 40.1 211.3 58.7
StoneJug 0.17 135 36.6 257 32.2
StoneJug 0.18 314.3 36 6.5 29.9
StoneJug 0.24 143.3 88.5 146.6 65.8
StoneJug 0.25 327.1 60.6 320.2 11.9
StoneJug 0.76 163.6 89.5 163.6 33.7
StoneJug 1.39 162.4 88.5 341.5 31
StoneJug 2.35 159.8 84.6 338.7 10.9
StoneJug 2.53 164.3 80.9 168.4 23.8
StoneJug 2.29 160.5 84.3 162.2 16.1
StoneJug 2.23 342 72.7 157 15.7
StoneJug 1.35 149.2 81.6 190.5 41.3
StoneJug 1.32 167.9 66.2 195.2 46.1
283

StoneJug 0.56 159.3 36.2 284.4 30.9
StoneJug 0.18 314.2 0.5 6.6 0.4
StoneJug 0.87 334.6 63 142.4 22.6
StoneJug 1.11 329.4 25.6 138.5 5.2
StoneJug 1.78 137 67.1 302.7 30.4
StoneJug 1.08 128.4 66.8 141.8 28.5
StoneJug 0.77 116.8 29.2 135.1 10
StoneJug 0.54 101.4 2.9 116 0.7
StoneJug 0.41 103.1 11.9 109.5 1.4
StoneJug 0.5 299.6 26.4 13.6 25.5
StoneJug 0.73 312 88 314.9 54.5
StoneJug 0.65 321.8 59.2 331.2 15.3
StoneJug 0.58 155.9 42.7 181.1 21.4
StoneJug 0.48 173.5 64.7 164.7 17.9
TinlineDowns 3.67 223.1 74.3 254.6 61.6
TinlineDowns 4.54 71 69.8 226.3 48.6
TinlineDowns 4.01 69.9 84.3 241.7 55.3
TinlineDowns 2.49 50.5 86.5 226.1 51.6
TinlineDowns 2.92 218.1 58.7 252 42.5
TinlineDowns 2.38 229.5 74.4 244.3 42.3
UpperKowhai 1.07 245.7 55.2 21.6 45
UpperKowhai 1.78 70.9 55.5 134.3 52.5
UpperKowhai 1.77 244.4 89.8 63.5 75.8
UpperKowhai 1.83 230 88.8 20.4 87.5
UpperKowhai 4.04 221.3 44 7.5 28.2
UpperKowhai 2.75 241.4 30.2 352.4 28.5
UpperKowhai 3.48 243.5 29.4 8.6 24.7
UpperKowhai 3.76 253.1 28.2 21.9 22.7
UpperKowhai 4.03 234.9 62.1 31.9 36.5
UpperKowhai 3.35 245.3 28.7 29.9 17.6
UpperKowhai 3.59 246.7 67.1 41.7 44.9
UpperKowhai 5.86 242.9 82.1 50.8 56.7
UpperKowhai 4.51 229.9 81.5 37.6 55
UpperKowhai 4.02 247.6 73.3 56.9 31.7
UpperKowhai 4.07 243.8 82.9 47.9 65.4
UpperKowhai 4.56 227.4 53.1 21.6 30
UpperKowhai 4.55 244.6 25.3 32 14.3
UpperKowhai 4.65 217.4 62.4 32.5 9.2
UpperKowhai 3.87 181 23.1 190.4 4
Waiautoa 1.06 173.3 67.6 293 64.6
Waiautoa 0.37 167.9 47.6 310.8 33.5
Waiautoa 0.34 157.2 43.7 220.3 40.5
284

Waiautoa 0.59 162 42.7 216.3 36.9
Wharekiri 1.25 344.8 42.6 135 24.5
Wharekiri 1.62 157.2 83.8 165 51.3
Wharekiri 1.27 157.5 78.1 160.3 13.1
Wharekiri 2.31 351 73.9 159 35.7
Wharekiri 2.17 348.8 67.4 166.5 5.4
Wharekiri 2.32 321.5 44.1 342.1 18.9
Wharekiri 2.06 155.1 41.7 191.8 28
Wharekiri 1.04 167 15.4 181.8 4
Whites 0.86 187.8 36.3 247.4 32.4
Whites 0.94 169.1 63 317.9 45.4
Whites 1.22 197.6 28.5 310.3 26.3
Whites 1.46 163.3 46 310.4 29.3
Whites 1.93 159.4 67.4 329.8 21.7
Whites 1.65 183.9 64.4 344.9 34.2
Whites 1.65 178.8 81 355.9 18
Whites 2.08 182.7 47.4 344.5 18.8
Whites 1.92 177.8 78.2 353.6 19.2
Whites 1.95 187.4 80.6 1.4 32
Whites 2.2 192.9 63.4 357.3 28.3
Whites 2.12 195.8 35.4 345.9 19.5
Whites 2.01 171.2 89.9 351.2 29.7
Whites 1.83 184.3 64.9 355.2 18.7
Whites 1.76 188.9 75.7 358.4 35.5
Whites 2.21 197.3 54.1 330.8 45
Whites 1.48 202.4 71 7.6 36.6
Whites 1.21 183.7 73.5 355.3 26.3
Whites 0.9 176.8 82.9 178.3 11.7
Whites 1.12 175.4 1.8 339.5 0.6
Whites 0.63 351.2 85.9 167.2 44.1

285

Figure S10a.

286

Figure S10b.

287

Figure S10c.

288

Figure S10d.

289

Figure S10e.

290

Figure S10f.

291

Figure S10g.

292

Figure S10h.

293

Figure S10i.

294

Figure S10j.

295

Figure S10k.

296

Figure S10l.

297

Figure S10m.

298

Figure S10n.

299

Figure S10o.

300

Figure S10p.

301

Figure S10q.

302

Figure S10r.

303

Figure S10s.

304

Figure S10t.

305

Figure S10 u.

306

Figure S10v.

307

Table S11.
Off-fault deformation determinations, calculated using different methods
ALL MEASUREMENTS

Binning
method
mean
OFD (m)
mean
OFD%
median
OFD (m)
median
OFD%

Untrimmed
Median 2.0 18.0 1.8 17.6

Mean 2.0 17.8 1.7 17.3

Maximum 1.6 14.2 1.5 14.3

Unbinned 2.1 19.2 1.8 17.9

Trimmed
Median 2.3 20.0 1.9 18.1

Mean 2.2 19.8 1.8 17.9

Maximum 1.8 15.8 1.6 14.5

Unbinned 2.4 23.2 2.1 20.6

DISCRETE MEASUREMENT ONLY

Binning
method
mean
OFD (m)
mean
OFD%
median
OFD (m)
median
OFD%

Untrimmed
Median 3.9 37.8 3.5 35.8

Mean 3.9 37.8 3.5 35.8

Maximum 3.7 35.9 3.5 35.2

Unbinned 3.9 37.5 3.5 35.2

PROJECTED MEASUREMENTS ONLY

Binning
method
mean
OFD (m)
mean
OFD%
median
OFD (m)
median
OFD%

Untrimmed
Median 0.9 5.5 0.6 6.4

Mean 0.9 5.6 0.6 6.4

Maximum 0.9 5.1 0.6 6

Unbinned 0.98 6.6 0.66 7

Trimmed
Median 1.3 12.7 1.1 10.6

Mean 1.3 12.9 1.1 10.6

Maximum 1.3 12.2 0.9 9.9

Unbinned 1.4 13.1 1.1 10.9

At the SW and NE ends of the Kekerengu fault, the Jordan Thrust and Tinline Downs faults
(respectively) are quite close to the Kekerengu, and do not allow for COSI-Corr measurements to
be accurately projected into the fault. Our "untrimmed" fault sets include these measurements;
for our trimmed data sets, we conducted the same analyses without considering those points.
308

The discrete measurement set did not include points from within the overlapping parts of the
fault zones. Excluded field measurements: 122, 120, 119, 17, 16, 15, 12, 10, 8.
The binning method determines how offsets were grouped for cases in which multiple field
measurements were collected within a single, 200-m-wide COSI-Corr profile. Four binning
approaches: (1) Median binned, as presented in the text - because COSI-Corr takes a running
median of the data for profile measurement; (2) mean binned, in case the mean is more
representative of the data; (3) max binned, to provide the most conservative estimate possible
of OFD; and (4) unbinned, in which all field measurements within a given set are compared
directly to the nearest COSI-Corr measurement.

309

Appendix C: Evolution and progressive geomorphic manifestation of surface faulting: A
comparison of the Wairau and Awatere faults, South Island, New Zealand

310

Text S1.
Characteristics of the Wairau and Awatere faults
The Wairau and Awatere faults are principal faults within the Marlborough Fault System
(MFS), a system of mainly right-lateral strike-slip faults that accommodate relative Pacific–
Australian plate motion between the Hikurangi subduction margin to the north, and the Alpine
right-lateral–reverse oblique-slip fault to the south. From the NE corner of the conspicuous
Alpine-Wairau restraining bend (-42.0655°N, 172.4166°E) to the NE-most onshore extent of the
fault (-41.4375°N, 174.0307°E), the average strike of the Wairau fault is ~065°. At the Branch
River site (between -41.6744°N, 173.1843°E and -41.6686°N, 173.1996°E), the average strike of
the Wairau fault is ~064°. From its approximate junction with the Alpine fault (-42.3332°,
172.2610°) to its junction with the eastern Awatere fault near Molesworth Station (-42.0297°
173.3351°), the western (Molesworth) section of the Awatere fault has an average strike of ~070°.
At the Saxton River site (between -42.0911° 173.1517° and -42.0879° 173.1654°) the average
strike of the Awatere fault is ~074°. The regional and local (site-specific) strikes of the Wairau
and Awatere faults are therefore within ~10° of the Pacific plate motion vector relative to the
Australian within the MFS, which is ~39 mm/yr at ~253° (DeMets et al., 1990; DeMets et al.,
1994).
The Wairau and Awatere faults are hosted in similar bedrock. The Wairau fault at Branch
River juxtaposes sandstone and siltstone greywacke of the Caples terrane, N of the fault, against
deformed sandstone and mudstone of the Torlesse composite terrane, S of the fault (Bishop et al.,
1976; Rattenbury et al., 2006). The Awatere fault at Saxton River is hosted within the sandstones
and mudstones of the Torlesse composite terrane (see also S2; Rattenbury et al., 2006).
311

Additionally, these faults have similar average slip rates since ~15–18 ka, roughly the time
of deposition of the oldest terrace deposits at each site (e.g., Lensen, 1968; Mason et al.,
2006b). The Wairau fault has a horizontal slip rate of 3–5 mm/yr since ~18 ka, as determined
from measuring displaced features dated using cobble weathering-rind age analysis (Knuepfer,
1988, 1992) or correlated to glacial outwash deposits dating to c. 18 ka (Lensen, 1976). Mason et
al. (2006b) reported an average slip rate 5.6 ± 0.8 mm/yr for the Awatere fault, though their data
suggest that the slip rate has been highly variable since ~15 ka, ranging from as slow as 3.8 (+1.3/-
1.2) mm/yr between ~2–4 ka, to as fast as 7.8 (+2.0/-2.4) mm/yr between ~5.5–7 ka, and (Mason
et al., 2006b).
The Wairau and Awatere faults have experienced a similar number of earthquakes since c.
18 ka, with similar amounts of average slip per earthquake. Onshore and offshore paleoseismic
records indicate that the Wairau fault has ruptured up to 8 times since c. 18 ka (Zachariasen et al.,
2006; Barnes and Pondard, 2010). Paleoseismic trenching at multiple locations along the Awatere
fault indicate that the Molesworth section of the Awatere fault has ruptured at least 10 times since
c. 13 ka (e.g., McCalpin, 1996; Benson et al., 2001; Mason, 2004; Mason et al., 2006a). Lensen
(1968, 1976) used geomorphic features variably displaced ~5–70 m along the Wairau fault to infer
an average slip per event of 5–7 m since c. 18 ka. Various workers (e.g., Grapes et al., 1998; Little
et al., 1998; Mason and Little, 2006) used offset geomorphic features along the Awatere fault to
infer an average slip per event of 5–8 m.
Moreover, these findings indicate that the Wairau and Awatere faults have similar tectonics
settings, bedrock lithologies, modern slip rates, and number of earthquakes and average slip per
event (since ~15–18 ka). Because of the similarities, and the similarities of the Branch River and
312

Saxton River sites discussed in the main text, the Wairau and Awatere faults are comparable for
the analyses conducted in this study.

313

Figure S2a.

314

Figure S2b.

315

Figure S3.

316

Table S4.
Wairau fault, Branch River site

Awatere fault, Saxton River site
Feature
Horizontal
offset (m)
1
Age (ka)
2

Feature
Horizontal
offset (m)
3
Age (ka)
4

Terrace A 55 ± 2 16 ± 2

Bedrock
Spur
69 ± 5 > 14.5 ± 1.5

Terrace W ≥ 57 ± 2 14.7 ± 6.71

Terrace T1 69 ± 5 14.5 ± 1.5

Terrace B 57 ± 2 13.3 ± 2.47

Terrace T2 55 ± 6 6.7 ± 0.7

Terrace C 53 ± 2 12.1 ± 2.17

Terrace T3 32.5 ± 2.5
> 5.46 ±
0.77

Terrace D 38 ± 2 10.8 ± 1.89

Terrace T4 25 ± 2 5.46 ± 0.77

Terrace E 34 ± 2 10.8 ± 1.89

Terrace T5 25 ± 2 5.46 ± 0.77

Terrace F 27 ± 2 6.72 ± 1.01

Terrace T6 9 ± 0.5
> 1.17 ±
0.11
1
From Grenader et al.
(2014)

2
From Knuepfer (1992)

3
From Zinke et al. (2014a)

4
From Mason et al. (2006b) and references therein

317

Figure S5.

318

Figure S6a.

319

Figure S6b.

320

Appendix D: Highly variable latest Pleistocene–Holocene incremental slip rates on the
Awatere fault at Saxton River, South Island, New Zealand, revealed by lidar mapping and
luminescence dating

321

Figure S1.

322

Text S2.
Lidar collection and processing details. The lidar data we used to map the Saxton River site were
collected as part of a 300 km
2
acquisition along five sections of fault in the Marlborough fault
system, South Island, New Zealand, with acquisition funded by NSF Grant EAR-1321914 (Dolan).
These data are now available through OpenTopography (https://opentopography.org; Dolan and
Rhodes, 2016). The acquisition was completed in 2014 by the US National Center for Airborne
Laser Mapping (NCALM) and NZ Aerial Mapping using two different sensors for the Awatere
fault: an Optech 3100EA Airborne Laser Terrain Mapper (ALTM) operated with a pulse rate
frequency (PRF) of 70–100 kHz; and an Optech Gemini ALTM operated in Multi-Pulse mode
with a PRF of 125 kHz. These surveys were combined to produce a point cloud with a shot density
of ≥ 12 shots/m
2
along the length of the Awatere fault, and with a raw return density of ~18
shots/m
2
at the Saxton River site. The data were processed to make digital elevation models
(DEMs) and bare-earth digital terrain models (DTMs) using TerraSolid TerraScan Version 14.008
software, a surfer kriging algorithm, and other Perl, Python, and AML scripts. The DTMs used in
this study have a pixel size of 0.33 m.

323

Figure S3a.

324

Figure S3b.

325

Figure S3c.

326

Figure S3d.

327

Figure S3e.

328

Figure S3f.

329

Figure S3g.

330

Text S4. IRSL sample collection, processing, and analysis. Description of the IRSL dating process,
and two-step Bayesian age model.

S4.1 – IRSL sample collection and preparation
Samples were collected mostly in steel or aluminum tubes, pushed horizontally into
sections of sediment in excavated pits, with two samples collected without tubes by excavation at
night using dim amber LED lighting (SR14-22 and SR14-23 from terrace T1). Samples were
prepared at the University of California, Los Angeles using wet sieving to isolate the 175–200 µm
fraction, followed by a dilute HCl treatment to remove carbonate, rinsing and drying. K-feldspar
fractions were separated from quartz, plagioclase, and heavy minerals by floatation in a centrifuge
using a lithium metatungstate (LMT) solution of 2.565 or 2.58 g cm
-3
. Following copious rinsing
to remove residual LMT, samples were treated for 10 minutes in 10% HF to remove outer surfaces.
A few low yield samples were not treated in HF, and this was found to make no discernable
difference in equivalent dose for paired samples (one treated, one untreated) from northern Spain
(Lewis et al., 2017). Observations using binocular microscope suggests that the main effect of the
HF treatment was to remove dark grain coatings, presumed to absorb light (both stimulation and
emission). Samples treated in HF were dried and sieved again at 175 µm to remove heavily etched
or fractured grains.

S4.2 – IRSL measurement and dating
331

IRSL measurements were made using two Risø automated luminescence readers, both
fitted with a single grain XY dual laser unit. These comprised a standard TL-DA-20 D automated
luminescence reader based at UCLA and a newer TL-DA-20 CD fitted with a DASH automated
filter changer in Sheffield. On both instruments, IRSL measurements were made using a
BG3/BG39 filter combination, with transmission at around 340–470 nm. The UCLA instrument
uses an EMI 9235QB PMT, while the Sheffield machine has an Electron Tube 1” EMD-9107
PMT. IRSL stimulation was performed using a 150 mW 830 nm IR laser at 90% power for 2.5 s;
as recommended by the manufacturers, the laser beam was passed through a single RG-780 filter
mounted within the single-grain attachement to reduce the resonance emission at 415 nm. Vishay
TSFF 5210 870 nm IR diodes were used at 90% power for 40 s for the hot bleach treatment to
reduce overall measurement time. Table S5a (below) shows the single grain post-IR IRSL225
measurement protocol used; the measurement procedure was as described in detail in Rhodes
(2015). The protocol has been assessed thoroughly, using 34 samples with independent age
control, primarily from fault slip rate or paleoseismic contexts. A total of 13600 single grains of
K-feldspar were measured from Saxton River.

Table S4a. Single Aliquot Regenerative-dose (SAR) protocol steps and measurement conditions
used.

SAR step Measurement parameters

1 Beta irradiation 0 (Nat), 20, 6.4, 64, 200, 140, 0, 20 Gy in turn
2 Preheat 60s at 250°C
3 IRSL 1 2.5s 90% power at 50°C
4 IRSL 2 2.5s 90% power at 225°C
5 Beta test dose 8 Gy
6 Preheat 60s at 250°C
7 IRSL S1 Sensitivity measurement 2.5s 90% power at 50°C
332

8 IRSL S1 Sensitivity measurement 2.5s 90% power at 225°C
9 Hot bleach – then return to 1 40s IRSL (diodes) at 290

S4.3 – IRSL analysis and age calculation
The proportion of grains with sensitive post-IR IRSL signals measured at 225°C varied
between samples to a surprising degree. For grains without any response (with a detectable natural
post-IR IRSL signal greater than 3 sigma above background) we are unable to estimate an
equivalent dose value, but for most grains with signals above this limit we were able to construct
a growth curve by fitting a single exponential functions. Some grains were in saturations, that is,
the sensitivity-corrected natural signal was indistinguishable from the maximum fitted response,
and so we were not able to determine a finite equivalent dose estimate and associated uncertainty.
For other grains, following normal luminescence dating practice, we applied a series of rejection
criteria to select those grains with potentially meaningful equivalent dose values (see plots showing
apparent grain ages for each sample). We used relatively wide criteria because we have previously
found no evidence that more restrictive criteria lead to more precise or accurate combined
equivalent dose estimates, but instead these (samples analyzed with more restrictive criteria) tend
to suffer from lower precision and increased unwanted effects caused by small number of sensitive
outlying estimates (Rhodes, 2015). That is, it appears that the benefits of increasing the population
number (n) outweigh the potential ambiguities introduced by including dose estimates with larger
uncertainties. This approach is more conservative and efficient, as it includes a greater proportion
of the measured single age estimates. We consider that this is the case in part because of the
relatively well-bleached status of many of the samples measured at Saxton River (see plots of
apparent age for each sample, below).
333

For each grain, we add in quadrature an additional 15% to the measured individual
equivalent dose uncertainty estimate that is based solely on IRSL counting statistics and fitting
errors, to allow for the influence of different beta dose rates and variation in response to the dating
protocol between grains. These effects lead to overdispersion in single grain equivalent dose
estimates; the value of 15% is based on experience from quartz single grain estimates and appears
to provide age estimates in good agreement with independent age control from several hundred
years to tens of thousands of years (Rhodes, 2015). To determine the depositional age for each
sample, we first isolate those grains that were well bleached, and reject those with a residual dose
remaining, caused by incomplete signal zeroing by exposure to daylight during or shortly before
transport. In a few cases, several low dose values were interpreted as representing grains that were
introduced by post-depositional grain movement, or by very small amounts of sample
contamination by exposed grains during sample collection measurement.
We have plotted figures for each sample showing the different apparent age estimates for
each grain from the different samples (shown below). The combined equivalent dose estimate for
each sample was converted to depositional age (shown by the red dashed lines in the single grain
apparent age plots, below) by division by the attenuated total dose rate (Table S4b, below). Dose
rates were based on the sum of several contributions, including direct measurement of the gamma
dose rate in-situ using a calibrated EG&G mircroNomad NaI gamma spectrometer, combined with
beta dose rates calculated from U, Th, and K sediment concentration determinations using ICP-
MS (U, Th) and ICP-OES (K) plus an internal K contribution based on an assumed 12.5% K
content (Huntley and Baril, 1997). Beta dose rates were corrected for grain size and water
attenuation, and a cosmic dose rate contribution was based on depth using the approach of Prescott
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and Hutton (1994). A 2% systematic uncertainty to account for beta dose rate calibration error was
added in quadrature.

Table S4b. Measured post-IR IRSL225 age estimates, age estimation parameters, sample codes,
terrace feature, pit or location code, and sample depth from the ground surface.

Note all uncertainties represent 1 sigma, and that these are the measured age estimates, not the
results of the two step Bayesian age model.

S4.4 Results of single grain analysis for IRSL samples
Field Lab Feature Pit or Unit Depth Dose 1 sigma Equivalent 1 sigma Age 1 sigma
code code Location (m) rate uncertainty dose uncertainty (ka) uncertainty
(mGy.a
-1
) (Gy)
SR14-01 J0857 Terrace T3 Loc 14-16 Coarse gravel 0.48 3.74 ± 0.21 14.6 ± 0.7 3.91 ± 0.30
SR14-02 J0858 Terrace T3 Loc 14-16 Silty gravel 0.20 3.46 ± 0.21 17.1 ± 1.8 4.94 ± 0.61
SR14-03 J0859 Terrace T4 Loc 14-17 Fine sand 0.73 4.04 ± 0.23 27.3 ± 0.8 6.76 ± 0.43
SR14-04 J0860 Terrace T4 Loc 14-17 Sandy gravel 0.56 3.77 ± 0.20 17.2 ± 1.3 4.58 ± 0.43
SR14-05 J0861 Terrace T4 Loc 14-17 Sandy silt 0.43 3.78 ± 0.22 15.0 ± 0.5 3.98 ± 0.26
SR14-06 J0862 Terrace T2 Loc 14-18 Coarse sand 0.78 3.98 ± 0.22 36.1 ± 1.8 9.08 ± 0.67
SR14-07 J0863 Terrace T2 Loc 14-18 Silty sand 0.58 3.96 ± 0.22 33.5 ± 1.5 8.46 ± 0.61
SR14-08 J0864 Terrace T2 Loc 14-18 Sandy silt 0.34 3.56 ± 0.21 8.23 ± 2.09 2.31 ± 0.60
SR14-09 J0865 Terrace T2 Loc 14-18 Gravel 0.76 3.90 ± 0.21 34.3 ± 2.3 8.77 ± 0.76
SR14-10 J0866 Terrace T2 Loc 14-18 Gravel 0.86 3.77 ± 0.20 45.5 ± 3.0 12.1 ± 1.03
SR14-11 J0867 Terrace T1 Loc 14-19 Gravel 0.55 4.18 ± 0.24 50.6 ± 1.9 12.1 ± 0.82
SR14-12 J0868 Terrace T1 Loc 14-19 Gravel 0.58 4.30 ± 0.25 43.3 ± 4.1 10.1 ± 1.11
SR14-13 J0869 Terrace T1 Loc 14-19 Silt 0.27 3.50 ± 0.21 9.34 ± 1.00 2.67 ± 0.33
SR14-14 J0870 Terrace T2 Loc 14-20 Gravel 0.63 3.77 ± 0.21 29.2 ± 2.1 7.74 ± 0.71
SR14-15 J0871 Terrace T2 Loc 14-20 Gravel 0.74 3.85 ± 0.22 27.2 ± 2.0 7.06 ± 0.67
SR14-16 J0872 Terrace T2 Loc 14-20 Sandy gravel 0.99 3.74 ± 0.20 59.1 ± 5.0 15.8 ± 1.58
SR14-17 J0873 Terrace T2 Loc 14-20 Gravel 1.00 3.91 ± 0.22 56.5 ± 3.2 14.5 ± 1.16
SR14-18 J0874 Terrace T2 Loc 14-20 Sandy silt 0.46 3.49 ± 0.20 23.9 ± 2.0 6.86 ± 0.69
SR14-19 J0875 Terrace T5 Loc 14-21 Gravel 0.70 3.73 ± 0.20 15.3 ± 1.3 4.11 ± 0.41
SR14-20 J0876 Terrace T5 Loc 14-21 Gravel 0.75 3.63 ± 0.19 16.4 ± 1.2 4.52 ± 0.41
SR14-21 J0877 Terrace T5 Loc 14-21 Sandy silt 0.47 3.92 ± 0.22 16.4 ± 0.9 4.19 ± 0.33
SR14-22 J0878 Terrace T1 Loc 14-19 Gravel 0.80 3.76 ± 0.20 50.1 ± 3.1 13.3 ± 1.07
SR14-23 J0879 Terrace T1 Loc 14-19 Gravel 0.70 3.79 ± 0.20 37.9 ± 3.4 10.0 ± 1.05
SR14-24 J0880 Terrace T5 Loc 14-22 Sandy gravel 1.07 3.95 ± 0.23 34.1 ± 2.4 8.63 ± 0.80
SR14-25 J0881 Terrace T1 Loc 14-23 Sandy gravel 0.88 3.87 ± 0.21 46.0 ± 3.0 11.9 ± 1.02
SR14-26 J0882 Terrace T1 Loc 14-23 Sandy gravel 0.78 3.95 ± 0.22 57.2 ± 2.9 14.5 ± 1.11
SR14-28 J0884 Terrace T1 Loc 14-23 Sandy silt 0.30 3.52 ± 0.22 15.6 ± 1.2 4.43 ± 0.43
SR14-29 J0885 Terrace T6 Loc 14-24 Medium sand 0.37 3.95 ± 0.24 7.37 ± 1.27 1.87 ± 0.34
SR14-30 J0886 Terrace T6 Loc 14-24 Sandy silt 0.18 3.75 ± 0.26 7.65 ± 1.14 2.04 ± 0.33
SR14-31 J0887 Terrace T6 Loc 14-24 Gravel 0.60 3.62 ± 0.20 5.41 ± 0.88 1.50 ± 0.23
SR14-32 J0888 Terrace T4 Loc 14-25 Coarse sand 0.68 3.92 ± 0.22 20.3 ± 1.3 5.16 ± 0.44
SR14-33 J0889 Terrace T4 Loc 14-25 Sandy gravel 0.58 3.79 ± 0.21 16.2 ± 2.3 4.27 ± 0.66
SR14-34 J0890 Terrace T4 Loc 14-25 Silt 0.30 3.59 ± 0.22 8.32 ± 1.03 2.32 ± 0.32
SR17-01 J1269 Terrace T3 Loc 17-01 Gravel 0.58 3.68 ± 0.21 21.7 ± 1.5 5.90 ± 0.53
335

We have plotted figures for each sample showing the different apparent age estimates for
each grain from the different samples, as described above. We have selected to show these as
apparent age rather than as equivalent doses to make the plots easier to read. However, it should
be noted that the uncertainties on each apparent age estimate do not include dose rate measurement
uncertainty contributions, but just show the propagated IRSL measurement uncertainties plus the
15% overdispersion contribution described above. The dashed red lines in each plot show the
combined age estimate values for that sample; see Rhodes (2015) for guidance on the interpretation
for this type of plot.
In the caption for each of the plots, we have included the following information for each
sample in this order:
i. number of grains contributing to the combined age estimate (shown in red on the
accompanying plot)
ii. number of grains providing a finite age estimate (show in red or as open symbols on the
plot)
iii. total number of grains measured
iv. number of low dose grains omitted from the age estimate

Using these numbers, we have calculated the yield (Y; the proportion of grains providing a
meaningful equivalent dose result), what we consider the well-bleached fraction (WB; the
proportion of grains that are statistically consistent with a shared minimum equivalent dose value;
these are used to estimate the depositional age, and are shown in red), and in some cases a small
number of grains that we consider likely introduced post-depositionally, or by low levels of
336

contamination (RY; the proportion of grains considered to have anomalously low equivalent dose
estimates and omitted from age estimation – rejected young).

SR14-01 J0857 Age estimate: 3,910 ± 300 years 51/117/1200/1 (Y10%, WB44%, RY1%)

SR14-02 J0858 Age estimate: 4,940 ± 610 years 8/9/600/0 (Y2%, WB89%, RY0%)

0
5
10
15
20
25
30
35
40
0 20 40 60 80 100 120
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
2
4
6
8
10
12
14
16
18
20
0 1 2 3 4 5 6 7 8 9 10
Age (ka)
Grain number (rank order sensitivity, decreasing)
337

SR14-03 J0859 Age estimate: 6,760 ± 430 years 108/175/600/1 (Y18%, WB62%, RY1%)

SR14-04 J0860 Age estimate: 4,580 ± 430 years 10/58/400/0 (Y15%, WB17%, RY0%)

SR14-05 J0861 Age estimate: 3,980 ± 260 years 7/19/200/1 (Y10%, WB37%, RY5%)

0
10
20
30
40
50
60
70
80
0 20 40 60 80 100 120 140 160 180
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
5
10
15
20
25
30
35
40
0 2 4 6 8 10 12 14 16 18 20
Age (ka)
Grain number (rank order sensitivity, decreasing)
338

SR14-06 J0862 Age estimate: 9,080 ± 670 years 26/56/200/0 (Y28%, WB46%, RY0%)

SR14-07 J0863 Age estimate: 8,460 ± 610 years 29/55/200/0 (Y28%, WB53%, RY0%)

SR14-08 J0864 Age estimate: 2,310 ± 600 years 2/13/200/0 (Y7%, WB15%, RY0%)

0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
5
10
15
20
25
30
35
40
0 2 4 6 8 10 12 14
Age (ka)
Grain number (rank order sensitivity, decreasing)
339

SR14-09 J0865 Age estimate: 8,770 ± 760 years 12/26/400/0 (Y7%, WB46%, RY0%)

SR14-10 J0866 Age estimate: 12,070 ± 1,030 years 13/16/400/0 (Y4%, WB81%, RY0%)

SR14-11 J0867 Age estimate: 12,100 ± 820 years 47/51/400/2 (Y13%, WB92%, RY4%)

0
10
20
30
40
50
60
70
80
0 5 10 15 20 25 30
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 2 4 6 8 10 12 14 16 18
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60
Age (ka)
Grain number (rank order sensitivity, decreasing)
340

SR14-12 J0868 Age estimate: 10,100 ± 1,110 years 6/16/200/1 (Y8%, WB38%, RY6%)

SR14-13 J0869 Age estimate: 2,670 ± 330 years 10/14/200/0 (Y7%, WB71%, RY0%)

SR14-14 J0870 Age estimate: 7,740 ± 710 years 18/31/400/0 (Y8%, WB58%, RY0%)

0
10
20
30
40
50
60
70
80
0 2 4 6 8 10 12 14 16 18
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
2
4
6
8
10
12
14
16
18
20
0 2 4 6 8 10 12 14
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30 35
Age (ka)
Grain number (rank order sensitivity, decreasing)
341

SR14-15 J0871 Age estimate: 7,060 ± 670 years 13/37/400/0 (Y9%, WB35%, RY0%)

SR14-16 J0872 Age estimate: 15,800 ± 1,580 years 14/18/200/0 (Y9%, WB78%, RY0%)

SR14-17 J0873 Age estimate: 14,500 ± 1,160 years 23/28/200/0 (Y14%, WB82%, RY0%)

0
10
20
30
40
50
60
70
80
0 5 10 15 20 25 30 35 40
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 2 4 6 8 10 12 14 16 18 20
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 5 10 15 20 25 30
Age (ka)
Grain number (rank order sensitivity, decreasing)
342

SR14-18 J0874 Age estimate: 6,860 ± 690 years 11/13/200/0 (Y7%, WB85%, RY0%)

SR14-19 J0875 Age estimate: 4,110 ± 410 years 9/55/400/0 (Y14%, WB16%, RY0%)

SR14-20 J0876 Age estimate: 4,520 ± 410 years 13/36/600/2 (Y6%, WB36%, RY6%)

0
10
20
30
40
50
60
70
80
0 2 4 6 8 10 12 14
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 5 10 15 20 25 30 35 40
Age (ka)
Grain number (rank order sensitivity, decreasing)
343

SR14-21 J0877 Age estimate: 4,190 ± 330 years 24/64/400/1 (Y16%, WB36%, RY2%)

SR14-22 J0878 Age estimate: 13,300 ± 1,070 years 20/27/400/0 (Y7%, WB74%, RY0%)

SR14-23 J0879 Age estimate: 10,000 ± 1,050 years 11/32/400/0 (Y8%, WB34%, RY0%)

0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60 70
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 5 10 15 20 25 30
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 5 10 15 20 25 30 35
Age (ka)
Grain number (rank order sensitivity, decreasing)
344

SR14-24 J0880 Age estimate: 8,630 ± 800 years 10/29/200/0 (Y15%, WB34%, RY0%)

SR14-25 J0881 Age estimate: 11,900 ± 1,020 years 17/26/600/3 (Y4%, WB65%, RY12%)

SR14-26 J0882 Age estimate: 14,500 ± 1,110 years 23/39/600/7 (Y7%, WB59%, RY18%)

0
10
20
30
40
50
60
70
80
0 5 10 15 20 25 30
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
20
40
60
80
100
120
140
160
0 5 10 15 20 25 30
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
20
40
60
80
100
120
140
160
0 5 10 15 20 25 30 35 40 45
Age (ka)
Grain number (rank order sensitivity, decreasing)
345

SR14-28 J0884 Age estimate: 4,430 ± 430 years 13/16/200/0 (Y8%, WB81%, RY0%)

SR14-29 J0885 Age estimate: 1,870 ± 340 years 4/33/400/0 (Y8%, WB12%, RY0%)

SR14-30 J0886 Age estimate: 2,040 ± 330 years 5/28/200/0 (Y14%, WB18%, RY0%)

0
2
4
6
8
10
12
14
16
18
20
0 2 4 6 8 10 12 14 16 18
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 5 10 15 20 25 30 35
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 5 10 15 20 25 30
Age (ka)
Grain number (rank order sensitivity, decreasing)
346

SR14-31 J0887 Age estimate: 1,500 ± 230 years 9/19/600/0 (Y3%, WB47%, RY0%)

SR14-32 J0888 Age estimate: 5,160 ± 440 years 11/34/400/0 (Y9%, WB32%, RY0%)

SR14-33 J0889 Age estimate: 4,270 ± 660 years 5/59/600/0 (Y10%, WB8%, RY0%)

0
10
20
30
40
50
60
70
80
0 2 4 6 8 10 12 14 16 18 20
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 5 10 15 20 25 30 35
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60
Age (ka)
Grain number (rank order sensitivity, decreasing)
347

SR14-34 J0890 Age estimate: 2,320 ± 320 years 6/23/200/0 (Y12%, WB26%, RY0%)

SR17-01 J1269 Age estimate: 5,900 ± 530 years 12/24/400/1 (Y12%, WB50%, RY4%)

S4.5 – Bayesian age model development
We planned our IRSL sample collection strategy specifically to provide optimal control of
sediment deposition and erosion episodes at Saxton River. In contrast to some earlier slip rate or
paleoseismic studies that make use of luminescence sediment dating, we collected a higher number
of samples within this one extended site, and within each terrace unit. We used hand-dug pits rather
than natural exposures so that we could target features in the locations that we considered useful
for providing age estimates for this purpose. We made particular efforts to collect samples within
sand and gravel units themselves, rather than finer-grained cover sediments, as these can post-date
0
10
20
30
40
50
60
70
80
0 5 10 15 20 25
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 5 10 15 20 25
Age (ka)
Grain number (rank order sensitivity, decreasing)
348

the cessation of high-energy deposition by a considerable time. We rejected one sample whose
result appeared inverted (SR14-01; J0857) and for which we had noted sample collection problems
in the field.
We take advantage of the relatively close spacing of samples, and of their known
lithostratigraphic and morphostratigraphic relationships. To do this, we have constructed a two-
step Bayesian age model, following the principles pioneered for OSL dating by Rhodes et al.
[2003], using Markov chain Monte Carlo methods. In the first step, we analyzed each pit
individually, placing the samples into a Bayesian age model using depositional order (based on
the principle of superposition) to define a sequence using the radiocarbon calibration program
OxCal (Bronk Ramsey, 2001). Each pit was analyzed in this fashion including results from all
samples (except for SR14-01; see note above), to determine the age estimate uncertainty ranges
for each sample constrained by the IRSL measurements above and below them.
A second step Bayesian age model was constructed to take advantage of the
morphostratigraphic relationships between sampled terrace units recorded at the site evident from
the lidar-based digital terrain model (DTM). The simple morphostratigraphic model that we use
for this step comprises the deposition of a significant thickness of fluvial/alluvial sediments,
followed by gradual incision by the Saxton River into this body of sediment interspersed by periods
of relative stability during which thinner fluvial terrace deposits were laid down. It is these terrace
sediments that preserve the episodic record of cumulative fault slip, both by offsets of their internal
features (such as channels and bars) and of their eroded edges (terrace risers).
Our IRSL dating results demonstrate that we had in places dug through the terrace unit of
interest and had penetrated underlying, older sediments. This was clear based on a significant time
349

gap and these lowermost results lying out of a simple morphostratigraphic order for terrace
development. We used aa simple error in the difference analysis to identify these contexts,
rejecting older results based on a 2-sigma criterion. It is also clear that some of the cover sediments
deposited above the high-energy sand and gravel are younger than the incised terrace deposits at a
lower elevation. In this case, we interpret these sediments as overbank deposits from very large
floods after the incision has taken place, as locally deposited sediments from small temporary
streams crossing the abandoned terrace surface during high precipitation events, as eolian dust
deposition, or as some combination of these processes. Whichever of these pertained at each
location, these sediments do not provide us with a useful age constraint for the large-scale features
that provide offset markers preserved within the terrace and at its edge, although both may help
constrain the age of sand and gravel terrace sediments in step 1 of our age model.
To implement step 2, we construct a sequence within OxCal comprising each terrace unit,
based on the simple morphostratigraphic relationships described above. We omit samples from
older terrace units penetrated within pits and from cover sediments (silt and soil), including only
samples from the target terrace units made of sand and gravel. These sand and gravel samples have
already been constrained by the other samples within their individual pits in age model step 2
(lithostratigraphic constraint), so we now use the adjusted probability distributions (the output of
step 1) as the input for step 2 (morphostratigraphic constraint). We construct a phase for each
terrace unit containing each of the sand and gravel samples from that terrace unit. The use of a
phase means that the model is not sensitive to the order of these samples within each terrace within
the larger-scale morphostratigraphic (step 2) sequence. We analyze this step to derive posterior
age probability distributions for each sand and gravel sample and for the boundaries between them
(representing in some cases times of erosion and terrace riser construction). Note that the high-
350

resolution lidar data and detailed field observations allow us to select appropriate features for slip
rate calculations, and these are described in the main text of the paper.
For the age results at Saxton River, although one sample has a rather low individual
agreement index value of 40% (SR14-05, J0861, from terrace T5), the overall agreement index
from the step 2 age model is 86%, well above the acceptable limit of 60% (Bronk Ramsey, 2000).
This demonstrates the high degree of internal consistency with the age estimates from different
terraces, and the likely veracity of the age model. The sample and boundary ages determined from
our two-step Bayesian age model are reported in Fig. S4a and Table S4c, below.

351

Figure S4a. Age probability functions (PDFs) of the youngest sand and gravel samples in each
terrace surface, deposit ages, and boundary ages. Open PDFs are the prior distributions
determined for each sample age in step 1 of our Bayesian age model; black PDFs are the
“trimmed,” posterior distributions from step 2 of the Bayesian age model; magenta PDFs are
“deposit ages,” as explained above; blue PDFs represent the ages between deposits (modelled as
boundaries in OxCal).

Table S4c. Results of Bayesian age model step 2, and deposit ages.
352

Sample ages and boundary ages are 95%, 68%, and central value ages of the posterior
distributions from step 2 of our age model. “Deposit ages” were determined by summing the
PDFs of all samples from each youngest sand and gravel deposit.
353

Text S5.
Slip rate determinations
The displacement–time history of the Awatere fault at Saxton River was determined by
matching the offset recorded by each geomorphic feature with the feature age (see supplemental
Fig. S3 and Text S4). Incremental slip rates representing additional fault slip over the time between
formation of each offset marker were determined by Markov chain Monte Carlo analysis of the
offset and age probability density functions (PDFs), as explained in Text S7. A similar sampling
procedure (also explained in Text S7) was used to obtain a slip rate estimate for each individual
feature, assuming that was the only offset feature at the Saxton River site. We term this latter type
of slip rate determination an “individual” slip rate. All slip rate values are reported as 68 %
confidence intervals.

S5.1 T1/bedrock contact slip rate
The T1/bedrock contact (offset 72.5 ± 7.5 m) is a buttress unconformity, formed when
gravels aggraded throughout the Saxton River valley, filling the valley to the present-day elevation
of the T1 terrace tread. This aggradational period spanned c. 15.8 ± 1.6 to 12.9 +1.2/-1.0 kyb2017,
based on the oldest dated gravel exposures in Pit14-20, and Pit14-23. Streamflow during or
immediately prior to the latest stage of aggradation shaped the flank of the bedrock spur, and
gravels deposited along the flank formed a curvilinear contact with the bedrock (shown restored
in main text Fig. 2a; Fig. S3). Because the gravel deposition that formed the T1/bedrock contact
closely coincided with lateral trimming of the bedrock spur, the most recent aggradational T1
gravel deposits (i.e., gravels from Pit14-23 near the eastern edge of T1) provide a robust age on
354

the offset of the T1/bedrock contact. The 72.5 ± 7.5 m of offset recorded by the T1/bedrock contact
is therefore dated to 12.9 +1.2/-1.0 kyb2017. Assuming the T1/bedrock contact was the only offset
feature preserved at Saxton River (i.e., no younger incremental slip rate data were available), our
offset and age data yield an “individual” slip rate of 5.6 + 0.4/-0.3 mm/yr since 2017 C.E.

S5.2 T1 initial incision slip rate
The T1 terrace surface is characterized by a subtle down-to-the-west gradient, roughly
perpendicular to southward streamflow of Saxton River. Specifically, the T1 tread is ~2 m higher
near the T1/bedrock contact than it is near the T1/T2 riser, ~200 m to the west (see main text Fig.
3). We interpret this gradient to be inherent to the T1 floodplain, that is, after the peak of
aggradation that filled the Saxton River valley to the maximum elevation of T1 (~967 m at Pit14-
23), the river floodplain temporarily stabilized before incising to the level of T2A. During this
period of stability, stream flow gradually concentrated toward the middle of the valley, away from
the bedrock valley walls, producing the subtle E–W gradient, but forming no distinct terrace risers
within the T1 surface. Channel flow and deposition of the bedload gravels at the western edge of
T1 (dated by Pit14-19) therefore represents a later stage in T1 history than gravel deposition in the
higher, eastern portions of T1 (dated by Pit14-23). The latest stage of bedload gravel deposition
along the T1 terrace occurred immediately prior to the pronounced incision that formed the T1/T2
riser. As explained in supplementary Text S3, the restored far- field projection of the T1/T2 riser
and edge of the original T1 surface forms a smooth, curvilinear trend that may represent the edge
of the Saxton River at the time when it initially incised into T1. That offset of 70 +3/-16 m (with
a preferred range of 69–71 m) would have initiated as soon as incision into T1 began, and
355

reworking of the youngest T1 bedload gravels ceased (i.e., at 11.3 +1.4/-1.2 kyb2017, as dated by
the youngest gravel deposits in Pit14-19 in the western portion of T1).
As explained in the main text and supplementary Fig. S3, the T1/T2 riser and edge of the
original T1 surface north of the fault may have been modified by subsequent lateral fluvial erosion,
during the period when terrace T2 was the active floodplain. Any such erosion north of the fault
would enhance the apparent offset of the original T1 surface edge. We fully account for this
uncertainty by setting the lower limit of the probability density function for T1 initial incision to
54.0 m, the lowest sedimentologically plausible offset value for the younger T2A offset. This large
offset uncertainty allows for a range of slip rate behaviors between the time of youngest deposition
of bedload gravels adjacent to the T1/bedrock deposition at 12.9 +1.2/-1.0 kyb2017, and
abandonment of terrace T1 at 11.3 +1.4/-1.2 kyb2017. Incremental slip rate analysis as described
in S11 shows that the fault was likely slipping at ~ 4 mm/yr between initial incision into T1 and
deposition of the easternmost aggradational T1 bedload gravels at T1/bedrock terrace inner edge.
However, the similarity between our preferred offset estimate for the edge of T1 initial incision
(71 ± 2 m) and the offset of the T1/bedrock contact (72.5 ± 7.5 m) suggests that the fault was
moving slowly (~2 mm/yr) or not at all during this time. If this were the only feature preserved at
Saxton river, it would yield an individual slip rate of 5.8 ± 0.5 mm/yr.

S5.3 T2 bedforms slip rate
The T2 bedforms (offset 56 ± 2 m) are incised into T2A, but not into T2B. They are
therefore younger than T2A, but older than T2B. We therefore use the 8.1 ± 0.8 kyb2017 T2A/T2B
“boundary” age determined by OxCal to represent the age of these features. This yields an
356

incremental slip rate between the T2 bedforms and T1 deposition of 3.4 +1.0/-0.8 mm/yr. The
individual slip rate based solely on the T2 bedforms is 6.9 ± 0.4 mm/yr.

S5.4 T1/T2 riser slip rate
The T1/T2 riser (offset 45 ± 3 m) is bounded north of the fault by a channel at its base
that overprints the T2 bedforms described above, and which is incised into the T2B surface,
indicating that the riser morphology is slightly younger than, or similar in age to T2B (7.6 +0.7/-
0.8 kyb2017). As discussed in supplemental Fig. S3, a subtle channel margin west of the T1/T2
riser may also be offset 45 ± 1 m, further supporting our interpretation of an incision event that
occurred at that time. If incision of the channel along the base of the T1/T2 riser and the subtle
channel in T2B was simultaneous with final floodplain transport and deposition of the bedload
gravels on the T2B surface, then the offset features are equivalent in age to T2B. If, on the other
hand, formation of those channel features occurred after the final abandonment of T2B (e.g., by
overbank flooding during formation of T3) then the age of T2B provides a robust maximum on
the age of the T1/T2 riser. Even in the latter scenario, however, the true formation age of these
offset features is likely very close in age to the T2B terrace deposits, as the T2B/T3 riser underwent
very rapid incision after T2B abandonment (~7 m in ~2 ky; see main text Fig. 4d), and the high
river banks developed during T3 formation would have quickly posed an obstacle for the Saxton
River to overtop. For these reasons, we are confident in using the T2B terrace age as the date at
which the eroded T1/T2 riser and the similarly offset channel features formed. This yields an
incremental slip rate of 16.8 +28.2/-7.6 mm/yr between final trimming of the T1/T2 riser, and
development of the T2 bedforms. The T1/T2 riser and associated channels yields an individual slip
rate of 5.9 +0.4/-0.3 mm/yr.
357

S5.5 T2B/T3 riser slip rate
The T2B/T3 riser is offset 30.0 ± 2.5 m. This is significantly less than the least-offset
features on the upper, T2B terrace (described above), and the riser has a similar slope on either
side of the fault (see supplemental Fig. S3). The age of lower terrace T3 abandonment (5.6 +1.0/-
0.8 kyb2017) therefore dates the riser (Lensen, 1973; Cowgill, 2007), yielding an incremental slip
rate of 6.2 +3.4/-1.7 mm/yr between T2B/T3 riser offset and eroded T1/T2 riser/channel offset.
The T2B/T3 individual slip rate is 5.9 ± 0.6 mm/yr.

S5.6 T3-T4/T5 riser slip rate
The T3-T4/T5 riser is offset 12.5 +3.0/-1.5 m. We again use the lower-terrace abandonment
age (4.3 ± 0.3 kyb2017) to date the offset. This yields an incremental slip rate between the T3-
T4/T5 riser offset and T2B/T3 riser offset of 15.2 +9.6/-4.6 mm/yr, and an individual slip rate of
3.0 +0.3/-0.2 mm/yr. A set of channels incised into the lower, T5 tread are offset less than the T3-
T4/T5 riser. As described below, however, these are more likely to have resulted from overbank
flow during late-stage T6 occupation, and thus they do not have any bearing on the morphotectonic
history of the 5- to 8-m-tall T3-T4/T5 riser.

S5.7 T5/T6 riser slip rate
The T5/T6 riser is offset 9.5 ± 1.0 m. Subtle channels incised into the silt deposits on the
upper, T5 tread are offset approximately the same amount as the T5/T6 riser. Taken alone, this
observation might suggest that both the T5 tread offset, and the offset of the T5/T6 riser itself are
best dated by the age of the T5 surface. However, as described in supplemental Fig. S3, the riser
358

has a similar slope across the fault, exhibiting little evidence of diachronous formation that would
support an upper-terrace reconstruction (Cowgill, 2007). Instead, we suggest that it is more likely
that a flooding event occurred during T6 floodplain occupation, in which streamflow overtopped
the T5/T6 riser and incised shallowly into the very fine-grained, unconsolidated 40–50-cm-thick
silt deposits overlying the T5 terrace.
Further justification for using the lower, T6 terrace abandonment age comes from
comparison of the displacement–age data presented in this study, with the paleoseismic record at
the Saxton River site (presented by Mason [2004]) and smallest fault offsets recorded in the nearby
landscape (Mason, 2004; Zinke et al., 2016b). The paleoseismic record uncovered from the sag
pond in T1 reveals 3–4 surface ruptures since T6 abandonment (Mason, 2004). Assuming
relatively similar slip of ~2.5 m per event, based on nearby offset geomorphic features (Mason,
2004; Zinke et al., 2016b), this totals ~10 m of slip since c. 1.8 kyb2017. This matches our
preferred offset range for the T5/T6 riser if offset began accruing when the lower, T6 terrace was
abandoned c. 1.8 ka. The T3-T4/T5 riser is offset 3–5 m more than the T5/T6 riser, and has
experienced an additional 1–3 earthquakes based on the paleoseismic record (Mason, 2004). This
also matches what we would expect for riser offset since c. 4.3 kyb2017, assuming characteristic
slip in past earthquakes, and using a lower-terrace reconstruction for the T3-T4/T5 riser. The T5
channels are therefore most likely superficial, and do not indicate that the river had sufficient
stream power to either laterally trim the T3-T4/T5 riser or justify an upper-terrace reconstruction
for the T5/T6 riser. These observations are further discussed in supplemental Fig. S6. Using the
lower, T6 terrace abandonment age of 1.8 +0.4/-0.3 kyb2017 to date the 9.5 ± 1.0 m of offset
recorded by the T5/T6 riser yields an incremental slip rate of 1.4 +0.5/-0.4 mm/yr (averaged over
359

the interval between T5/T6 riser offset, and T3-T4/T5 riser offset). The individual slip rate
recorded by the T5/T6 riser is 5.2 ± 0.5 mm/yr.

S5.8 Most recent earthquake
The time period between the onset of T5/T6 offset accrual (dated by T6 abandonment at c.
1.8 kyb2017) and the present day represents an open seismic interval along the Awatere fault. We
use the age and displacement of the most recent event (MRE) at Saxton River to close that interval.
As noted above, paleoseismic evidence from Mason (2004) indicates that the Awatere fault at
Saxton River most recently experienced a surface rupturing earthquake post-440 cal. yr B. P. Based
on comparison of the paleoseismic record at Saxton River, with evidence for surface rupturing
events along other parts of the Awatere fault, Mason (2004) suggested that the MRE at Saxton
River was likely the 16 October 1848 M 7.4–7.5 Marlborough earthquake. We use this age (169
yb2017) and displacement in that event inferred from the least-offset nearby geomorphic features
(2.5 ± 1.0 m; Mason, 2004; Zinke et al., 2016b) to complete the most recent full earthquake cycle
on the Awatere fault at Saxton River. This yields an incremental slip rate of 4.2 +0.6/-0.5 mm/yr
between the MRE and offset of the T5/T6 riser.

360

Figure S6.

361

Text S7.
Our measurements of both displacement and age of the offset geomorphic features at
Saxton River have complex uncertainties, represented in this study as probability density functions
(PDFs). The complex, often asymmetric or multi-peaked nature of these PDFs needs to be
accommodated with regards to slip rate calculations. Additionally, tectonostratigraphic inferences
can be used to place constraints on “geologically allowable” fault slip rate behavior. Specifically,
the assumption that a fault at no point in its history slipped backward (left-laterally, in the case of
Saxton River) can significantly reduce uncertainties in determining the slip rate of the fault over a
given interval (Gold and Cowgill, 2011). To account for these factors, we used a Markov chain
Monte Carlo sampling scheme to determine incremental and individual slip rates.
For each geomorphic marker at Saxton River, our 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 (left-lateral) slip history, the sampled path
is rejected, and a new set of values is chosen (similar to Gold and Cowgill [2011]). This is done
iteratively until the desired number of sample paths is reached (10,000 for this study). Such a
scheme is especially appropriate in cases where incremental age or offset measurements overlap
within uncertainty (as is the case for several of the geomorphic markers at Saxton River) as it limits
the number of geologically allowable slip histories represented by a data set, thereby reducing
uncertainty in slip rates.
Unlike the sampling method used by Gold and Cowgill (2011) and Gold et al. (2017),
however, our approach samples each displacement and age measurement in a way that is
representative of the PDF for that measurement. This sampling approach allows for propagation
of uncertainties for both incremental and individual slip rate calculations. Slip rates and
362

uncertainties are reported as the 16th, 50th, and 84th percentiles of all geologically allowable
sample paths, representing the inner 68 % or approximately one standard deviation of the sample
data set.

Table S8.
Offset marker Offset (m)
a

Offset age
(kyb2017)
b

Individual slip rate
(mm/yr)
c

Incremental slip
rate (mm/yr)
d

T1/bedrock inner edge 72.5 ± 7.5
T1E (12.9 +1.2/-
1.0)
5.6 +0.4/-0.3

3.4 +1.0/-0.8
e

T1 initial incision 71 +2/-17
T1W (11.3 +1.4/-
1.2)
5.8 ± 0.5

T2 bedforms 56 +3/-2
T2A-T2B boundary
(8.1 ± 0.8)
6.9 ± 0.4

16.8 +28.2/-7.6
T1/T2 riser 45 ± 3 T2B (7.6 +0.7/-0.8) 5.9 +0.4/-0.3

6.2 +3.4/-1.7
T2B/T3 riser
33.5 +2.5/-
3.5
T3 (5.6 +1.1/-0.8) 5.9 ± 0.6

15.2 +9.6/-4.6
T3-T4/T5 riser
12.5 +3.0/-
1.5
T5 (4.3 +0.3/-0.4) 3.0 +0.3/-0.2

1.4 +0.5/-0.4
T5/T6 riser 9.5 ± 1.0 T6 (1.8 ± 0.3) 5.2 ± 0.5

4.2 +0.6/-0.5
MRE 2.5 ± 1.0 0.169

a
Most likely value ± minimum/maximum sedimentologically plausible estimates
b
Feature defining offset age (age of feature)

c
Slip rate assuming that was the only feature preserved at Saxton River (68% confidence limits)
d
Slip rate between offset markers. Uncertainties are 68% confidence limits

e
Incremental slip rate between T1/bedrock contact and development of the T2 bedforms

363

Appendix E: Multi-millennial incremental slip rate variability of the Clarence fault at the
Tophouse Road site, Marlborough fault system, New Zealand

364

Figure S1.

365

Figure S2.

366

Figure S3a.

367

368

Figure S3b.

369

370

Figure S3c.

371

372

Figure S3d.

373

Figure S3e.

374

Text S4. IRSL Sample Collection, Preparation, Processing, and Analysis
The following text closely follows the description of sample collection, preparation,
measurement and analysis provided in the supplementary material of Zinke et al. (2017). It is
included here for convenience, and includes a few differences.

S4.1 Sample Collection and Preparation
We dated the terraces and Channel Y at Tophouse Road by collecting infrared stimulated
luminescence (IRSL) samples from hand-dug pits over the course of three field seasons between
March 2012 and September 2017. Samples were collected from the pit faces using steel or
aluminum tubes, hammered horizontally into sedimentary deposits. In-situ NaI gamma
spectrometer measurements were made for most sample locations. Samples were prepared at the
University of California, Los Angeles, USA, and at the University of Sheffield, UK, using wet or
dry sieving to isolate the 175–200 or 180–212 µm size fraction. Samples were then treated using
dilute HCl to remove any carbonate, followed by rinsing and drying. K-feldspar fractions were
separated from quartz, plagioclase, and heavy minerals by floatation in a centrifuge using a heavy
liquid lithium metatungstate (LMT) solution with a density of 2.565 or 2.58 g cm
-3
. Samples were
then rinsed to remove residual LMT and some were treated for 10 min in 10% HF to remove outer
surfaces. A few low-yield samples were not treated in HF, but this was found to make no
discernable difference in equivalent dose estimate for paired samples similarly processed by Lewis
et al. (2017). Samples treated in HF were dried and sieved again at 175 or 180 µm to remove
heavily etched or fractured grains.

S4.2 IRSL measurement
375

Luminescence measurements were made using two Risø automated luminescence readers,
both fitted with a single grain XY dual laser unit. These instruments included a standard TL-DA-
20 D automated luminescence reader based at UCLA and a newer TL-DA-20 CD fitted with a
DASH automated filter changer in Sheffield. Measurements were made by both instruments using
a BG3/BG39 filter combination, with transmission at around 340–470 nm. The UCLA instrument
uses an EMI 9235QB photomultiplier tube (PMT), whereas the Sheffield machine uses an Electron
Tube 1” EMD-9107 PMT. IRSL stimulation was performed using a 150 mW 930 nm IR laser at
90% power for 2.5s. The beam was passed through a single RG-780 filter mounted within the
single-grain attachment to reduce resonance emission at 415 nm. Vishay TSFF 5210 870 nm IR
diodes were used at 90% power for 40 s for the hot bleach treatment to reduce overall measurement
time. Table S4a (below) shows the single grain post-IR IRSL225 measurement protocol used
(detailed in Rhodes, 2015). In total, 5700 grains were measured from Tophouse Road.

Table S4a. Single Aliquot Regenerative (SAR)-dose protocol steps and measurement conditions.
SAR step Measurement parameters
1 Beta irradiation 0 (Nat), 20, 6.4, 64, 200, 140, 0, 20 Gy in turn
2 Preheat 60 s at 250°C
3 IRSL 1 2.5 s 90% power at 50°C
4 IRSL 2 2.5 s 90% power at 225°C
5 Beta test dose 8 Gy
6 Preheat 60 s at 250°C
7 IRSL S1 sensitivity measurement 2.5 s 90% power at 50°C
8 IRSL S1 sensitivity measurement 2.5 s 90% power at 225°C
376

9 Hot bleach – then return to 1 40 s IRSL (diodes) at 290

S4.3 IRSL Analysis and Age Determination
As noted in other studies of single-grain IRSL dating in New Zealand sediments (e.g.,
Zinke et al., 2017), the proportion of grains with sensitive post-IR IRSL signals measured at 225°C
varied between samples. For grains without any response, but with a detectable natural post-IR
IRSL signal greater than 3 sigma above background levels, we were unable to estimate an
equivalent dose value. For most grains with signals above this limit, however, we were able to
construct a growth curve by fitting a single exponential function. Some grains were in saturation,
i.e., the sensitivity-corrected natural signal was indistinguishable from the maximum fitted
response, and so we were not able to determine a finite equivalent dose estimate and associated
uncertainty. For other grains, we applied a series of rejection criteria to select those grains with
potentially meaningful equivalent dose values, following normal single-grain luminescence dating
practices (single grain distributions are shown as plots in section S4.4 below). We used relatively
broad criteria for rejection because we have previously found no evidence that more restrictive
criteria lead to more precise or accurate combined equivalent dose estimates. Instead, the samples
analyzed with more restrictive criteria tended to suffer from lower precision and increased
unwanted effects caused by a small number of outlying age estimates.
For each grain, we add in quadrature an additional 15% uncertainty to the measured
individual equivalent dose uncertainty estimate that is based solely on IRSL counting statistics and
fitting errors, allowing for the influence of different beta dose rates and variations in response to
the dating protocol between different grains. These effects lead to overdispersion in single grain
377

equivalent dose estimates. The value of 15% is based on experience from quartz single grain
estimates, and appears to provide age estimates in good agreement with independent age control
over ages ranging from several hundred years to tens of thousands of years (Rhodes, 2015). To
determine the depositional age of each sample, we isolated the well-bleached grains and rejected
the grains with a residual dose remaining (caused by incomplete zeroing of the luminescence signal
by exposure to daylight during or shortly before transport). In a few cases, several low dose values
were interpreted as representing grains that were introduced by post-depositional grain movement,
or by very small amounts of sample contamination by exposed grains during sample collection
and measurement.
The equivalent dose estimates were converted into ages by dividing these by the attenuated
total dose rate for that sample (reported in Table S4b, below). Dose rates were based on the sum
of several contributors, including: (1) gamma radiation, measured in-situ using a calibrated EG&G
microNomad NaI gamma spectrometer; (2) beta radiation calculated from U, Th, and K sediment
concentrations determined using ICP-MS (U, Th) and ICP-OES (K); and (3) an internal K
contribution based on an assumed 12.5% K content (Huntley and Baril, 1997). Beta dose rates
were corrected for grain size and water attenuation, and a cosmic dose rate contribution was
estimated based on depth (Rhodes, 2015, and references therein). A 2% systematic uncertainty to
account for beta dose rate calibration error was added in quadrature.
For each sample, we combined the single grain age estimates into a depositional age of the
entire sample. The results for the Tophouse Road samples are reported in table S4b (below) and
shown in the figures in Section S4.4 (each figure shows the apparent age estimates for single grains
within a sample; the sample depositional ages are represented by red dashed lines).

378

Table S4b. Post-IR IRSL225 age estimates.

Field
code
Lab
code
Feature Pit # Unit Depth
(m)
Dose rate
(mGy.a
-1
)
± 1
sigma
Equivalent
dose (Gy)
± 1
sigma
Age
(kyb2018)
± 1
sigma
CR14-05 J0895 T1 terrace 14-27 Gravel 0.66 9.92 0.21 47.0 2.9 12.0 1.0
CR12-04 J0391 T2 terrace 12-13 Silt 0.30 3.14 0.18 14.8 1.7 4.72 0.60
CR12-05 J0392 T2 terrace 12-13 Silt 0.30 3.08 0.17 8.84 1.29 2.87 0.45
CR17-103 17171 T2 terrace 17-14 Gravel 0.36 3.67 0.23 44.3 2.2 12.1 1.0
CR17-102 17170 T2 terrace 17-14 Gravel 0.38 3.67 0.22 54.4 3.1 14.8 1.2
CR17-101 17169 T2 terrace 17-14 Gravel 0.55 3.64 0.22 51.2 2.5 14.0 1.1
CR12-03 J0390 T3 terrace 12-12 Silt 0.30 3.56 0.21 16.1 1.7 4.53 0.55
CR12-02 J0389 T3 terrace 12-12 Sand 0.48 3.72 0.20 33.0 1.5 8.86 0.62
CR12-01 J0388 T3 terrace 12-12 Sand 0.58 3.83 0.21 33.3 2.4 8.69 0.79
TR17-01 J1271 T4 terrace 17-01 Silt 0.33 3.99 0.24 30.5 2.5 7.65 0.78
TR17-02 J1272 T4 terrace 17-01 Silt 0.38 3.98 0.27 29.3 4.1 7.35 1.14
TR17-03 J1273 T4 terrace 17-01 Gravel 0.58 3.98 0.22 34.3 1.9 8.62 0.68
CR12-07 J0394 Channel Y 12-14 Sand 0.48 3.22 0.17 13.9 1.4 4.43 0.50
CR12-06 J0393 Channel Y 12-14 Sand 0.75 3.20 0.16 23.5 1.0 7.35 0.47
CR14-04 J0894 Channel Y 14-26 Silt 0.15 2.93 0.23 3.12 0.93 1.06 0.33
CR14-03 J0893 Channel Y 14-26 Silt 0.31 3.10 0.17 10.3 1.0 3.32 0.38
CR14-02 J0892 Channel Y 14-26 Silt 0.44 3.44 0.19 14.6 1.1 4.25 0.39
CR14-01 J0891 Channel Y 14-26 Gravel 0.52 3.46 0.19 14.4 1.3 4.16 0.45

Depositional ages are reported in thousands of years before 2018 (kyb2018). All uncertainties are
± 1 sigma. Note, these are the independent sample age estimates, not the result of our two-step
Bayesian age model, described below.

379

S4.4 Results of Single Grain Dating Procedure for IRSL Samples
Below are figures for each IRSL sample showing apparent age estimates for each grain
(black circles and whiskers) and the sample depositional age (red dashed line), calculated as
described in S4.3 (above). Note that the single grain ages are shown as apparent age, rather than
equivalent dose in order to make the plots easier to read. The uncertainties on each apparent age
estimate therefore do not include dose rate measurement uncertainty (as this is common to all
grains from a single sample), just the propagated IRSL measurement uncertainties plus the 15%
overdispersion contribution described above. See Rhodes (2015) for guidance on the interpretation
of this type of plot.
In the caption of each plot, we include the following information for each sample (in order):
i. number of grains contributing to the combined age estimate (shown in red on the
accompanying plot)
ii. number of grains providing a finite age estimate (shown in red or as open symbols on the
plot)
iii. total number of grains measured
iv. number of low dose grains omitted from the depositional age estimate
Based on these numbers, we calculated: (1) the yield (Y; the proportion of grains providing a
meaningful equivalent dose result); (2) what we consider to be the well-bleached fraction (WB;
the proportion of grains that are statistically consistent with a shared minimum equivalent dose
value – these are used to estimate the depositional age of the sample and are shown in red); and
(3) a small number of grains that we consider likely introduced post-depositionally, or by low
levels of contamination (RY; the proportion of grains considered to have anomalously low
equivalent dose estimates and omitted from age estimation, i.e., rejected as being too young).
380

Pit 14-27 (T1 terrace)
Sample CR14-05 (J0895) Age estimate: 12,000 ± 1,000 yb2018
19/31/100/0 (Y 31%, WB 61%, RY 0%)

Pit 12-13 (T2 terrace)
Sample CR12-04 (J0391) Age estimate: 4,720 ± 600 yb2018
17/37/500/0 (Y 7%, WB 49%, RY 0%)

Sample CR12-05 (J0392) Age estimate: 2,870 ± 450 yb2018
5/12/500/0 (Y 2%, WB 42%, RY 0%)
0
10
20
30
40
50
60
70
80
90
100
0 5 10 15 20 25 30 35
Age (kyr)
Grain number (rank order sensitivity, decreasing)
0
5
10
15
20
25
30
35
40
0 5 10 15 20 25 30 35 40
Age (kyr)
Grain number (rank order sensitivity, decreasing)
381

Pit17-14 (T2 terrace)
Sample CR17-103 (17171) Age estimate: 12,100 ± 1,000 yb2018
32/62/200/0 (Y 31%, WB 52%, RY 0%)

0
5
10
15
20
25
30
35
40
0 2 4 6 8 10 12 14
Age (ka)
Grain number (rank order sensitivity, decreasing)
0
20
40
60
80
100
0 10 20 30 40 50 60 70
Age (kyr)
Grain number (rank order sensitivity, decreasing)
382

Sample CR17-102 (17170) Age estimate: 14,800 ± 1,200 yb2018
27/48/200/0 (Y 24%, WB 56%, RY 0%)

Sample CR17-101 (17169) Age estimate: 14,000 ± 1,100 yb2018
36/59/200/0 (Y 30%, WB 61%, RY 0%)

0
20
40
60
80
100
0 10 20 30 40 50
Age (kyr)
Grain number (rank order sensitivity, decreasing)
0
20
40
60
80
100
120
0 10 20 30 40 50 60
Age (kyr)
Grain number (rank order sensitivity, decreasing)
383

Pit 12-12 (T3 terrace)
Sample CR12-03 (J0390) Age estimate: 4,530 ± 550 yb2018
10/53/200/0 (Y 27%, WB 19%, RY 0%)

Sample CR12-02 (J0389) Age estimate: 8,860 ± 620 yb2018
35/55/200/1 (Y 28%, WB 64%, RY 2%)

0
10
20
30
40
50
60
70
80
90
100
0 10 20 30 40 50 60
Age (kyr)
Grain number (rank order sensitivity, decreasing)
0
10
20
30
40
50
60
70
80
0 10 20 30 40 50 60
Age (kyr)
Grain number (rank order sensitivity, decreasing)
384

Sample CR12-01 (J0388) Age estimate: 8,690 ± 790 yb2018
17/26/200/0 (Y 13%, WB 65%, RY 0%)

Pit 17-01
Sample TR17-01 (J1271) Age estimate: 7,650 ± 780 yb2018
13/21/400/0 (Y 5%, WB 62%, RY 0%)

0
10
20
30
40
50
60
0 5 10 15 20 25 30
Age (kyr)
Grain number (rank order sensitivity, decreasing)
0
20
40
60
80
100
120
0 5 10 15 20 25
Age (kyr)
Grain number (rank order sensitivity, decreasing)
385

Sample TR17-02 (J1272) Age estimate: 7,350 ± 1,140 yb2018
7/11/600/0 (Y 2%, WB 64%, RY 0%)

Sample TR17-03 (J1273) Age estimate: 8,620 ± 680 yb2018
21/60/1200/0 (Y 5%, WB 35%, RY 0%)

0
20
40
60
80
100
120
0 2 4 6 8 10 12
Age (kyr)
Grain number (rank order sensitivity, decreasing)
0
20
40
60
80
100
0 10 20 30 40 50 60 70
Age (kyr)
Grain number (rank order sensitivity, decreasing)
386

Pit 12-14 (Channel Y)
Sample CR12-07 (J0394) Age estimate: 4,430 ± 500 yb2018
10/28/200/0 (Y 14%, WB 36%, RY 0%)

Sample CR12-06 (J0393) Age estimate: 7,350 ± 470 yb2018
55/66/200/6 (Y 33%, WB 83%, RY 9%)

0
10
20
30
40
50
60
70
80
0 5 10 15 20 25 30
Age (kyr)
Grain number (rank order sensitivity, decreasing)
0
5
10
15
20
25
30
35
40
0 10 20 30 40 50 60 70
Age (kyr)
Grain number (rank order sensitivity, decreasing)
387

Pit 14-26 (Channel Y)
Sample CR14-04 (J0894) Age estimate: 1,060 ± 330 yb2018
6/11/200/0 (Y 6%, WB 55%, RY 0%)

Sample CR14-03 (J0893) Age estimate: 3,320 ± 380 yb2018
16/19/200/0 (Y 10%, WB 84%, RY 0%)

0
10
20
30
40
50
60
0 5 10 15 20
Age (kyr)
Grain number (rank order sensitivity, decreasing)
0
2
4
6
8
10
12
0 2 4 6 8 10 12
Age (kyr)
Grain number (rank order sensitivity, decreasing)
388

Sample CR14-02 (J0892) Age estimate: 4,250 ± 390 yb2018
17/33/200/0 (Y 17%, WB 52%, RY 0%)

Sample CR14-01 (J0891) Age estimate: 4,160 ± 450 yb2018
10/15/200/0 (Y 8%, WB 67%, RY 0%)

0
5
10
15
20
25
30
35
40
45
0 5 10 15 20 25 30 35
Age (kyr)
Grain number (rank order sensitivity, decreasing)
0
2
4
6
8
10
12
14
16
18
20
0 2 4 6 8 10 12 14 16
Age (kyr)
Grain number (rank order sensitivity, decreasing)
389

S4.5 Bayesian Age Model Approach
Our sampling strategy at Tophouse Road was specifically designed to provide optimum
age control for the preserved offset markers. Importantly, we targeted stratigraphic units
representing periods of high energy streamflow during which the river was capable of modifying
the offset markers.
Based on our sedimentologic and geomorphologic interpretations of the site, we take
advantage of the lithostratigraphic and morphostratigraphic relationships between different
sampled units to more precisely constrain the sample ages. Specifically, we constructed a two-step
Bayesian age model following the principles established by Rhodes et al. (2003) and later applied
to the terrace sequence at nearby Saxton River by Zinke et al. (2017). This workflow uses the
Markov chain Monte Carlo approach implemental in the radiocarbon calibration program OxCal
(v. 4.3; Bronk Ramsey, 2001, 2017; see Rhodes et al., 2003 for application to luminescence data).
The first step of our Bayesian statistical model modifies (“trims”) the sample age
probability distributions by accounting for the depositional history of samples within each pit or
set of pits common to a terrace surface. Specifically, the ages are trimmed based on their relative
stratigraphic ordering, with the assumption that stratigraphically higher samples are younger than
stratigraphically lower samples (the principle of superposition). This is carried out by ordering the
samples as a sequence in OxCal. The net result of this first step is that high-energy gravel and sand
sample ages are trimmed by the overlying, younger silt sample ages that represent periods of low-
energy streamflow (our lithostratigraphic observations).
The second step of our Bayesian age model takes advantage of the morphostratigraphic
relationships among the terraces and channels at the site (evident in the lidar-based digital elevation
model and described in the main text). In step 2 of our age model, we trimmed the ages of the
390

high-energy gravel and sand samples within each morphostratigraphic unit (terrace or channel)
relative to the those in neighboring units. We implemented step 2 by constructing a sequence
within OxCal comprising each terrace unit (and Channel Y). We omit samples from overyling,
low-energy sediments (silt and soil). We also found that some sampled units might represent older,
underlying gravel units. The specific inclusion or omission of samples representing potentially
older terrace terrace deposits is discussed in Section S4.6, below. The gravel and sand samples
representing main-phase occupation of each terrace floodplain were already constrained by other
samples within the individual pits (the lithostratigraphic constraints in step 1). Now, we used the
adjusted probability distributions (the output of step 1) as inputs in step 2. We constructed a phase
for each terrace or channel unit containing the sand and gravel sample ages. The model is not
sensitive to the ordering of samples within each phase. We then assembled the phases within a
larger sequence, based on their morphostratigraphic order (discussed above and in the main text).
Simultaneously, we modeled the intervals of time between gravel ages as “dates” in OxCal. The
posterior age distributions resulting from step 2 precisely constrain the ages of high-energy
streamflow and deposition represented by the gravel and sand samples, and give a clear
representation of the time intervals between those sampled periods.

S4.6 Bayesian Age Model Application and Results at Tophouse Road
Here we discuss how we applied our age modeling procedure to the specific stratigraphy
at Tophouse Road. We discuss our observations and their implications in the context of the two-
step process.

Step 1
391

There is little or no ambiguity in the lithostratigraphic relationships between deposits
preserved at Tophouse Road, allowing us to proceed with a single straightforward interpretation
of the sample ages in step 1. Refer to Figure 3 in the main text and Table S4b (above) for
unmodeled samples ages and stratigraphic context.
For the T1 terrace, the gravel sample (CR14-05) from Pit 14-27 had no neighboring
samples. The age distribution of CR14-05 was therefore unmodified by step 1.
For the T2 terrace, we consider samples from Pit 12-13 and Pit 17-14 to represent a single,
stratigraphically consistent sequence of deposits. Samples CR17-101 through -103 were collected
from gravel deposits uncovered in Pit 17-14. The pre-step 1 ages of samples CR17-101 (14.0 ± 1.1
kyb2018; 55 cm depth) and CR17-102 (14.8 ± 1.2 kyb2018; 38 cm depth) are slightly inverted,
relative to their stratigraphic ordering, but agree within their 1-sigma uncertainties. Both appear
potentially older than the stratigraphically highest gravel sample CR17-103 (12.1 ± 1.0 kyb2018;
36 cm depth). This age-depth relationship could be explained by one of two scenarios: (1) all three
samples from Pit 17-14 represent the same age, since they overlap within 2-sigma error; or (2)
samples CR17-101 and -102 sampled an older deposit, such as an underlying aggradational “fill”
gravel deposit. If samples CR17-101 and -102 indeed sampled the fill gravels, they agree with the
approximate age (c. 14 ka) of a regionally extensive post-Last Glacial Maximum aggradational
event observed throughout much of South Island (e.g., Bull and Knuepfer, 1987), including at the
Saxton River site (Zinke et al., 2017, and references therein). Due to the age uncertainties,
however, we cannot be certain whether these apparently older gravels sample the fill deposits
specifically, or not. This leads to some minor speculation, discussed in more depth below.
Regardless of their regional stratigraphic context, we modeled the gravel samples from Pit
17-14 as part of a sequence in step 1. We also used silt sample ages, which overlie gravels in the
392

nearby Pit 12-13, to trim the gravel ages in Pit 17-14. The silt samples (CR12-04 and CR12-05)
were collected from roughly equivalent depths (~30 cm), and we therefore model them in
stratigraphic order. The lithostratigraphic sequence defined in step 1 of our age model
encompasses all the T2 samples. In order from stratigraphically lowest and oldest, to
stratigraphically highest and youngest, these are: CR17-101, CR17-102, CR17-103, CR12-04,
CR12-05.
For the T3 terrace, we excavated a single pit (Pit 12-12) from which we collected three
samples. We recovered two samples (CR12-01, CR12-02) from a thick sand horizon capped by a
pebbly stone layer. We interpret these two samples to represent the last stages of high-energy
streamflow across the T3 terrace floodplain. Their ages (CR12-01: 8.7 ± 0.8 kyb2018, 58 cm depth;
and CR12-02: 8.9 ± 0.6 kyb2018, 48 cm depth) appear to be inverted, but agree within 1-sigma
error. The sand and gravel samples in Pit 12-12 are overlain by silt, from which we collected
sample CR12-03 (4.5 ± 0.6 kyb2018). We modeled all three of these samples in stratigraphic order
as part of a single sequence.
For the T4 terrace, we collected three samples from Pit 17-01. The stratigraphically lowest
of these samples (TR17-03: 8.6 ± 0.7 kyb2018) comes from a gravel unit. Two stratigraphically
higher samples were collected from an overlying silt deposit (TR17-02, -03). We modeled these
three samples in stratigraphic order.
We excavated two pits into the Channel Y deposits. These pits were located near the
channel thalweg, separated from each other by ~3 m distance. Despite their proximity, they
recorded slightly different stratigraphic relationships: Pit 12-14 comprised a relatively thick (~90
cm) silt deposit, overlying a ~10-cm-thick sand deposit, which in turn capped gravels; in contrast,
the stratigraphic sequence in Pit 14-26 comprised only ~50 cm of silt directly overlying gravels.
393

Because of these differences, we chose to model the two pits separately in step 1. First, we modeled
the two samples (CR12-06, -07) from Pit 12-14 in stratigraphic order. Then, we modeled the four
samples (CR14-01–04) from Pit 14-26 in stratigraphic order. Samples CR14-01 (4.2 ± 0.5
kyb2018; 52 cm depth) and CR14-02 (4.3 ± 0.4 kyb2018; 44 cm depth) ages show a very slight
apparent age inversion, but the ages overlap within 1-sigma error.

Step 2
The morphostratigraphic relationships between terraces T1–T4 and Channel Y at Tophouse
Road allowed us to more precisely constrain the ages of the gravel and sand samples representing
high-energy streamflow deposits, when the Clarence River was capable of laterally trimming its
banks. As discussed in the main text, the morphostratigraphic history of the Tophouse Road site is
well-described by a simple fill-and-cut terrace model (e.g., Bull and Knuepfer, 1987) in which
significant deposits of gravel (“fill gravel”) accumulated throughout Clarence River valley that
were subsequently incised (“cut”) by the continued streamflow, punctuated by periods of relative
floodplain stability. These episodes of stability are represented by the sand and gravel deposits we
sampled. Channel Y cross-cuts and therefore post-dates the younger T3 and T4 terraces. Gravel
and sand deposits within the channel sediments represent periods of high-energy streamflow
through the channel. We apply the information gained from these morphostratigraphic
interpretations to our IRSL ages by ordering the gravel and sand deposit samples within a
sequence.
Step 2 of our Bayesian age model is thus relatively straightforward: The T1 floodplain
deposits are older than T2 floodplain deposits, and so forth. The only significant source of
ambiguity stems from how we interpret the gravel ages (CR17-101–103) from Pit 17-14 in the T2
394

terrace. All three sample ages overlap within 2-sigma error and may therefore represent a single
deposit of the same age. Given that samples CR17-101 and -102 appear consistently older than the
uppermost sample CR17-103, however, one might infer that they represent an older gravel deposit
that is chronologically distinct from the most recent floodplain gravels (dated to 12.1 ± 1.0
kyb2018 by CR17-103). We therefore test three distinct scenarios for step 2 of our age model, in
which: (1) all three Pit 17-14 samples are included as part of the same depositional phase defining
the youngest floodplain deposits of T2; (2) the lower and apparently older samples CR17-101 and
-102 are part of the underlying fill gravels, and were therefore deposited before development of
the T1 floodplain (at c. 12.0 ka, as represented by sample CR14-05); or (3) samples CR17-101 and
-102 pre-date T1 floodplain development, but cannot be specifically linked to the fill gravels, and
therefore have no bearing on the T1 floodplain age or the T2 floodplain age beyond step 1.
We tested each of these three scenarios in OxCal. The first scenario, in which samples
CR17-101 and -102 were included with CR17-103 as part of the same phase of T2 floodplain
occupation, led to very poor model agreement in step 2 (Amodel = 42.3%; Aoverall = 51%). The usual
acceptance level for this agreement index is 60% (Rhodes et al., 2003). The low agreement is
caused by samples CR17-101 and -102 apparently being significantly older than T1 sample CR14-
05.
The second scenario provides a sedimentologic explanation for the apparently older ages,
ascribing them to the underlying fill gravels into which the T2 floodplain “cut” or incised. Because
these gravels are stratigraphically beneath, and older than, the T1 floodplain, we use them to trim
the T1 age in this model. This yields an excellent agreement index in step 2 (Amodel = 103%; Aoverall
= 104%). This is our preferred model, which we report in the main text.
395

In the third scenario, we assume that the CR17-101 and -102 ages are truly older than the
T2 floodplain (dated solely by CR17-103 in this case), but we do not consider them in a broader,
morphostratigraphic context. We therefore exclude them from step 2 of our age model entirely.
This yields a good agreement index of Amodel = 94.4%, and Aoverall = 95%. We consider this as an
alternative model only in this data repository, as it makes only a small difference to the oldest
T1/T2 riser to T2/T3 riser incremental slip rate (9.6 +5.0/-2.5 mm/yr for our preferred age model
versus 10.0 +5.7/-3.0 mm/yr for our alternative age model). No difference is made to the younger
slip rates.

S4.7 Feature Age Determinations
The youngest bedload sand and gravel sample ages resulting from step 2 represent the final
stages of high-energy streamflow across each terrace, or through a channel. We refer to these final
stages of high-energy floodplain occupation as “abandonment ages”. In the T3 Pit 12-12, samples
CR12-02 and CR12-01 represent roughly the same age, and are therefore added to form a
conservative, but robust age estimate. In the figure and table below, we show the results of our
two-step Bayesian age model.

396

Figure S4a. Posterior age distributions from Step 2 of our model. Red and orange curves are
modeled ages of gravel and sand samples, respectively. The gray curves they overlie are post-
step 1 sample age distributions that served as prior constraints in step 2. Purple curves are OxCal
sequence boundaries (“young” and “old”), and hypothetical distributions representing the time
intervals between phases (e.g., “T1-T2” is the time interval between the T1 terrace age and the
T2 terrace age). T3 combo is the sum of T3 samples CR12-01 and -02. “Aggr-Degr” is the
interval between the age of the aggradational “fill” terrace, and abandonment of the T1
floodplain.

397

Table S4c. Modeled (post-step 2) ages of sand and gravel ages, sequence boundaries, and
intervals between depositional units at Tophouse Road.

Age (kyb2018)
Geomorphic
feature Sample
2-sigma
low
1-sigma
low
Most
likely
value
1-sigma
high
2-sigma
high
Old 13.1 13.8 14.7 15.7 17.4
Fill terrace
CR17-101 12.9 13.4 14.1 14.8 15.6
CR17-102 12.6 13.2 13.8 14.5 15.2
Aggradational-
Degradational interval
11.7 12.4 13.1 13.7 14.4
T1 terrace CR14-05 11.1 11.7 12.3 13.0 13.6
T1-T2 interval 10.4 11.1 11.8 12.5 13.1
T2 CR17-103 9.9 10.6 11.2 11.9 12.5
T2-T3 interval 8.9 9.4 10.4 10.9 11.7
T3
T3 combo 8.1 8.5 9.0 9.4 10.0
CR12-01 8.2 8.6 9.1 9.6 10.1
CR12-02 8.1 8.5 8.9 9.2 9.7
T3-T4 interval 7.7 8.1 8.4 8.8 9.2
T4 TR17-03 7.4 7.7 8.1 8.5 8.9
T4-Channel Y interval 6.6 7.1 7.6 8.0 8.5
Channel Y CR12-06 6.1 6.6 6.9 7.4 7.8
398

CR14-01 3.8 4.1 4.5 4.9 5.3
Young 1.7 3.4 4.2 4.8 5.3

Ages reported in kyb2018; 1-sigma (68.27%) and 2-sigma (95.45%) limits are based on the
highest posterior density likelihood estimates (See Figure S4a).

399

Text S5.
Markov chain Monte Carlo sampling methodology
Our method for determining incremental slip rates (included as the script
“MCMC_incr_slip_rates.py” for Python 2) uses a Markov Chain Monte Carlo (MCMC) approach
to sample estimates of the age and offset recorded by progressively displaced, dated geomorphic
markers. It assumes that the fault at no point reversed its sense of displacement (e.g., from right-
lateral to left-lateral) relative to its long-term kinematics. The result of randomly sampling many
possible paths yields the incremental slip rates between stratigraphically adjacent geomorphic
markers, expressed as probability density functions (PDFs). This methodology, described below,
can be applied generally to any group of geomorphic markers with a shared tectonic history. It
improves upon an earlier methodology proposed by Gold and Cowgill (2011), in that it samples
the displacement and age data according to the specific shape of each PDF (Zinke et al., 2017),
thereby allowing accurate propagation of uncertainties through the statistical model.

S5.1 – Sampling marker age and offset PDFs
For each geomorphic marker, our routine accepts a PDF describing the marker age, and a
PDF describing the marker offset. The PDFs can be of any arbitrary shape, so long as the age or
offset is represented by a continuous series of values (e.g., 1000-2000 ka) with finite, non-negative
likelihoods. For example, the age of a geomorphic marker may take the form of a posterior
distribution from a Bayesian age model such as OxCal (as demonstrated in this study). Similarly,
the marker offset may be represented by a “goodness of fit” function based on topographic
expression (e.g., from LaDiCaoz [Zielke et al., 2012, 2015]). This offers a fundamental
400

improvement over the methodology of Gold and Cowgill (2011), in which marker ages and offsets
are expressed as uniform distributions encompassing the inner ~95% confidence limits of each,
resulting in loss of resolving power.
The age and offset PDFs are sampled via the inverse transform method, whereby uniform
samples of the cumulative distribution function (CDF) are mapped to values in the PDF domain.
We illustrate the method below for an age or offset estimate x, chosen with normally distributed
errors (though the method is suitable for sampling any non-parametric PDF). Note that uniform
sampling of the CDF (left) allows us to retrieve normally distributed samples in x (bottom).

401

Figure S5a. Red line is CDF representing age or offset. 100,000 uniformly distributed samples
(left histogram) of the CDF map into normally distributed sample values of the PDF (bottom
histogram).

S5.2 – Slip rate sampling
An offset-age “path” consists of age and offset samples randomly selected for each
geomorphic marker. The MCMC routine checks that no negative slip rates are represented by an
offset-age path (i.e., sample ages do not get younger, or less offset). This approach is the same as
that proposed by Gold and Cowgill (2011) and is illustrated below. Paths with negative slip rates
are rejected by our routine, and sampling continues until the desired number of sample paths n is
reached. It should be noted that offset-age paths representing “zero” slip rates (i.e., no difference
in offset between samples) are allowed, as are paths representing infinite slip rates (i.e., no
difference in age between samples). From the n valid (non-negative) offset-age paths we calculate
incremental slip rates as simply the difference in offset (∆u) divided by the difference in age (∆t)
between adjacent markers. This yields n slip rate samples for each pair of adjacent markers.

402

Figure S5b. Offset and age data for a hypothetical set of displaced and dated geomorphic features.
Blue boxes represent the 95% confidence limits for the offset and age determined for each feature.
Blue circles are random offset-age samples; black line connecting blue dots in each panel is an
offset-age path. All the above examples of offset-age paths are valid (non-negative) except in the
lower-right panel where the T1/T2 riser and the T2/T3 riser samples create a negative incremental
slip rate between those markers. Such instances are tossed out.

S5.3 – Slip rate reporting
For very large values of n, these incremental slip rate samples constitute a distribution of
possible slip rates that represent the input PDFs, and the applied constraint that negative slip rate
403

paths are not tectonically allowable. These incremental slip rate distributions can be represented
in a number of ways. The simplest is to report percentiles of the slip rate samples, as done for the
displaced terrace sequence at Saxton River by Zinke et al. (2017).
A more meaningful way of expressing the incremental slip rate probabilities, however, is
by expressing them as PDFs. This involves representing a set of discrete values (slip rate samples)
as continuous functions. Our “MCMC_incr_slip_rates.py” routine includes two ways of doing
this: (1) by binning as histograms; and (2) by approximating the function using kernel density
estimation (KDE). The first method requires defining the width (e.g., in mm/yr) of the histogram
bins. The bin width must be small enough to provide meaningful distinctions between different
slip rate values, yet large enough to produce a smooth function and avoid undersampling any
intervals. We found that for Marlborough faults with slip rates ranging from 10
0
to 10
1
mm/yr, a
bin width of 0.1 mm/yr and n of 10
4
-10
6
samples yielded optimal results. The second method,
using KDE, has a similar requirement of specifying kernel width or bandwidth. This is may
become problematic when the ages of different offset features overlap, or are relatively close, as
this situation can lead to high (potentially infinite) slip rate samples. Because the very high slip
rate values are relatively sparsely sampled, rule of thumb bandwidth estimators typically result in
kernel widths that are too large to capture detail at relatively slow slip rates. In our script, the
bandwidth can be specified manually, however, for the Tophouse Road data, we found it more
prudent to use a histogram with bin width of 0.1 mm/yr.
Finally, once the incremental slip rate samples are expressed as PDFs, it is often convenient
to describe a slip rate estimate as a “central” value, with error bars representing the dispersion or
spread of the function. For this, our script again provides two methods of calculating and reporting
these values: (1) highest posterior density; and (2) interquantile range. The highest posterior
404

density (HPD) range is found by determining value of maximum likelihood (peak value), and the
limits of the highest-likelihood values constituting 68.27% of mass of the function. For instance,
consider a slip rate PDF for which the peak value is 3.0 mm/yr, and the highest likelihood values
are between 1.5 mm/yr and 5.0 mm/yr. The HPD range in this example would be 3.0
+2.0
/-1.5 mm/yr.
We prefer the HPD range for describing our slip rate PDFs. The interquantile range (IQR) gives a
better description of the range of “central mass” of the function, and is determined from CDF of
the slip rate function. For given sufficiently large n, the IQR is roughly equivalent to the percentiles
of slip rate samples. The “central value” is the 50th percentile, and the expressed uncertainties
(inner 68.27% of mass) are the 15.685th and 84.135th percentiles. For right-skewed PDFs, the IQR
gives larger slip rate values. This is illustrated below for a log-normal PDF.

405

Figure S5c. Hypothetical slip rate PDF in the form of a (right skewed) log-normal distribution.
The PDF (gray field) is the same on the left and right sides. (Left) the blue field encompasses
68.27% of the function mass, expressed as the highest posterior density (HPD). The reported slip
rate in this case is 1.1
+0.3
/-0.2 mm/yr. (Right) The blue field shows the interquantile range (IQR)
encompassing 68.27% of the function mass. The reported slip rate is 1.2
+0.3
/-0.2 mm/yr. These
differences become more exaggerated for increasingly skewed functions.

The implementation of this method using our MCMC_incr_slip_rates.py routine is designed for
general application to any incremental slip rate data, given that the assumption of unidirectional
slip over the course of the slip rate history holds valid.
406

Table S6.
Offsets, ages, and slip rates of features at Tophouse Road

Offset
marker Offset (m)
a
Sample(s)
b

Offset age
(kyb2018)
c

Individual slip
rate (mm/yr)
d

Incremental slip
rate (mm/yr)
e

T1/T2 riser 47.0 ± 3.0
f
CR17-103 11.2 ± 1.3 4.2
+0.2
/ -0.3

9.6
+5.0
/ -2.5
T2/T3 riser 21.5 ± 2.0
g

combination
(CR12-01, CR12-
02)
9.0
+1.0
/ -0.9 2.4
+0.1
/ -0.2

1.3
+2.2
/ -1.2
T3/T4 riser
17.5–21.5 ±
0.5
h

TR17-03 8.1
+0.8
/ -0.7 2.4 ± 0.2

2.9 ± 0.5
Channel Y 9.0 ± 1.0 CR14-01 4.5
+0.8
/ -0.7 2.0 ± 0.2

2.0 ± 0.2
Present day

a
Preferred restoration value +/- limits of sedimentologically plausible geometries
b
IRSL sample or age distribution used to date offset marker
c
Peak probability +/- limits of 95.45% confidence interval, resulting from step 2 of our Bayesian age
model
d
Slip rates assuming only that marker was preserved. Peak probability +/- limits of 68.27%
confidence interval, based on weighted convolution of offset and age
e
Slip rates of intervals between markers. Peak probability +/- limits of 68.27% confidence interval,
based on Monte Carlo sampling
f
Sum of 42.0 +2.0/-1.5 m on the primary fault strand, and 5.0 +1.0/-1.5 m on the secondary fault
strand, ~70 m to the S
g
Sum of 20.5 ± 1.5 m on the primary fault strand, and 1.0 ± 0.5 m on the secondary fault strand, ~100
m to the S
h
Assuming equal probability (boxcar) between 17.5 and 21.5 m offset, with 0.5 m of uncertainty
represented by diminishing probability

Abstract (if available)

Abstract Observations of surface fault slip over multiple spatial and temporal scales are critical for understanding the processes governing plate boundary behavior and earthquake occurrence. In this thesis, I document spatial patterns of earthquake surface deformation using geologic and geodetic methods, and temporal patterns of fault slip over Holocene and latest Pleistocene time averaged timescales of 10²⁻⁴ years. I discuss observations from single-earthquake ground deformation patterns in light of the insights they provide into fault mechanics and the processes controlling rupture propagation. I then use observations of distributed surface strain in recent earthquake ruptures to inform interpretations of prehistoric earthquake activity preserved in the landscape. Finally, I analyze records of multiple slip-versus-time markers preserved in the landscape to determine longer-term spatiotemporal patterns of fault behavior (incremental slip rates) to investigate potential coordinated behavior between faults in a mechanically linked fault network. ❧ This thesis comprises five studies examining the surface characteristics of earthquake deformation and long-term fault behavior. In Chapters 2 and 3, I analyze the spatial patterns of surface deformation resulting from two large fault ruptures: (1) the 2013 moment magnitude (MW) 7.7 Balochistan, Pakistan earthquake

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Asset Metadata

Creator Zinke, Robert Wayne (author)

Core Title Characterizing fault behavior and earthquake surface expression on timescales of single events to multiple millennia

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Geological Sciences

Publication Date 11/08/2018

Defense Date 10/19/2018

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag earthquake faults,earthquake geology,fault slip rates,OAI-PMH Harvest,off-fault deformation,optical image correlation

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Dolan, James F. (committee chair), Nutt, Steve R. (committee member), West, A. Joshua (committee member)

Creator Email rzinke@usc.edu,zink.oxide81@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-105546

Unique identifier UC11675635

Identifier etd-ZinkeRober-6938.pdf (filename),usctheses-c89-105546 (legacy record id)

Legacy Identifier etd-ZinkeRober-6938.pdf

Dmrecord 105546

Document Type Dissertation

Format application/pdf (imt)

Rights Zinke, Robert Wayne

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA