Three-Dimensional Surface Deformation in the 2016 MW 7.8 Kaikōura, New Zealand, Earthquake from Optical Image Correlation

Three-Dimensional Surface Deformation in the 2016 M W 7.8 Kaikōura, New Zealand, Earthquake From Optical Image Correlation: Implications for Strain Localization and Long-Term Evolution of the Pacific-Australian Plate Boundary Robert Zinke, James Hollingsworth, James Dolan, Russ van Dissen

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Robert Zinke, James Hollingsworth, James Dolan, Russ van Dissen. Three-Dimensional Surface De- formation in the 2016 M W 7.8 Kaikōura, New Zealand, Earthquake From Optical Image Correla- tion: Implications for Strain Localization and Long-Term Evolution of the Pacific-Australian Plate Boundary. Geochemistry, Geophysics, Geosystems, AGU and the Geochemical Society, 2019, 20 (3), pp.1609-1628. 10.1029/2018GC007951. hal-02416376

HAL Id: hal-02416376 https://hal.archives-ouvertes.fr/hal-02416376 Submitted on 1 Sep 2021

HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Copyright RESEARCH ARTICLE Three‐Dimensional Surface Deformation in the 2016 MW 10.1029/2018GC007951 7.8 Kaikōura, New Zealand, Earthquake From Optical Key Points: • Correlation of high‐resolution Image Correlation: Implications for Strain optical satellite imagery reveals 3‐D ‐ surface deformation patterns of the Localization and Long Term Evolution ō 2016 MW 7.8 Kaik ura earthquake ﬁ ‐ • Kinematic analysis shows slip on of the Paci c Australian Plate northern faults was subparallel to plate motion but slip on southern Boundary faults was transpressional Robert Zinke1 , James Hollingsworth2 , James F. Dolan1 , and Russ Van Dissen3 • Comparison of Kekerengu fault ﬁeld measurements with image 1Department of Earth Sciences, University of Southern California, Los Angeles, CA, USA, 2Université Grenoble Alpes, correlation measurements reveals 3 up to ~36% distributed deformation Université Savoie Mont Blanc, CNRS, IRD, IFSTTAR, Grenoble, France, GNS Science, Lower Hutt, New Zealand

Supporting Information: ‐ ‐ • Supporting Information S1 Abstract We generated dense, high resolution 3 D ground displacement maps for the 2016 MW 7.8 • Supporting Information S2 Kaikōura, New Zealand earthquake—the most geometrically and kinematically complex rupture yet • Table S1 — • recorded from stereo WorldView optical satellite imagery using a new methodology that combines subpixel Table S2 ‐ ‐fi • Table S3 image correlation with a ray tracing approach. Our analysis reveals fundamental new details of near eld • Table S4 displacement patterns, which cannot easily be obtained through other methods. From our detailed correlation • Table S5 ‐ ‐ • maps, we measured fault slip in 3 D along 19 faults at 500 m spacing. Minimum resolvable horizontal slip Table S6 ‐ • Table S7 is ~0.1 m, and vertical is ~0.5 m. Net slip measurements range from <1 to ~12 m. System level kinematic • Figure S1 analysis shows that slip on faults north of the Hope fault was oriented primarily subparallel to the Pacific‐ • Figure S2 Australian plate motion direction. In contrast, slip on faults to the south was primarily at high angle to the • Figure S3 • Data Set S1 plate motion and secondarily parallel to plate motion. Fault kinematics are in some locations consistent with • Data Set S2 long‐term uplift patterns, but inconsistent in others. Deformation within the Seaward Kaikōura Range may • Data Set S3 • indicate an attempt by the plate boundary fault system to geometrically simplify. Comparison of published Data Set S4 fi ‐ • Data Set S5 eld 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 Correspondence to: (e.g., fence lines) into the fault over apertures >5–100 m capture nearly all (~90%) of the surface deformation. R. Zinke, [email protected] 1. Introduction Citation: The 2016 M 7.8 Kaikōura, New Zealand, earthquake produced one of the most spatially and kinematically Zinke, R., Hollingsworth, J., Dolan, J. F., W & Van Dissen, R. (2019). complex ruptures ever recorded. The mainshock occurred shortly after midnight, local time, on 14 November Three‐dimensional surface deformation 2016, near the town of Waiau in northern South Island, New Zealand (Cesca et al., 2017; Kaiser et al., 2017; ō in the 2016 MW 7.8 Kaik ura, New Litchfield et al., 2018; Nicol et al., 2018). The focal mechanism indicated a nondouble couple source, with Zealand, earthquake from optical image fi ‐ correlation: Implications for strain signi cant right lateral and reverse motion (e.g., www.globalcmt.org/; Duputel & Rivera, 2017, localization and long‐term evolution of Hollingsworth et al., 2017, Kaiser et al., 2017). The rupture propagated northeastward for approximately fi ‐ the Paci c Australian plate boundary. 1.5 min, with peak moment release occurring at ~60 s or later (e.g., Cesca et al., 2017; Holden et al., 2017; Geochemistry, Geophysics, Geosystems, 20, 1609–1628. https://doi.org/10.1029/ Hollingsworth et al., 2017). Fault rupture with surface displacements >1 m was reported along more than 2018GC007951 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 Received 17 SEP 2018 the subduction megathrust fault played in the earthquake, though it is generally presumed that it, or some Accepted 9 FEB 2019 Accepted article online 15 FEB 2019 other deep thrust fault, contributed to the overall moment release and likely aided rupture propagation Published online 28 MAR 2019 through the complex network of faults (Bai et al., 2017; Cesca et al., 2017; Clark et al., 2017; Duputel & Rivera, 2017; Hamling et al., 2017; Hollingsworth et al., 2017; Litchfield et al., 2018; Wang et al., 2018). Immediately following the 14 November mainshock, teams of field geologists began measuring surface deformation resulting from the rupture (Clark et al., 2017; Kearse et al., 2018; Langridge et al., 2018; Litchfield et al., 2018; Nicol et al., 2018; Stirling et al., 2017; Williams et al., 2018). These measurements pro-

©2019. American Geophysical Union. vide key insights into the spatial distribution, kinematics, and complexity of slip in the event. Due to the lim- All Rights Reserved. itations of measurable geomorphic and linear cultural features, however, the ﬁeld measurements of

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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 (Dolan & Haravitch, 2014; Milliner et al., 2015, 2016; Rockwell et al., 2002; Shelef & Oskin, 2010; Zinke et al., 2014). To help over- come 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., 2017; Wang et al., 2018; Xu 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. In this study, we take advantage of very high resolution (~0.5 m) stereo preearthquake and postearthquake WorldView optical satellite images to retrieve a homogeneous 3‐D 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.

1.1. 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 (Figure 1; Barnes et al., 1998; Holt & Haines, 1995; Reyners & Cowan, 1993; Wallace et al., 2012). The subducting Pacific plate underlies much of the 2016 earthquake rupture area, at depths as shallow as 20– 30 km (Eberhart‐Phillips & Bannister, 2010; Wallace et al., 2012). 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 & Cowan, 1993; Wallace et al., 2012). Here the tectonic regime transitions into continental collision (Eberhart‐Phillips & Bannister, 2010; Nicol et al., 1994; Pettinga et al., 2001; 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 (Figure 1; e.g., Litchfield et al., 2014; Wallace et al., 2012). 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 (Figure 1, inset; Litchfield et al., 2018; Nicol et al., 1994, 2018; Pettinga et al., 2001).

2. Methods and Observations 2.1. Determination of 3‐D Deformation From Optical Image Correlation OIC has shown great utility in previous studies measuring earthquake‐induced surface deformation (e.g., Avouac et al., 2014; Avouac & Leprince, 2015; Ayoub et al., 2009; Barnhart et al., 2014; Hollingsworth

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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. 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 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 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 thick arrow is the plate motion vector (Beavan et al., 2002; DeMets et al., 2010). Black thin arrows and triangles represent fault kinematics in the 2016 rupture. MFS = Marlborough fault system; NCD = Northern Canterbury domain.

et al., 2012, 2017; Leprince, Ayoub, et al. (2007); Leprince, Barbot, et al. (2007); Leprince et al., 2008; Michel & Avouac, 2002, 2006; Milliner et al., 2015; Milliner, Dolan, et al., 2016; Milliner, Sammis, et al., 2016; Wei et al., 2011; Zinke et al., 2014). OIC processing tools, such as COSI‐Corr (Leprince, Ayoub, et al., 2007), MicMac (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 2‐D 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 furthermore 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, Sammis, et al., 2016). Because the correlations capture both on‐fault 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 (Milliner et al., 2015; Milliner, Dolan, et al., 2016; Zinke et al., 2014). 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 ~1992), OIC provides the only method for remotely characterizing the ground deformation (e.g., Hollingsworth et al., 2012; Marchandon et al., 2018; Milliner et al., 2015).Using a standard 2‐D OIC processing approach (COSI‐Corr; see also Ayoub et al., 2015), Hollingsworth et al. (2017) correlated preearthquake and postearthquake 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 details of ground displacement. Furthermore, given the nadir (i.e., vertical incidence) acquisition geometry of

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Landsat8 (and Sentinel‐2) imagery, OIC can only resolve the horizontal (2‐D) 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 preearthquake and postearthquake high‐resolution stereo imagery. This methodology provides highly detailed maps of 3‐D surface deformation without the need to combine multiple sensors or generate separate digital elevation models (DEMs) with which to precisely orthorectify the preearthquake and postearthquake satellite images (which are not always available and yet are essential for the successful correlation of nonstereo WorldView images using a traditional 2‐D correlation approach; e.g., Kuo et al., 2018). 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 times) 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 motion. Following the methods of Avouac and Leprince (2015), we chose sets of WorldView images such that each ground location was imaged by two preearthquake images and two postearthquake images (each with opposing viewing geometries to exploit the stereo effect). For each set, we coregistered three of the images to a master preearthquake image and orthorectified all four using a relatively coarse‐resolution (8 m) DEM to remove long‐wavelength topographic distortions. We then correlated each pair of images to create disparity maps for precise determination of tectonic offset and finer‐ scale topographic distortions. Coregistration, orthorectification, and correlation were all performed using COSI‐Corr software (see, e.g., Leprince, Ayoub, et al., 2007). In much the same way that stereo imagery is used to create topographic maps, we then used a ray tracing approach between the disparity maps and satellite positions, known from the satellite auxiliary information, to invert for the precise horizontal and vertical positions of each point on the ground surface. Finally, we directly solved for the 3‐D vectors by simply differ- encing the preearthquake and postearthquake locations of each point (see Avouac & Leprince, 2015 and supporting information S2). The final result was a map of 3‐D ground displacement measurements every 8 m. Postprocessing was performed to remove noise and linear ramps within the final correlation maps. The precise details of the correlation approach are given in the supporting information (Table S1 and 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 & Leprince, 2015), the lack of available high‐resolution preearthquake and postearthquake 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 multitemporal stereo acquisitions in the future, this new methodology will help to facilitate more precise and rapid 3‐D 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 S1). 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 km2. 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 downdropping of the ground‐surface along faults, folds, and landslides. Vertical deformation on the order of a few tens 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°).

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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).

Correlation proved challenging in areas of very high topographic relief, where steep mountainous slopes may be undersampled 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 (Figure 2). Agricultural change (harvesting, crop growth, and replant- ing) 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 (Figures 1 and 2). 2.2. Fault Trace Mapping We mapped the fault traces by visually identifying persistent gradients in our correlation results (Figures 1 and 2) and by noting discernable surface faulting in the postevent orthoimages. 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 reach the ground‐surface along discrete planes. Because of this ambiguity, we include both cases in

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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 consti- tuted 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 (Figure 1 and Data Set S1). Segment lengths ranged between 0.5 and 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 therefore not considered. Landsliding, evinced by decorrelation or displacement of iso- lated 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.

2.3. Identification and Mapping of the Snowflake Spur Fault We identified the Snowflake Spur fault (Figure 1) based on strong, persistent, EW, and NS correlation gradients (Figure 2 and Figure S1 and Data Set S1). The Snowflake Spur fault trends northeast‐southwest (~210°), roughly following the general trend of the Jordan Thrust and Manakau faults (Figure 1) to its ter- minus at or near the lower, unruptured segment of the Kowhai fault (Langridge et al., 2016; Van Dissen & Yeats, 1991). Displacement across the fault is primarily right lateral with minor, locally variable compres- sional 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 (Figures 2 and 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.

2.4. 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 measurement, we collected a swath of displacement values from our three correlations, which we stacked into 1‐D profiles and filtered with a moving median filter. We then projected the displacement profiles into fault‐parallel, fault‐perpendicular, and vertical directions, relative to local fault strike (Figure 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 (Figure 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 (Figure 3, insets; e.g., Milliner et al., 2015). Where the distance between subparallel faults was less than 3 km, we only

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Figure 3. Net slip measurements (colored dots) from stacked displacement profiles collected every 500 m (black bar in upper right corner represents measurement profile dimensions: 0.5 km along strike and 6 km perpendicular to strike). (insets) Three components of slip on the Kekerengu fault. Blue solid lines are linear best fits of surface deformation. Blue bashed lines show fault zone width. (bottom left) Histogram of all net slip measurements. (bottom right) Histogram of all valid fault zone width measurements.

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 also by subsidiary faulting, warping, granular flow, and/or local block rotations (Ben‐Zion & Sammis, 2003; Milliner et al., 2015; Milliner, Dolan, et al., 2016; Shelef & Oskin, 2010; Zinke et al., 2014). The fault zone is evident as rollover in our displacement profiles, where displacement values curve into the primary fault trace (Figure 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. Nontectonic phenomena such as landslides, clouds, and shadows, as well as minor uncorrected topographic 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 fault‐perpendicular, and vertical).

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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). MFS = Marlborough fault system; NCD = Northern Canterbury domain; SKR = Seaward Kaikōura Range.

2.5. Offset Measurement and Fault Zone Width Analysis In all, we collected 531 measurements of fault offset at 500‐m spacing (Figure 3 and Table S2 and Data Set S2). 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; Figure 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 and 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‐ to ~1.5‐km wide (mean 370 m), based on 302 measurements (biased measurements excluded; Figure 3 and Table S3 and Data Set S3). 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 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.

2.6. 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 3‐D 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 (Figure S2) for a more complete description this methodology. We expressed all fault planes and slip vectors in a lower‐hemisphere projection (Figure 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 (Figure 3,

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insets). On the whole, however, our calculations lead to a coherent picture of near‐surface fault plane atti- tude 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 S6). 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 represented than smaller ones. Generally, our kinematic orientation measurements (summarized for each fault in Figure S3) reflect the broader deformation patterns shown in Figure 4 and agree with broad‐scale field observations of deformation and uplift (Clark et al., 2017; Kearse et al., 2018; Langridge et al., 2018; Litchfield et al., 2018; Nicol et al., 2018; Stirling et al., 2017; 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 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 Figure S3) 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. Discussion and Implications 3.1. Consistency of Rupture Patterns With Long‐Term Kinematics: Observations Our 3‐D deformation maps, offset measurements, and kinematic analyses allow us to assess the consistency of the 2016 rupture patterns with longer‐term (multiearthquake) 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 (Figures 2 and 4). These kinematics are consistent with the long‐term history of the fault, which 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 (Kearse et al., 2018; Little et al., 2018).To the southwest, near its junction with the Papatea and Fidget faults, the Kekerengu fault transfers slip onto the Jordan Thrust (Figure 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 (Figure 1; Van Dissen & 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 (Figures 2 and 4). This anomalous sense of slip resulted from southward movement of the Papatea block (Figure 4), which experienced up to ~7.3 m of left‐lateral displacement and ~9.0 m of west 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 (Cesca et al., 2017; Van Dissen & Yeats, 1991). In either case, the fact that the Seaward Kaikōura Range northwest of the Jordan Thrust is substantially higher (and therefore building fas- ter) 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 strad- dle the crest of the Seaward Kaikōura Range (SKR; Figure 1). Despite some decorrelation in this area (see section 2.1), our 3‐D 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 (Figures 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

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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 preearthquake stereo aerial photography, the LINZ 8‐m DEM (https://data.linz.govt.nz) and postearthquake lidar. Interestingly, despite their positions near the top of the fastest‐rising mountain range in northern South Island (Van Dissen & Yeats, 1991), slip along these two faults produced relative downdropping of a graben along the range crest (Figures 2 and Figure S1). 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 & 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 preexisting 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 (Rattenbury et al., 2006; Van Dissen & Yeats, 1991). 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 correlates with topography (e.g., compare Figure 1 with Figure 2). For example, the reverse‐sinistral Whites fault ruptured through a range of low mountains north of Oaro, generating westside up movement and accommodating uplift of the highest sum- mits 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. Northside 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 folding in the Mt. Stewart area, west of the northern Leader fault and north of the eastern stretch of The Humps fault (Figure 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 predates the Neogene faulting we investigate here (Nicol et al., 2018; Rattenbury et al., 2006), 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 (Litchfield et al., 2018; Nicol 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.

3.2. 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 coseismic 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., Stirling et al., 1996; Wesnousky, 1988; Wesnousky, 2006), the Kaikōura earthquake was surprising for its ~180‐km‐long,

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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 perspec- tive, a single rupture linking all the broken faults in the 2016 earthquake would not have been expected prior to the earthquake. Nevertheless, consistency between the long‐term and short‐term uplift patterns demon- strates 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 multisegmented 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., 2014) could suggest that a fault previously participated in complex ruptures. Identiﬁcation of such phenomena could therefore be of signiﬁcance to seismic hazard assessment.

3.3. 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 (Figure 2 and Figure S1). 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 (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. 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 unli- kely, 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.

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3.4. 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 (Figure 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 (Figure 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 (Figure 4). In contrast, surface deformation patterns in the NCD are highly variable, with 3‐D deformation vectors arrayed in a diversity of orientations. Here strain was accommodated through faulting, folding, and block rotation. NCD 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 (Figure S3). 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 Figure 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 Figure 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 east to northeast 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°. 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., east 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 (Figure 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).

3.5. 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/year of relative plate motion is transferred from the Hope fault onto the Jordan Thrust‐ Kekerengu fault system (Langrdige et al., 2003; Little et al., 2018; Van Dissen & Yeats, 1991). The Hope and Jordan Thrust faults intersect at a high angle (~43°), forming a mechanically unfavorable kink across which

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strain is transferred (Figure 1). Instead, the various faults of the SKR form a ~30° kink angle between the Hope fault and faults of the SKR (west of the Jordan Thrust), making this zone of strain transfer mechanically more favorable. Transfer of strain through the SKR (and away from the mechanically unfavorable Hope‐Jordan intersection) via rupture of faults such as the Manakau, Upper Kowhai, and Snowﬂake Spur may represent an indication of a longer‐term tectonic progression toward simplifying the geometry of the plate boundary in this area.

3.6. 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, 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 (Tables S4–S5 and Data Sets S4–S5). 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 manmade 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 (Kearse et al., 2018; see supporting information). These unprojected measurements represent the discrete component of slip across the fault and include 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 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‐ to 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) and Milliner, Dolan, et al. (2016). We cal- culated 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 measurement. 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 the 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 S7). 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 observation 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 & Oskin, 2010).We note that a few field measurements were larger than the nearest correlation measurement (Figure 5). Some of these values

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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 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, and (c) projected field measurements. (d) Blue dots are OFD% for projected field measurements (unbinned) versus distance across, which they are projected. Gray dashed 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 (slope m = 0.94; Spearman rank coefficient ρS = 0.52).

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 (Milliner et al., 2015; Zinke et al., 2014). At either end of the slip profile (Figure 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 accu- rately 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 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 (Figure 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 (Figure 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

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each projected measurement versus the fault‐perpendicular width over which it was projected (Figure 5d), two key observations emerge. First, 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 pointing out that in general, the percentage of missing deformation decreases rapidly as the projection width increases (over meters to tens of meters). Second, 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.

3.7. 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‐ to 80‐km cumulative displacement, Dolan & 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, 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 ~46% OFD (Zinke et al., 2014). 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, Dolan, et al., 2016). 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. (2014) 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 (Duffy et al., 2013; Quigley et al., 2012; Van Dissen et al., 2011). Existing geologic maps show that along much of its length, the Kekerengu fault juxtaposes Cretaceous Torlesse greywacke bedrock against Tertiary mudstones and silt- stones (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 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 & Allen, 2007), as steeper slopes are sustained by stronger geologic

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materials (e.g., bedrock), whereas low‐relief areas commonly correspond to basins in which weaker sedi- mentary 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 (Figure 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 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 not only is ubiquitous among surface ruptures but also can constitute a substantial portion of total surface deformation (e.g., Dolan & Haravitch, 2014; Milliner et al., 2015; Milliner, Dolan, et al., 2016; Zinke et al., 2014). 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 microzona- tion 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, Dolan et al., 2016; Ganev et al., 2012; Peltzer et al., 2001; eastern California shear zone, Meade & Hager, 2005; Oskin et al., 2008; Doruneh fault, Farbod et al., 2016; Walpersdorf et al., 2014; 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., Dolan et al., 2007; Kozacı et al., 2009; Oskin et al., 2008; Peltzer et al., 2001). However, if OFD is considered in determining fault slip rates (whether by direct measurement of near‐field warping or by estimating OFD% based on empirical studies, e.g., Kearse et al., 2018), such differences may narrow considerably (e.g., Dolan & Haravitch, 2014; Milliner, Dolan, et al., 2016; Shelef & Oskin, 2010). Properly accounting for OFD is therefore important for determining the roles of faults in accommodating crustal deformation (e.g., in Tibet, Tapponier & Molnar, 1976) and characterizing the mechanics of lithospheric rheology based on comparisons of fault slip rates to geodetically inferred slip deficit rates (Dolan & Meade, 2017; Meade et al., 2013, and references therein). Furthermore, Quaternary fault slip rates provide an important input for Probabilistic Seismic Hazard Assessment (e.g., Field et al., 2017; Stirling et al., 2012), 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 (Milliner, Dolan, et al., 2016; Stirling et al., 2012, 2013; Wells & Coppersmith, 1994; Wesnousky, 2008). 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, Dolan, et al., 2016). 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.

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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 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.

4. Conclusions We used an advanced workflow for optical image correlation of high‐resolution WorldView satellite stereo imagery to determine the 3‐D surface deformation generated during the 2016 Kaikōura earthquake. The resulting deformation maps revealed the spatial and kinematic complexity of the 2016 earthquake in unpre- cedented detail. We identified a previously unrecognized fault, the Snowflake Spur fault, that acts as a con- tinuation 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 subperpendicular (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 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 Acknowledgments Seaward Kaikōura Range (Van Dissen & Yeats, 1991). We especially thank Jesse Kearse (Victoria University of Wellington) for Comparison of our displacement measurements along the Kekerengu fault with published field measure- his assistance with field measurements ments revealed up to ~36% of surface deformation is accommodated as OFD. The amount of OFD not and observations; Sebastien Leprince, captured by field measurements, however, decreases nonlinearly as the fault‐perpendicular distance over whose assistance with implementing the 3‐D ray tracing methodology was which the field measurements are projected increases, and most OFD is accommodated within ~100 m of instrumental; and Francois Ayoub for the primary fault. More high‐resolution surface deformation studies such as that presented above, and assistance with outlier removal. Thanks detailed geotechnical characterization of the fault zone materials, are necessary to determine what factors to Paul Morin and Mike Cloutier (UMN Polar Geospatial Center) for their assis- control the magnitude and distribution of OFD on faults generally. tance acquiring WorldView imagery and Oleg Alexandrov and Scott McMichael (NASA Ames Research Center) for help with image processing References with Ames Stereo Pipeline. This work Avouac, J.‐P., Ayoub, F., Wei, S. J., Ampuero, J.‐P., Meng, L. S., Leprince, S., et al. (2014). The 2013, MW 7.7 Balochistan earthquake, was funded by U.S. National Science energetic strike slip reactivation of a thrust fault. Earth and Planetary Science Letters, 391, 128–134. https://doi.org/10.1016/j. Foundation grant EAR‐1321914 epsl.2014.01.036 (Dolan), with financial support from Avouac, J.‐P., & Leprince, S. (2015). Geodetic imaging using optical systems. In G. Schubert (Ed.), Treatise on Geophysics (pp. 387–424). research grants by INSU Tellus, and Oxford: Elsevier. INSU PNTS, and the Harry von Zell Ayoub, F., Leprince, L., & Avouac, J.‐P. (2015). User's guide to COSI‐Corr: Co‐registration of optically sensed images and correlation, USC Geophysics Research Fund. The California Institute of Technology. Retrieved from http://www.tectonics.caltech.edu/slip_history/spot_coseis/pdf_files/CosiCorr‐ 8‐m DEM was available through Land Guide2015a.pdf Information New Zealand (https:// Ayoub, F., Leprince, S., & Avouac, J.‐P. (2009). Co‐registration and correlation of aerial photographs for ground deformation measure- www.linz.govt.nz). Thanks to Stephane ments. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 551–560. Dominguez and an anonymous Bai, Y., Lay, T., Cheung, K. F., & Ye, L. (2017). Two regions of seafloor deformation generated the tsunami for the 13 November 2016, reviewer for their helpful reviews. All Kaikōura, New Zealand, earthquake. Geophysical Research Letters, 44, 6597–6606. https://doi.org/10.1002/2017GL073717 data used can be found in the support- Barnes, P. M., de Lépinay, B. M., Collot, J.‐Y., Delteil, J., & Audru, J.‐C. (1998). 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