DOI: 10.5061/dryad.dr7sqvb6j
title: Late Holocene offsets from the Panamint Valley transtensional relay, CA, USA

description: Several historic earthquakes in the Eastern California Shear Zone (ECSZ)
 have involved complex and multi-fault ruptures. However, the physical
 conditions that promote or inhibit discontinuity-spanning coseismic
 ruptures are still poorly defined. These physical conditions tend to vary
 in space and time over multiple earthquake cycles, making it difficult to
 forecast the likelihood of coordinated or triggered ruptures between
 multiple fault systems. Using tectonogeomorphic mapping, we document
 paleoseismic evidence for late Holocene complex rupture in Panamint
 Valley, in the 10 km-wide transtensional relay zone between the Ash Hill
 and Panamint Valley faults. We quantify rupture kinematics using vertical,
 lateral, and total offsets from over 250+ piercing lines, measured from
 field mapping and backslipped reconstructions of newly generated,
 high-resolution (5 cm) structure from motion (SfM) digital surface models.
 Our measured right-lateral, vertical, and total offsets range from 0.24 -
 2.65 m, 0.01 - 0.78 m, and 0.28 - 2.74 m, respectively. The strike-slip to
 dip-slip ratios for surface ruptures in the Panamint Valley transtensional
 relay (PVTR) range from 4:3 to pure strike-slip, with an average ratio of
 ~5:1. Measured total offsets of up to ~0.6-1.0 m of slip on single-event
 scarps in the PVTR support total surface rupture lengths between ~20 - 33
 km, correlative to earthquake magnitudes of Mw ≈ 6.7 - 6.9.
description: We completed field and remote tectonogeomorphic mapping of faults
 within the Panamint Valley transtensional relay, at a resolution of
 1:4000, using base maps consisting of 1) GeoEye aerial imagery, 2) newly
 developed 5 cm structure-from-motion (SfM) digital surface models (DSMs),
 and 3) slope and hillshade derivatives from National Center for Airborne
 Laser Mapping (NCALM) 0.5 m airborne lidar, collected from the EarthScope
 SoCal Lidar Project, accessed at OpenTopography (http://opentopography.org). Offset locations are provided in UTM (NAD 1983 UTM Zone 11N) and decimal degrees. We resolved these smaller (~0.5 – 1 m) offsets in young Holocene surfaces with low &lt; ~0.5 m geomorphic relief using our newly collected SfM DSMs, available on OpenTopography (LaPlante, 2024). We processed the photogrammetry using SfM analysis in Agisoft Metashape, after the methods described in Reitman et al. (2015), and the USGS UAS Data Post-Processing Guide (Over et al., 2021). The DSMs used for our offset measurements have a horizontal uncertainty of 0.5 cm (+1.7/-0.46) per meter of horizontal distance, and a vertical uncertainty of 0.6 - 10 cm. We generated curvature, slope, standard deviation, and topographic profile index (TPI; Jenness, 2006) derivatives of the DSMs to locate offset geomorphic piercing lines along mapped fault traces. We calculated slope and curvature grids based on nearest-neighbor approaches, while standard deviation grids were calculated over 0.05 - 7 m radii. For TPI analyses, we used fine-scale and coarse-scale annuli to identify offsets in short- and long-wavelength topography. The fine-scale annulus neighborhood consisted of an inner and outer radius of 1.5 m and 4.5 m, respectively, and permitted the identification of offset short-wavelength and small-amplitude bar and swale morphology. The coarse-scale annulus neighborhood consisted of an inner and outer radius of 15 m and 45 m, respectively, and highlighted ruptures that offset entire alluvial fan surfaces. We measured lateral, vertical, and total offsets using piercing points defined by the intersection of bar edges and crests, swale thalwegs, and terraces risers with the traces of surface ruptures. Each piercing line was reconstructed using one of three methods: 1) field reconstructions of piercing points, 2) backslipped geomorphic features using the LaDiCaoz_v2 algorithm, and 3) Monte Carlo reconstructions of topographic profile regressions. In this dataset, each offset measurement is given a specific identifier where field measurements begin with “F”, backslipped measurements begin with “B” and reconstructions of topographic profiles begin with “T”. Right-lateral field measurements were taken by placing a measuring tape along the trace of the surface rupture and approximating a minimum, most likely, and maximum piercing line projection into the trace of the rupture, using several piercing lines along a single geomorphic feature (bar, swale, channel thalweg, terrace riser, etc). We measured statistically backslipped right-lateral, vertical, and total offsets using the LaDiCaoz_v2 algorithm, which cross-correlates topography on opposite sides of a fault using relief, width, and degree of symmetry (Zielke and Arrowsmith, 2012; Haddon et al., 2016). We measured vertical separation, including heave and throw, using a Monte Carlo method that measures the uncertainty of up-thrown and down-thrown surface regressions, and the location of the scarp midpoint, assuming fault dips of 70° – 90° (after the methods of Morell et al., 2017 and Thompson et al., 2002). We report the mean (µ) and standard deviation (σ) of lateral and vertical offsets, the ratio of vertical to lateral displacement, and the average total offset for each site, where applicable. All values are reported in meters (m). Additionally, we assign a confidence value to each offset measurement, ranking the piercing line(s) and piercing point(s) on a scale from 1 (high confidence) to 3 (low confidence), based on several qualitative indicators to assess the preservation quality of each piercing point. These descriptors include assessing the obliquity of the piercing point projection intersection with a surface rupture, the sharpness of the piercing point edges, the distance of separation between the end of the piercing point and the fault trace, as well as the likelihood that modification, alteration, or bioturbation had affected the trace of the piercing point or the trace of the surface rupture.
description: # Late Holocene offsets from the Panamint Valley transtensional relay, CA,
 USA
 [https://doi.org/10.5061/dryad.dr7sqvb6j](https://doi.org/10.5061/dryad.dr7sqvb6j) This dataset contains a table (provided in both .xls and .csv form) with all Holocene offsets measured between December 2020 and May 2022. This data was collected by Aubrey LaPlante, under the guidance of Christine Regalla (PI), as part of a Master’s thesis at Northern Arizona University, Flagstaff, AZ, USA. To measure cumulative displacements in Holocene alluvium, we used a combination of field measurements, backslip reconstructions of geomorphic features from high-resolution structure from motion drone-based imagery, and reconstructions of topographic profile lines taken perpendicularly to faults. Backslip reconstructions were completed using a Matlab script, LaDiCaoz_v2 (Zielke et al., 2012; Haddon et al., 2016), which is available on Github. We measured topographic Monte Carlo reconstructions using a Matlab code ‘reconstructTopo’, developed by Regalla (Morell et al., 2017), which has been included as a .zip folder in this dataset. ## Description of the data and file structure #### ***PVTRoffsets.csv/ PVTRoffsets.xls*** **offset_ID**: All offsets are given a unique identifier ID that references the offset type (backslip ‘B’, field ‘F’, or topographic reconstruction ‘T’), a three digit offset number, and a letter (‘a’ or ‘b’, etc) to indicate if it represents a pair of offsets on either side of a graben. **offset_type**: The primary offset measurement type from one of three options: SfM_backslip, field, or SfM_vert_sep. SfM_backslip measurements include lateral, vertical, and total offset. Field measurements include only the lateral component of offset. SfM_vert_sep measurements include the dip slip component, assuming an 80° (±10°) dip, and are sometimes paired with new measurements of the lateral component of slip using LaDiCaoz_v2 (i.e., includes both LaDiCaoz and reconstructTopo measurements) to calculate the total slip at a site. **northing, easting &** **zone**: UTM Coordinates are provided for a projected reference frame of NAD 1984 UTM zone 11. **longitude &** **latitude**: Location of offset measurement in decimal degrees (°). **unit**: The relative age of the offset unit, interpreted from semi-quantitative geomorphologic parameters involving desert pavement development, bar and swale degradation, clast alteration and weathering, and soil descriptions. From youngest to oldest, the offset Holocene to late Pleistocene units are Qf8, Qf7, Qf6a, Qf6b, Qf5, and Qia. For more information on our relative-age alluvial fan stratigraphy, please see LaPlante et al. (in review). **morphology**: This column references the feature or features that are offset, including bar and swale morphologies, inset terraces, contacts between generations of alluvium, alluvial fan edges, and/or drainages. **confidence**: We assigned a confidence rating to each offset measurement on a scale from 1 (high confidence) to 3 (low confidence) using measurement quality descriptors to estimate the accuracy and precision of the reported offset, following the approach of Sieh and Jahns (1984) and Haddon et al. (2016). For example, a high-confidence offset measurement has a well-defined piercing line that intersects nearly orthogonally with the fault, well-preserved piercing points, and minimal modification, alteration, and/or bioturbation of the surface. **lat_avg**: The average magnitude of right-lateral displacement (in meters) for a measured offset feature, calculated from 5-8 independent measurements. **lat_stdev**: The uncertainty of right-lateral displacement (in meters) for a measured offset feature from 5-8 independent measurements, given as one standard deviation of the lat_avg. **Z_avg**: The average magnitude of dip-slip displacement (in meters) for a measured offset feature, calculated from 5-8 independent measurements. These values were calculated using fault dip angles of 80° (±10°). **Z_stdev**: The uncertainty of right-lateral displacement (in meters) for an offset feature, calculated from 5-8 independent measurements, given as one standard deviation of the Z_avg. **lat:Z**: The ratio of right-lateral slip to dip:slip, representing the obliquity of the slip vector on a fault. The values in this column are equal to the amount of right-lateral slip expected for every 1 unit of dip-slip. For example, a lat:Z ratio of 8:1 means that for every 1 cm of dip-slip, you would expect 8 cm of strike-slip motion. **Z:lat**: The ratio of dip-slip to right-lateral slip, representing the obliquity of the slip vector on a fault. The values in this column are equal to the amount of dip-slip expected for every 1 unit of right-lateral slip. For example, a Z:lat ratio of 8:1 means that for every 1 cm of right-lateral motion, you would expect 8 cm of dip-slip motion. **Avg_total_offset**: The average magnitude of total displacement (in meters) for a measured offset geomorphic feature, calculated using the average values for right-lateral and dip-slip offset. #### ***reconstructTopo.zip*** In this folder, we include the reconstructTopo script as a Matlab script (reconstructTopo.m) and in rich text format (reconstructTopo.rtf). Additionally, we include a simple normal displacement scarp, ‘example-scarp.txt’, that you may use to test the script. Running this example, and using the default values, you should achieve similar results seen in the processed scarp .mat file, illustrated in ‘example-scarp-processed.png’, and similar Monte Carlo reconstructions seen in ‘example-scarp-MC’ .mat and .png files. ## Sharing/Access information We measured all backslips and topographically reconstructed fault offsets using 20 newly generated 0.5 cm structure-from-motion (SfM) digital surface models (DSMs). You can find these DSMs on [www.opentopography.org:](http://www.opentopography.org/) [https://doi.org/10.5069/G9PC30M4](https://doi.org/10.5069/G9PC30M4) ## Code/Software **reconstructTopo script** This Matlab script was written by Christine Regalla for the methods of Morell et al. (2017), after the methods of Thompson et al. (2002), and modified by Aubrey LaPlante in 2024 to increase public user-facing functionality (i.e., added more comments and more descriptions of methods). This code successfully runs on MATLAB R2023b. Please contact Aubrey LaPlante at [aal382@nau.edu](mailto:aal382@nau.edu) if you have any problems or encounter bugs. This code calculates the magnitude of the cumulative slip displacement of a surface across a fault scarp. Using a Monte Carlo approach, this code can estimate the vertical separation, dip slip, heave, and throw of an offset surface, along a fault of known dip angle and known location of intersection with the ground surface. The uncertainties of cumulative fault slip magnitude include input values of: fault dip, the regression through the upper and lower surfaces, and the location of the intersection point of the fault plane and the ground surface (usually around the scarp midpoint or above it).  Regression and intersection limits are entered through an interactive plot (a cross-hair for point selection should appear). Points need to be selected from left to right. After selecting the surface and fault scarp geometries, you can save these choices as a .mat file for future Monte Carlo reconstructions.  The script calculates uncertainty through a Monte Carlo simulation where it runs ’N’ iterations of the calculation given a range of fault dips, regression lines, and midpoint values, selected from value ranges dictated by uncertainty in these parameters. Uncertainty in fault dip is entered as a numeric value (ex: fault dip = 60 +/- 10 degrees); uncertainty in regressions is given by the 95% confidence interval of the linear regression through the upper and lower surfaces; uncertainty in scarp midpoint/fault intersection is determined interactively by the user defining the range of possible locations the fault could intersect the scarp surface. The output is a table with average displacement values and uncertainties given in 1 standard deviation. See the README included in the .zip file and additional comments throughout the code for clarification on individual steps. ## References Dixon, T. (1998). Simulation Modeling Using @RISK. *Journal of Property Valuation and Investment*, 16(3), 347-348. [https://doi.org/10.1108/jpvi.1998.16.3.347.4](https://doi.org/10.1108/jpvi.1998.16.3.347.4) Jenness, J. (2006). Topographic Position Index (tpi_jen.avx) extension for ArcView 3.x, v. 1.2. *Jenness Enterprises*. Available at: [http://www.jennessent.com/arcview/tpi.htm](http://www.jennessent.com/arcview/tpi.htm). Haddon, E. K., C. B. Amos, O. Zielke, A. S. Jayko, and R. Bürgmann. (2016). Surface slip during the large Owens Valley earthquakes. *Geochemistry, Geophysics, Geosystems.* 17, 2239-2269, [https://doi.org/10.1002/2015GC006033](https://doi.org/10.1002/2015GC006033) LaPlante, A., Regalla, C., Sethanant, I., Mahan, S., Gray, H. (2024). Spatiotemporally-variable strain accommodation and seismic rupture in multifault systems: An example from Panamint Valley, northern ECSZ [Manuscript submitted for publication]. School of Earth and Sustainability, Northern Arizona University. Morell, K.D., Regalla, C., Leonard, L.J., Amos, C. & Levson, V. (2017) Quaternary rupture of a crustal fault beneath Victoria, British Columbia, Canada. *GSA Today*, 27(3), 4–10. [https://doi.org/10.1130/GSATG291A.1](https://doi.org/10.1130/GSATG291A.1) Over, J.R., Ritchie, A.C., Kranenburg, C.J., Brown, J.A., Buscombe, D., Noble, T., Sherwood, C.R., Warrick, J.A., and Wernette, P.A. (2021). Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation: *U.S. Geological Survey Open-File Report. *2021–1039, 46 p. [https://doi.org/10.3133/ofr20211039](https://doi.org/10.3133/ofr20211039). Sieh, K. E., & Jahns, R. H. (1984). Holocene activity of the San Andreas fault at Wallace Creek, California. *Geological Society of America Bulletin*, *95*(8), 883-896. Thompson, S.C., Weldon, R.J., Rubin, C.M., Abdrakhmatov, K., Molnar, P. & Berger, G.W. (2002) Late Quaternary slip rates across the central Tien Shan, Kyrgyzstan, central Asia. *Journal of Geophysical Research: Solid Earth*, 107(B9), ETG 7-1– ETG 7-32. [https://doi.org/10.1029/2001JB000596](https://doi.org/10.1029/2001JB000596) Reitman, N. G., Bennett, S. E., Gold, R. D., Briggs, R. W., & DuRoss, C. B. (2015). High‐resolution trench photomosaics from image‐based modeling: Workflow and error analysis. *Bulletin of the Seismological Society of America*, *105* (5), 2354-2366. Winston, W. L. (2000). Simulation modeling using @RISK. Duxbury. Zielke, O. and Arrowsmith, J.R. (2012). LaDiCaoz and LiDARimager -MATLAB GUIs for LiDAR data handling and lateral displacement measurement, *Geosphere,* 8(1), 206-221. [https://doi.org/10.1130/GES00686.1](https://doi.org/10.1130/GES00686.1)