Regional‐Scale Detection of Fault Scarps and Other Tectonic Landforms: Examples From Northern California

Abstract

Fault scarps and fault-related landforms provide important information about fault zone activity over timescales that are not captured by instrumental measurements or historic records. Semiautomated methods for delineating these landforms using topographic data from light detection and ranging (lidar) and spaceborne imaging systems offer the opportunity to characterize fault zones on a global scale. We present a computationally efficient method for extracting scarp-like landforms from high-resolution (≤ 2 m), regional-scale (≥ 100-km-long) digital topographic data sets. We identify fault-related landforms using a curvature template based on the diffusion model for scarp degradation and extract scarp heights and morphologic ages at each pixel. The method was applied to the GeoEarthScope Northern California data set, an airborne lidar acquisition imaging nearly 2,500 km 2 of the northern San Andreas Fault system, by adapting the algorithm to use cloud computing resources. Template results and fault trace mapping show spatial agreement in active fault zones with clear topographic expression, including detection of fault scarps, shutter ridges, and elongated drainages. Comparison of the method against field-based morphologic dating of scarps along the southern San Andreas reveals a trade-off between template window size and morphologic age contrasts resolved between strike-slip fault scarps of different relative ages. Detection performance suggests that window size and orientation constraints may play a key role in improving the accuracy of methods for semiautomated fault zone mapping. As data availability grows, these methods could constrain key earthquake simulation parameters such as damage zone width or rupture length and improve fault maps worldwide.

Microcontinent Breakup and Links to Possible Plate Boundary Reorganization in the Northern Gulf of California, México

“…We used a template length of 200 m, as in Sare et al. (2019), to capture scarp segments of similar length scales. Template matching also produces grid maps of morphologic age, also known as the degradation coefficient κt [m 2 ].…”

“…The extent of neotectonic field mapping is often limited by field conditions, inaccessibility, subtle topography, or dense arrays of distributed faulting. To overcome these limitations and identify unmapped faults across a large region of interest, we extracted topographic scarps from our large 3-m resolution DEM using Scarplet, an open-source software package that semi-automatically identifies scarps by matching topographic curvature in a DEM with that of ideal template scarp profiles from the hillslope diffusion equation (described and discussed in Supporting Information S1; Hilley et al [2010]; Sare et al [2019]). This template matching algorithm searches for areas of scarp-like features (usually 10's of meters wide and 100's of meters long; Figure S2 in Supporting Information S1) within DEMs and produces raster grid maps of scarp properties such as best-fit orientation and amplitude (Figure S1 in Supporting Information S1).…”

Statewide USGS 3DEP Lidar Topographic Differencing Applied to Indiana, USA

“…Insights into Big Data processing from studies such as those discussed above and our work can provide support for a variety of activities in Earth observation. For example, these and similar methods could be applied to the mapping of geologic hazards including landslides [56], sinkholes [57], flood zones [7], tectonic faults [58,59], and coastal erosion [9]. They can also be applied to understand anthropogenic interaction with the environmentfor example, biomass or biomass change [6,47,60,61], agriculture [62], and infrastructure development [8].…”

Semiautomatic Algorithm to Map Tectonic Faults and Measure Scarp Height from Topography Applied to the Volcanic Tablelands and the Hurricane Fault, Western US

“…Hodge et al's [25] Scarp Parameter Algorithm (SPARTA) maps normal fault scarps from calibrated slope and curvature parameters. Sare et al's [26] algorithm uses a curvature template and a hillslope diffusion model to map dip-slip fault scarps and calculate height and morphologic age. Howe et al's [27] approach constrains slip by mapping paleolake shorelines from topographic inflection points.…”