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

Abstract

Observations of fault geometry and cumulative slip distribution serve as critical constraints on fault behavior over temporal scales ranging from a single earthquake to a fault’s complete history. The increasing availability of high-resolution topography (at least one observation per square meter) from air- and spaceborne platforms facilitates measuring geometric properties along faults over a range of spatial scales. However, manually mapping faults and measuring slip or scarp height is time-intensive, limiting the use of rich topography datasets. To substantially decrease the time required to analyze fault systems, we developed a novel approach for systematically mapping dip-slip faults and measuring scarp height. Our MATLAB algorithm detects fault scarps from topography by identifying regions of steep relief given length and slope parameters calibrated from a manually drawn fault map. We applied our algorithm to well-preserved normal faults in the Volcanic Tablelands of eastern California using four datasets: (1) structure-from-motion topography from a small uncrewed aerial system (sUAS; 20 cm resolution), (2) airborne laser scanning (25 cm), (3) Pléiades stereosatellite imagery (50 cm), and SRTM (30 m) topography. The algorithm and manually mapped fault trace architectures are consistent for primary faults, although can differ for secondary faults. On average, the scarp height profiles are asymmetric, suggesting fault lateral propagation and along-strike variations in the fault’s mechanical properties. We applied our algorithm to Arizona and Utah with a specific focus on the normal Hurricane fault where the algorithm mapped faults and other prominent topographic features well. This analysis demonstrates that the algorithm can be applied in a variety of geomorphic and tectonic settings.

Quantifying and analysing rock trait distributions of rocky fault scarps using deep learning

“…For the correlation analyses with fault scarp geomorphic characteristics, we used fault scarp height from the recent work of Scott et al (2022). They developed a semi‐automatic algorithm to calculate fault scarp height from global and high‐resolution topography datasets.…”

“…The scarp height is the offset of the best‐fit lines to the hanging wall and footwall flats at the scarp locations. To determine the scarp height, Scott et al (2022) used a 0.5 m‐resolution digital surface model produced from panchromatic tri‐stereo images processed with Micmac software (Rupnik et al, 2016, 2017). The local fault scarp heights were calculated every 5 m from 2 m‐wide transects of the topography data.…”

Directional Pair‐Correlation Analysis of Fracture Networks

“…This basic mechanical knowledge provides guidelines for statistically characterizing fracture networks, and statistical analysis can be used to supplement and calibrate this theoretical framework; see Schwartz and Sibson (1989) and Scott et al. (2022) for examples in the context of fault segmentation.…”

Resolving Quaternary Tectonic Activity with High-Resolution Data in Space and Time

“…Fault geometry and cumulative slip distribution are key issues in active tectonic studies, which are essential for constraining fault behavior over temporal scales from single earthquakes to fault's history. In this issue, Scott et al [19] developed a MATLAB algorithm to semiautomatically map active faults and measure scarp heights from high-resolution topographic data including small unscrewed aerial system (sUAS, sometimes also called UAV), airborne LiDAR, Pléiades stereo satellite imagery, and SRTM digital elevation model. By applying the semiautomatic algorithm to the Volcanic Tablelands of eastern California and Hurricane fault in Arizona and Utah, they showed that the algorithm mapped faults and other prominent topographic feature well and could be applied in a variety of geomorphic and tectonic settings.…”