Hydrothermal processes influence the along-strike morphologic variability of fault scarp profiles: applying supervised machine learning to Holocene scarps in the Stillwater Seismic Gap, Dixie Valley, Nevada, USA
DOI:
https://doi.org/10.59236/geomorphica.v2i2.58Keywords:
fault scarp, machine learning, hydrothermal alteration, sinter, support vector machinesAbstract
Fault scarp morphology can yield information about the age and slip distribution of surface-rupturing earthquakes. However, bedrock characteristics and geomorphic modification also contribute to scarp form. Additional complications arise in settings where hydrothermal processes modify the material properties of the faulted surface material. We present a case study where a single Holocene scarp offsets three adjacent units: alluvial and colluvial gravels; quartz-rich sinter and silicified gravels; and clay-rich, acid-sulfate altered gravels and bedrock.
We examined scarp-normal topographic profiles in each unit and employed supervised machine learning to describe the variations in scarp form and quantify changes in morphologic variability. In the alluvial and colluvial deposits, scarp profiles have a mean height of 6.2 (±0.9) m and exhibit low morphologic variability. The section of the scarp through hot spring sinter and cemented gravels has a mean height of 5.8 (±0.8) m and exhibits a high degree of along-strike variability. In the altered region, the individual Holocene scarp is difficult to discern and has low morphologic variability. Our study emphasizes the need to consider fault scarp composition during paleoseismic interpretation, and illustrates the potential utility of along-strike fault scarp morphologic variability in identifying lithologic and paleohydraulic boundaries.
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Copyright (c) 2026 Cassandra A. P. Brigham, Owen A. Callahan, Juliet G. Crider
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