Group A, Poster #165, Fault and Rupture Mechanics (FARM)
Semi-automated extraction of fault displacement profiles and displacement-length relationships from high-resolution lidar data and standard fault maps
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Poster Presentation
2022 SCEC Annual Meeting, Poster #165, SCEC Contribution #12491 VIEW PDF
t. We then train a Support Vector Machine to detect scarps based on spatial slope characteristics in a manually curated subset. Finally, we use the second derivative of elevation to fit each scarp in the profile and calculate throw. The algorithm outputs the displacement profile for every fault mapped, and the maximum displacement vs length relationship for the network of faults. This approach enables rapid and standardized collection of fault throw and length metrics for large datasets. We tested our algorithm on normal faults in the Volcanic Tableland in Bishop, California. Going forward, we will validate our method on other landscapes dominated by normal faulting where high-resolution topography is available.
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