FAULT MAPPING IN THE AGE OF LiDAR: INTEGRATING ALGORITHMS WITH TRADITIONAL GEOMORPHIC MAPPING

Geomorphic mapping that identifies landforms commonly associated with tectonic deformation is typically a first step in identifying and characterizing potential seismogenic faults. Such geomorphic mapping has traditionally been performed manually in the field, however, the introduction of widespread high quality Light Detection and Ranging (LiDAR) data has made desktop geomorphic interpretation an integral mapping step. Current methods of geomorphic mapping remain subjective and rely entirely on the individual mapper’s interpretation of the landscape, which can vary widely. Recently, several papers have been published that use algorithmic mapping methodologies to reduce subjectivity associated with traditional mapping. Scott et al. (2022) presented a “semi-automated” algorithm that maps fault scarps from LiDAR data using a MATLAB script calibrated by human mapping to identify laterally continuous topographic scarps. They demonstrated the effectiveness of this algorithmic approach on normal faults in the Basin and Range of the arid southwest. However, the algorithm was not applied to tectonic and climatic scenarios found outside of the arid Basin and Range, such as strike-slip faults or areas with higher precipitation. To compare this approach to traditional geomorphic mapping methodologies, we have applied the Scott et al. (2022) semi-automated algorithm to faults of varying tectonic and climatic conditions with preexisting geomorphic mapping, such as the strike-slip Rodgers Creek and the oblique-slip Quien Sabe Faults in Northern California. This work discusses how algorithmic mapping performs when applied to known seismogenic faults located outside of the arid Basin and Range as well as how such quantitative methods can be integrated with current subjective geomorphic mapping methods.