Utilizing Remote Sensing and Site Reconnaissance Data to Map Surface Manifestation of Liquefaction

Following the 2019 Ridgecrest (California) earthquake sequence, the Geotechnical Extreme Events Reconnaissance (GEER) association deployed and coordinated a reconnaissance effort that included teams funded by National Aeronautics and Space Administration (NASA), the U.S. Geological Survey (USGS), the California Geological Survey (CGS), and the U.S. Navy to document ground failure that had occurred at China Lake, Searles Lake, and surrounding areas. At Searles Lake, the teams found locations with ejecta and locations without surface manifestation, although the reconnaissance was relatively rapid and most areas around the lake were not observed. Accordingly, two other data sources have been considered to develop a more complete spatial representation of ground failure: (1) Damage Proxy Maps (DPMs) based on the analysis of multi-epoch synthetic aperture radar (SAR) data and (2) optical (visible and near-infrared) satellite images. The Searles Lakebed lacks vegetation and is relatively level in elevation, making it an ideal location for using the remote sensing data. We begin by training a machine learning algorithm to detect the presence of ejecta from the optical satellite images. This is used to generate post-event maps of surface manifestation at the same resolution as the satellite images. Ultimately, we plan to validate the DPMs against such maps to facilitate future applications in which DPMs can be used for rapid post-event ground failure detection and loss estimation.