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 US Geological Survey (USGS), the California Geological Survey (CGS), and the US 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 Lake 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 using observations from a small area to detect the presence of ejecta from the optical satellite images. Subsequent testing of the algorithm using observations from a broader area showed reasonably good results, but with larger rates of misidentification than in the training data. The algorithm in combination with the direct observations are used to generate post-event maps of surface manifestation at the same resolution as the satellite images, which are being used to validate the DPMs to facilitate future applications for rapid post-event ground failure detection and loss estimation.
Utilizing Remote Sensing and Site Reconnaissance Data to Map Surface Manifestation of Liquefaction
Zimmaro P.;
2023-01-01
Abstract
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 US Geological Survey (USGS), the California Geological Survey (CGS), and the US 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 Lake 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 using observations from a small area to detect the presence of ejecta from the optical satellite images. Subsequent testing of the algorithm using observations from a broader area showed reasonably good results, but with larger rates of misidentification than in the training data. The algorithm in combination with the direct observations are used to generate post-event maps of surface manifestation at the same resolution as the satellite images, which are being used to validate the DPMs to facilitate future applications for rapid post-event ground failure detection and loss estimation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
