Quantitative validation of NASA ARIA damage proxy maps for detection of ground displacement from surface fault rupture from the 2019 Ridgecrest earthquake sequence

National Aeronautics and Space Administration Advanced Rapid Imaging Analysis (ARIA) Damage Proxy Maps (DPMs) are developed by the NASA Jet Propulsion Laboratory (NASA JPL) to identify potentially damaged areas based on interferometric coherence loss in Synthetic Aperture Radar (SAR) data. DPMs are typically based on data from Sentinel-1 satellites that orbit every 12 days, meaning that results can be provided within 1-2 weeks of an event. Although DPMs have been qualitatively validated as being able to detect surface effects of earthquakes, quantitative validations of their ability to differentiate damaged from undamaged areas and different types and levels of surface effects are lacking. We propose a framework for quantitative validation and apply it to surface fault rupture data from the 2019 Ridgecrest Earthquake sequence. The quantitative analyses take two forms: (1) the statistical distribution of a DPM index ( (Formula presented) ) for different fault displacement ranges through box and whisker plots, and (2) empirical fragility functions that relate (Formula presented) to probabilities of displacements exceeding certain thresholds. These relationships are developed for DPM1 (derived from one pre-event pair and one co-event pair of SAR images) and DPM2 (derived from multiple pre-event and co-event pairs of SAR images). We show that both DPM types perform similarly well for distinguishing between no surface displacement and some surface displacement. The predictive power of (Formula presented) metrics, as measured by a dispersion term in the fragility model, shows the best performance for low surface fault displacements and DPM2-based indices. Recall and precision performance metrics show favorable performance of fragility models for identifying locations of fault displacement but increasing rates of false positives as fault displacement increases (i.e. predictions of displacements exceeding a threshold that did not occur). While this validation study was performed for a single earthquake sequence involving a specific area of California, these results demonstrate both the capabilities and limitations of (Formula presented) as a rapid, post-event predictive tool for identifying locations and severity of ground displacements in environments similar to those in the Ridgecrest area (flat terrain, limited vegetation).