Quantitative Validation of NASA ARIA Damage Proxy Maps for Detection of Ground Displacement from Surface Fault Rupture

National Aeronautics and Space Administration Advanced Rapid Imaging Analysis 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 gather data from the Sentinal-1 satellite approximately every 12 days, meaning that results can generally 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 (I_DPM) for different fault displacement ranges in the form of box and whisker plots, and (2) empirical fragility functions that relate I_DPM 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 I_DPM metrics, as measured by a dispersion term in the fragility model, shows the best performance for low threshold displacements and DPM2-based indices. Recall and precision error metrics show favorable performance of fragility models for identifying locations of fault displacement, but increasing rates of false positives as fault displacement increases. These results demonstrate both the capabilities and limitations of I_DPM 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).