Feature Detection in SAR Interferograms With Missing Data Displays Fault Slip Near El Mayor-Cucapah and South Napa Earthquakes

Abstract:

Edge detection identifies seismic or aseismic fault motion, as demonstrated in repeat-pass inteferograms obtained by the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) program. But this identification, demonstrated in 2010, was not robust: for best results, it requires a flattened background image, interpolation into missing data (holes) and outliers, and background noise that is either sufficiently small or roughly white Gaussian. Proper treatment of missing data, bursting noise patches, and tiny noise differences at short distances apart from bursts are essential to creating an acceptably reliable method sensitive to small near-surface fractures. Clearly a robust method is needed for machine scanning of the thousands of UAVSAR repeat-pass interferograms for evidence of fault slip, landslides, and other local features: hand-crafted intervention will not do.

Effective methods of identifying, removing and filling in bad pixels reveal significant features of surface fractures. A rich network of edges (probably fractures and subsidence) in difference images spanning the South Napa earthquake give way to a simple set of postseismically slipping faults. Coseismic El Mayor-Cucapah interferograms compared to post-seismic difference images show nearly disjoint patterns of surface fractures in California's Sonoran Desert; the combined pattern reveals a network of near-perpendicular, probably conjugate faults not mapped before the earthquake. The current algorithms for UAVSAR interferogram edge detections are shown to be effective in difficult environments, including agricultural (Napa, Imperial Valley) and difficult urban areas (Orange County.).