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Systematic characterization of slip behaviours on active faults is key tounraveling the physics of tectonic faulting and the interplay between slow andfast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enablingmeasurement of ground deformation at a global scale every few days, may holdthe key to those interactions. However, atmospheric propagation delays oftenexceed ground deformation of interest despite state-of-the art processing, andthus InSAR analysis requires expert interpretation and a priori knowledge offault systems, precluding global investigations of deformation dynamics. Herewe show that a deep auto-encoder architecture tailored to untangle grounddeformation from noise in InSAR time series autonomously extracts deformationsignals, without prior knowledge of a fault's location or slip behaviour.Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mmdetection, revealing a slow earthquake twice as extensive as previouslyrecognized. We further explore the generalization of our approach toinflation/deflation-induced deformation, applying the same methodology to thegeothermal field of Coso, California.

Autonomous Extraction of Millimeter-scale Deformation in InSAR Time Series Using Deep Learning