Autonomous Extraction of Millimeter-scale Deformation in InSAR Time Series Using Deep Learning
| Autor: | Claudia Hulbert, Paul A. Johnson, Manon Dalaison, Romain Jolivet, Bertrand Rouet-Leduc |
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| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Science General Physics and Astronomy North Anatolian Fault FOS: Physical sciences Slip (materials science) Active fault Deformation (meteorology) Fault (geology) 010502 geochemistry & geophysics 01 natural sciences General Biochemistry Genetics and Molecular Biology Article Physics - Geophysics Slow earthquake Interferometric synthetic aperture radar 0105 earth and related environmental sciences geography Multidisciplinary geography.geographical_feature_category Tectonics General Chemistry Geodesy Computer science Geophysics (physics.geo-ph) Geophysics |
| Zdroj: | Nature Communications, Vol 12, Iss 1, Pp 1-11 (2021) Nature Communications |
| DOI: | 10.48550/arxiv.2012.13849 |
| Popis: | Systematically characterizing slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here, we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault’s location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California. A deep neural network is developed to automatically extract ground deformation from Interferometric Synthetic Aperture Radar time series. Applied to data over the North Anatolian Fault, the method can detect 2 mm deformation transients and reveals a slow earthquake twice as extensive as previously recognized. |
| Databáze: | OpenAIRE |
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