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Title: Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning

Abstract

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.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [1]; ORCiD logo [3]
+ Show Author Affiliations
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Paris Sciences et Lettres (PSL) Univ., Paris (France). Lab. de Géologie; Inst. Univ. de France, Paris (France)
  3. Paris Sciences et Lettres (PSL) Univ., Paris (France). Lab. de Géologie
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program; European Research Council (ERC)
OSTI Identifier:
1834522
Report Number(s):
LA-UR-19-27445
Journal ID: ISSN 2041-1723
Grant/Contract Number:  
89233218CNA000001; 758210
Resource Type:
Accepted Manuscript
Journal Name:
Nature Communications
Additional Journal Information:
Journal Volume: 12; Journal Issue: 1; Conference: AGU Fall Meeting 2019, 12/09/2019; Journal ID: ISSN 2041-1723
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; Computer science; Geophysics; Tectonics

Citation Formats

Rouet-Leduc, Bertrand, Jolivet, Romain, Dalaison, Manon, Johnson, Paul A., and Hulbert, Claudia. Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning. United States: N. p., 2021. Web. doi:10.1038/s41467-021-26254-3.
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Rouet-Leduc, Bertrand, Jolivet, Romain, Dalaison, Manon, Johnson, Paul A., & Hulbert, Claudia. Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning. United States. https://doi.org/10.1038/s41467-021-26254-3
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Rouet-Leduc, Bertrand, Jolivet, Romain, Dalaison, Manon, Johnson, Paul A., and Hulbert, Claudia. Wed . "Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning". United States. https://doi.org/10.1038/s41467-021-26254-3. https://www.osti.gov/servlets/purl/1834522.
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@article{osti_1834522,
title = {Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning},
author = {Rouet-Leduc, Bertrand and Jolivet, Romain and Dalaison, Manon and Johnson, Paul A. and Hulbert, Claudia},
abstractNote = {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.},
doi = {10.1038/s41467-021-26254-3},
journal = {Nature Communications},
number = 1,
volume = 12,
place = {United States},
year = {2021},
month = {11}
}
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https://doi.org/10.1038/s41467-021-26254-3
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