Machine learning refines earthquake detection capabilities
New methodology enables the detection of ground deformation automatically
at a global scale

Date:
November 11, 2021
Source:
DOE/Los Alamos National Laboratory
Summary:
Researchers are applying machine learning algorithms to help
interpret massive amounts of ground deformation data collected
with Interferometric Synthetic Aperture Radar (InSAR) satellites;
the new algorithms will improve earthquake detection.



FULL STORY ========================================================================== Researchers at Los Alamos National Laboratory are applying machine
learning algorithms to help interpret massive amounts of ground
deformation data collected with Interferometric Synthetic Aperture Radar (InSAR) satellites; the new algorithms will improve earthquake detection.


========================================================================== "Applying machine learning to InSAR data gives us a new way to understand
the physics behind tectonic faults and earthquakes," said Bertrand
Rouet-Leduc, a geophysicist in Los Alamos' Geophysics group. "That's
crucial to understanding the full spectrum of earthquake behavior."
New satellites, such as the Sentinel 1 Satellite Constellation and
the upcoming NISAR Satellite, are opening a new window into tectonic
processes by allowing researchers to observe length and time scales that
were not possible in the past. However, existing algorithms are not suited
for the vast amount of InSAR data flowing in from these new satellites,
and even more data will be available in the near future.

In order to process all of this data, the team at Los Alamos developed
the first tool based on machine learning algorithms to extract ground deformation from InSAR data, which enables the detection of ground
deformation automatically -- without human intervention -- at a global
scale. Equipped with autonomous detection of deformation on faults,
this tool can help close the gap in existing detection capabilities and
form the foundations for a systematic exploration of the properties of
active faults.

Systematically characterizing slip behavior on active faults is key to unraveling the physics of tectonic faulting, and will help researchers understand the interplay between slow earthquakes, which gently release
stress, and fast earthquakes, which quickly release stress and can cause significant damage to surrounding communities.

The team's new methodology enables the detection of ground deformation automatically at a global scale, with a much finer temporal resolution
than existing approaches, and a detection threshold of a few
millimeters. Previous detection thresholds were in the centimeter range.

In preliminary results of the approach, applied to data over the North Anatolian Fault, the method reaches two millimeter detection, revealing
a slow earthquakes twice as extensive as previously recognized.

This work was funded through Los Alamos National Laboratory's Laboratory Directed Research and Development Office.

========================================================================== Story Source: Materials provided by
DOE/Los_Alamos_National_Laboratory. Note: Content may be edited for
style and length.


========================================================================== Journal Reference:
1. Bertrand Rouet-Leduc, Romain Jolivet, Manon Dalaison, Paul
A. Johnson,
Claudia Hulbert. Autonomous extraction of millimeter-scale
deformation in InSAR time series using deep learning. Nature
Communications, 2021; 12 (1) DOI: 10.1038/s41467-021-26254-3 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211111130420.htm

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