Machine learning improves earthquake detection capabilities – Los Alamos reporter

Image showing improved slow earthquake detection in Turkey. Courtesy LANL

LANL News Release

Researchers at Los Alamos National Laboratory are applying machine learning algorithms to help interpret vast amounts of Earth deformation data collected using Synthetic Aperture Radar (InSAR) satellites; 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 Roet Leduc, a geophysicist with the Geophysics Group at Los Alamos. “This is critical to understanding the full range 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 weren’t possible in the past. However, current algorithms are not suitable for the huge amount of InSAR data streaming from these new satellites, and more data will be available in the near future.

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

Systematic characterization of slip behavior on active faults is fundamental to unraveling the physics of tectonic faults, and will help researchers understand the interaction between slow earthquakes, which gently release stress, and fast earthquakes, which release stress quickly and can cause significant damage to surrounding communities.

The team’s new methodology makes it possible to automatically detect Earth deformation on a global scale, with temporal resolution much finer than current methods, and a detection threshold of a few millimeters. Previous detection thresholds were in the centimeter range.

In the preliminary results of the approach, applied to the data on the North Anatolian Fault, the method reaches a detection of 2 mm, revealing twice as slow seismicity as previously recognized.

The results are detailed in the paper “Self-extracting millimeter-scale deformation in an InSAR time series using deep learning,” published in Nature Communications, DOI: 10.1038/s41467-021-26254-3

Funding: This work was funded by the Los Alamos National Laboratory’s Office of Research and Development.

About Los Alamos National Laboratory

Los Alamos National Laboratory, a multidisciplinary research institution engaged in strategic sciences on behalf of national security, is managed by Triad, a public service-oriented national security science organization that is equally owned by its three founding members: Battle Memorial Institute (Battle), Texas The A&M University System (TAMUS), and the University of California (UC) Regents of the Department of Energy’s National Nuclear Security Administration.

Los Alamos enhances national security by ensuring the safety and reliability of the US nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.

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