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Real-time determination of earthquake focal mechanism via deep learning

Earth Sciences

Real-time determination of earthquake focal mechanism via deep learning

W. Kuang, C. Yuan, et al.

Rapid and automated reporting of earthquake focal mechanisms is now possible with FMNet, a deep learning method that can predict focal mechanisms in real-time, showcasing its promise in regions lacking historical data. This groundbreaking research was conducted by Wenhuan Kuang, Congcong Yuan, and Jie Zhang.

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~3 min • Beginner • English
Abstract
An immediate report of an earthquake’s focal mechanism is crucial for characterizing fault geometry, assessing stress changes, and anticipating aftershock patterns. Here, a deep learning method, Focal Mechanism Network (FMNet), is proposed to determine focal mechanisms in real time from full waveforms. FMNet is trained on 787,320 synthetic samples and successfully estimates focal mechanisms for four Mw ≥ 5.4 events in the 2019 Ridgecrest sequence. Trained on theoretical data, the network learns global waveform characteristics, enabling application in regions with limited historical data. After data arrival and preprocessing, prediction takes under ~200 ms on a single CPU.
Publisher
Nature Communications
Published On
Mar 04, 2021
Authors
Wenhuan Kuang, Congcong Yuan, Jie Zhang
DOI
https://doi.org/https://doi.org/10.1038/s41467-021-21670-x
Tags
earthquake
focal mechanisms
deep learning
real-time prediction
FMNet
synthetic data
Ridgecrest earthquakes

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