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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Abstract

Rapid and automated reporting of earthquake focal mechanisms is crucial for understanding faulting geometry, stress changes, and aftershock patterns. This paper introduces Focal Mechanism Network (FMNet), a deep learning method trained on synthetic data to estimate focal mechanisms in real-time. FMNet successfully predicted focal mechanisms for four 2019 Ridgecrest earthquakes (Mw > 5.4), demonstrating its potential for application in regions with limited historical data. Prediction time is under 200 milliseconds on a single CPU.

Publisher

Nature Communications

Published On

Mar 04, 2021

Authors

Wenhuan Kuang, Congcong Yuan, Jie Zhang