Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking

S. Mostafa Mousavi (), William L. Ellsworth, Weiqiang Zhu, Lindsay Y. Chuang and Gregory C. Beroza
Additional contact information
S. Mostafa Mousavi: Stanford University
William L. Ellsworth: Stanford University
Weiqiang Zhu: Stanford University
Lindsay Y. Chuang: Georgia Institute of Technology
Gregory C. Beroza: Stanford University

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: Abstract Earthquake signal detection and seismic phase picking are challenging tasks in the processing of noisy data and the monitoring of microearthquakes. Here we present a global deep-learning model for simultaneous earthquake detection and phase picking. Performing these two related tasks in tandem improves model performance in each individual task by combining information in phases and in the full waveform of earthquake signals by using a hierarchical attention mechanism. We show that our model outperforms previous deep-learning and traditional phase-picking and detection algorithms. Applying our model to 5 weeks of continuous data recorded during 2000 Tottori earthquakes in Japan, we were able to detect and locate two times more earthquakes using only a portion (less than 1/3) of seismic stations. Our model picks P and S phases with precision close to manual picks by human analysts; however, its high efficiency and higher sensitivity can result in detecting and characterizing more and smaller events.

Date: 2020
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DOI: 10.1038/s41467-020-17591-w

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