SeisT: A Foundational Deep-Learning Model for Earthquake Monitoring Tasks

Seismograms, the fundamental seismic records, have revolutionized earthquake research and monitoring. Recent advancements in deep learning have further enhanced seismic signal processing, leading to even more precise and effective earthquake-monitoring capabilities. This article introduces a foundat...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2024, Vol.62, p.1-15
Main Authors: Li, Sen, Yang, Xu, Cao, Anye, Wang, Changbin, Liu, Yaoqi, Liu, Yapeng, Niu, Qiang
Format: Article
Language:English
Subjects:
Azimuth
Back-azimuth estimation
Convolution
Deep learning
Earthquakes
epicentral distance estimation
Estimation
Feature extraction
first-motion polarity classification
magnitude estimation
Monitoring
phase picking
Seismic activity
seismogram
Seismograms
Signal processing
Task analysis
Transformers
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Summary:Seismograms, the fundamental seismic records, have revolutionized earthquake research and monitoring. Recent advancements in deep learning have further enhanced seismic signal processing, leading to even more precise and effective earthquake-monitoring capabilities. This article introduces a foundational deep-learning model, the seismogram transformer (SeisT), designed for a variety of earthquake-monitoring tasks. SeisT combines multiple modules tailored to different tasks and exhibits impressive out-of-distribution generalization performance, outperforming or matching state-of-the-art models in tasks like earthquake detection, seismic phase picking, first-motion polarity classification, magnitude estimation, back-azimuth estimation, and epicentral distance estimation. The performance scores on the tasks are 0.96, 0.96, 0.68, 0.95, 0.86, 0.55, and 0.81, respectively. The most significant improvements, in comparison to existing models, are observed in phase-P picking, phase-S picking, and magnitude estimation, with gains of 1.7%, 9.5%, and 8.0%, respectively. Our study, through rigorous experiments and evaluations, suggests that SeisT has the potential to contribute to the advancement of seismic signal processing and earthquake research.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3371503