Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog

Cao, Chen; Wu, Xiangbin; Yang, Lizhi; Zhang, Qian; Wang, Xianying; Yuen, David A.; Luo, Gang

doi:10.3390/su13094905

Title: Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog

Journal Article · Tue Apr 27 00:00:00 EDT 2021 · Sustainability (Basel)

DOI:https://doi.org/10.3390/su13094905· OSTI ID:1853393

Cao, Chen ^[1]; Wu, Xiangbin ^[1]; Yang, Lizhi ^[1]; Zhang, Qian ^[1];

^[2]; Yuen, David A. ^[3]; Luo, Gang ^[4]

Central South Univ., Changsha (China)
Guangzhou Marine Geological Survey, Guangzho (China)
Columbia Univ., New York, NY (United States); China Univ. of Geosciences, Wuhan (China)
Wuhan Univ. (China)

Exploring the spatiotemporal distribution of earthquake activity, especially earthquake migration of fault systems, can greatly to understand the basic mechanics of earthquakes and the assessment of earthquake risk. By establishing a three-dimensional strike-slip fault model, to derive the stress response and fault slip along the fault under regional stress conditions. Our study helps to create a long-term, complete earthquake catalog. We modelled Long-Short Term Memory (LSTM) networks for pattern recognition of the synthetical earthquake catalog. The performance of the models was compared using the mean-square error (MSE). Our results showed clearly the application of LSTM showed a meaningful result of 0.08% in the MSE values. Our best model can predict the time and magnitude of the earthquakes with a magnitude greater than Mw = 6.5 with a similar clustering period. These results showed conclusively that applying LSTM in a spatiotemporal series prediction provides a potential application in the study of earthquake mechanics and forecasting of major earthquake events.

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Research Organization:: Columbia Univ., New York, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC); National Natural Science Foundation of China (NSFC); Ministry of Science and Technology of China

Grant/Contract Number:: SC0019759; 41974107; 2019CSES0112; 2018YFC0603500; 2016YFC0600310

OSTI ID:: 1853393

Journal Information:: Sustainability (Basel), Vol. 13, Issue 9; ISSN 2071-1050

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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