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

Abstract

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.

Authors:
 [1];  [1];  [1];  [1]; ORCiD logo [2];  [3];  [4]
+ Show Author Affiliations
  1. Central South Univ., Changsha (China)
  2. Guangzhou Marine Geological Survey, Guangzho (China)
  3. Columbia Univ., New York, NY (United States); China Univ. of Geosciences, Wuhan (China)
  4. Wuhan Univ. (China)
Publication Date:
Research Org.:
Columbia Univ., New York, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC); National Natural Science Foundation of China (NSFC); Ministry of Science and Technology of China
OSTI Identifier:
1853393
Grant/Contract Number:  
SC0019759; 41974107; 2019CSES0112; 2018YFC0603500; 2016YFC0600310
Resource Type:
Accepted Manuscript
Journal Name:
Sustainability (Basel)
Additional Journal Information:
Journal Name: Sustainability (Basel); Journal Volume: 13; Journal Issue: 9; Journal ID: ISSN 2071-1050
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; long short-term memory networks; pattern recognition; earthquake catalog; physics-based simulation

Citation Formats

Cao, Chen, Wu, Xiangbin, Yang, Lizhi, Zhang, Qian, Wang, Xianying, Yuen, David A., and Luo, Gang. Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog. United States: N. p., 2021. Web. doi:10.3390/su13094905.
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Cao, Chen, Wu, Xiangbin, Yang, Lizhi, Zhang, Qian, Wang, Xianying, Yuen, David A., & Luo, Gang. Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog. United States. https://doi.org/10.3390/su13094905
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Cao, Chen, Wu, Xiangbin, Yang, Lizhi, Zhang, Qian, Wang, Xianying, Yuen, David A., and Luo, Gang. Tue . "Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog". United States. https://doi.org/10.3390/su13094905. https://www.osti.gov/servlets/purl/1853393.
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@article{osti_1853393,
title = {Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog},
author = {Cao, Chen and Wu, Xiangbin and Yang, Lizhi and Zhang, Qian and Wang, Xianying and Yuen, David A. and Luo, Gang},
abstractNote = {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.},
doi = {10.3390/su13094905},
journal = {Sustainability (Basel)},
number = 9,
volume = 13,
place = {United States},
year = {Tue Apr 27 00:00:00 EDT 2021},
month = {Tue Apr 27 00:00:00 EDT 2021}
}
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https://doi.org/10.3390/su13094905
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