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

Author

Listed:
  • Chen Cao

    (School of Geosciences and Info-Physics, Central South University, Changsha 410012, China
    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Changsha 410012, China)

  • Xiangbin Wu

    (School of Geosciences and Info-Physics, Central South University, Changsha 410012, China
    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Changsha 410012, China)

  • Lizhi Yang

    (School of Geosciences and Info-Physics, Central South University, Changsha 410012, China
    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Changsha 410012, China)

  • Qian Zhang

    (School of Geosciences and Info-Physics, Central South University, Changsha 410012, China
    Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Changsha 410012, China)

  • Xianying Wang

    (Guangzhou Marine Geological Survey, Guangzhou 510760, China)

  • David A. Yuen

    (Department of Applied Physics and Applied Mathematics, Columbia University, New York, NY 10026, USA
    Department of Big Data, School of Computer Science, China University of Geosciences, Wuhan 430074, China)

  • Gang Luo

    (School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
    Key Laboratory of Geospace Environment and Geodesy, Wuhan University, Wuhan 430079, China)

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.

Suggested Citation

  • Chen Cao & Xiangbin Wu & Lizhi Yang & Qian Zhang & Xianying Wang & David A. Yuen & Gang Luo, 2021. "Long Short-Term Memory Networks for Pattern Recognition of Synthetical Complete Earthquake Catalog," Sustainability, MDPI, vol. 13(9), pages 1-13, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4905-:d:544476
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    References listed on IDEAS

    as
    1. Yosihiko Ogata, 1998. "Space-Time Point-Process Models for Earthquake Occurrences," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(2), pages 379-402, June.
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