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Predicting the magnitude of injection-induced earthquakes using machine learning techniques

Author

Listed:
  • Javad N. Rashidi

    (University of Tehran)

  • Mehdi Ghassemieh

    (University of Tehran)

Abstract

Predicting the magnitude of induced earthquakes by underground injection is a critical strategy for risk assessment. This paper proposes the application of three machine learning techniques—support vector machine, probabilistic neural network, and AdaBoost algorithm—to predict the magnitude of the largest injection-induced earthquake (M) within a predetermined period. These machine learning techniques are used to model the relationships between ten input parameters—six seismicity indicators and four inputs related to injection wells—and earthquake magnitude classes (M

Suggested Citation

  • Javad N. Rashidi & Mehdi Ghassemieh, 2023. "Predicting the magnitude of injection-induced earthquakes using machine learning techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 545-570, August.
    • Handle: RePEc:spr:nathaz:v:118:y:2023:i:1:d:10.1007_s11069-023-06018-6
    DOI: 10.1007/s11069-023-06018-6
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    References listed on IDEAS

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    1. G. Zöller & S. Hainzl & J. Kurths & J. Zschau, 2002. "A Systematic Test on Precursory Seismic Quiescence in Armenia," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 26(3), pages 245-263, July.
    2. Cornelius Langenbruch & Matthew Weingarten & Mark D. Zoback, 2018. "Physics-based forecasting of man-made earthquake hazards in Oklahoma and Kansas," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    3. K. M. Asim & F. Martínez-Álvarez & A. Basit & T. Iqbal, 2017. "Earthquake magnitude prediction in Hindukush region using machine learning techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 85(1), pages 471-486, January.
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