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

Javad N. Rashidi and Mehdi Ghassemieh ()
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Javad N. Rashidi: University of Tehran
Mehdi Ghassemieh: University of Tehran

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 118, issue 1, No 22, 545-570

Abstract: 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

Keywords: Injection-induced earthquakes; Support vector machine; AdaBoost algorithm; Probabilistic neural network; Imbalanced data (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-023-06018-6

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