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Earthquake magnitude prediction using a VMD-BP neural network model

Author

Listed:
  • Jiaqi Zhang

    (Beijing Technology and Business University (BTBU))

  • Xijun He

    (Beijing Technology and Business University (BTBU))

Abstract

Earthquakes instantaneously occur and can cause huge disasters to cities, villages, and human beings. Therefore, it is of great significance to develop relevant theories and methods of earthquake prediction. This study builds a new model for seismic magnitude prediction, which uses a classic back propagation (BP) neural network combined with the variational mode decomposition (VMD) technique as a preprocessing for seismic dataset. The proposed model is referred to as VMD-BP. For each entry in the chronological earthquake catalog, three features are taken into consideration: magnitude, latitude, and longitude. The features of the past three adjacent seismic events are used as the input of the VMD-BP model, and the magnitude of the next seismic event is considered as the output. The VMD-BP model is then applied for seismic magnitude prediction in the Tibet and Yunnan regions. The results show that the VMD-BP model has high prediction accuracy, it performs better than the single BP neural network, and it can effectively predict the earthquake magnitude.

Suggested Citation

  • Jiaqi Zhang & Xijun He, 2023. "Earthquake magnitude prediction using a VMD-BP neural network model," 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. 117(1), pages 189-205, May.
    • Handle: RePEc:spr:nathaz:v:117:y:2023:i:1:d:10.1007_s11069-023-05856-8
    DOI: 10.1007/s11069-023-05856-8
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    References listed on IDEAS

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