Estimation of ground motion parameters via multi-task deep neural networks

Fanchun Meng, Tao Ren (), Enming Guo, Hongfeng Chen, Xinliang Liu, Haodong Zhang and Jiang Li
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Fanchun Meng: Northeastern University
Tao Ren: Northeastern University
Enming Guo: Northeastern University
Hongfeng Chen: China Earthquake Networks Center
Xinliang Liu: Northeastern University
Haodong Zhang: Northeastern University
Jiang Li: Northeastern University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2024, vol. 120, issue 7, No 34, 6737-6754

Abstract: Abstract Ground motion parameters are crucial characteristics in earthquake warning and earthquake engineering practice. However, the existing methods are time-consuming and labor-intensive. In this study, a multi-task approach (GMP-MT) based on a hard parameter sharing strategy and single station data is proposed to improve the overall estimation accuracy by jointly optimizing the estimation of peak ground acceleration (PGA) and peak ground velocity (PGV). In addition, this study reshapes the mean squared error by adjusting the weight of the loss according to the data distribution to solve the data imbalance. The developed network structure extracts not only the seismic features from various dimensions but also the spatial–temporal correlations from large-dimensional seismic data. The designed model is trained and tested based on the global three-component seismic waveform data recorded in the STanford EArthquake Dataset. Experimental results show that the correlation coefficients of PGA and PGV are above 90%, and the average errors are less than 0.19. The model has good stability, specifically insensitive to epicenter distance, hypocentral depth, and signal-to-noise ratio. Furthermore, the superiority of the model in terms of learning and fitting is demonstrated by comparison with several state-of-the-art models in the existing literature.

Keywords: Massive tectonic earthquakes; Ground motion parameter; Multi-task; Attention (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11069-024-06464-w

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