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

被引：0
作者：
Fanchun Meng
Tao Ren
Enming Guo
Hongfeng Chen
Xinliang Liu
Haodong Zhang
Jiang Li
机构：
[1] Northeastern University,Software College
[2] Northeastern University,Computer Science and Engineering College
[3] China Earthquake Networks Center,undefined
来源：
Natural Hazards | 2024年 / 120卷
关键词：
Massive tectonic earthquakes; Ground motion parameter; Multi-task; Attention;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：6737 / 6754
页数：17
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