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

• Published in: Natural hazards (Dordrecht) Vol. 120; no. 7; pp. 6737 - 6754
• Main Authors: Meng, Fanchun, Ren, Tao, Guo, Enming, Chen, Hongfeng, Liu, Xinliang, Zhang, Haodong, Li, Jiang
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.05.2024; Springer Nature B.V
• Subjects: Acceleration; Artificial neural networks; Civil Engineering; Correlation coefficient; Correlation coefficients; Depth perception; Earth and Environmental Science; Earth Sciences; Earthquake engineering; Earthquakes; Environmental Management; Error analysis; Estimation accuracy; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Ground motion; Hydrogeology; Machine learning; Natural Hazards; Neural networks; Original Paper; Parameters; Seismic activity; Seismic data; Seismic engineering; Seismic stability; Seismic waves; Seismological data; Signal to noise ratio; Waveforms; Massive tectonic earthquakes; Ground motion parameter; Multi-task; Attention
• ISSN: 0921-030X; 1573-0840
• DOI: 10.1007/s11069-024-06464-w

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.