Models for predicting ground motion significant duration based on deep learning algorithm
JIA Jia1,2, GONG Maosheng1,2, ZHAO Yinan1,2
1. Key Lab of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, China;
2. Key Lab of Earthquake Disaster Mitigation, Ministry of Emergency Management, Harbin 150080, China
Abstract：The development of a reliable prediction model is important in earthquake engineering. Based on the 67,813 ground motion records obtained by Japan K-NET and KIK-net from 1997 to 2021, the ground motion records are divided into four categories according to the source type: shallow crustal, subduction interface, subduction slab and upper mantle earthquakes. The duration prediction model for four types of earthquakes are established by using deep learning algorithm and compared with the traditional prediction equation. The results show that deep learning algorithm is reasonable and reliable, and can achieve accurate prediction results. There are obvious differences in the prediction results, especially in the case of large magnitude. The different prediction model should be used to predict the significant duration for different type of earthquakes. The results and conclusions can be used as reference for prediction of ground motion parameters, seismic zoning, seismic design and probabilistic analysis of seismic risk.
贾佳1,2，公茂盛1,2，赵一男1,2. 基于深度学习算法的地震动重要持时预测模型[J]. 振动与冲击, 2023, 42(19): 249-259.
JIA Jia1,2, GONG Maosheng1,2, ZHAO Yinan1,2. Models for predicting ground motion significant duration based on deep learning algorithm. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(19): 249-259.
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