Prediction of PGA in earthquake early warning using a long short-term memory neural network. Issue 1 (14th February 2023)

• Record Type: Journal Article
• Authors: Wang, Ao; Li, Shanyou; Lu, Jianqi; Zhang, Haifeng; Wang, Borui; Xie, Zhinan
• Is Part Of: Geophysical journal international. Volume 234:Issue 1(2023)
• Page Start: 12
• Page End: 24
• Publication Date: 2023-02-14
• Subjects: Time-series analysis -- Machine learning -- Neural networks fuzzy logic -- Earthquake early warning -- Earthquake hazards; Geophysics -- Periodicals; 550
• DOI: 10.1093/gji/ggad067
• Languages: English
• ISSNs: 0956-540X

Abstract

SUMMARY: Peak ground acceleration (PGA) is a key parameter used in earthquake early warning systems to measure the ground motion strength and initiate emergency protocols at major projects. The traditional P -wave peak displacement-dependent PGA prediction model (Pd-PGA model) tends to underestimate the PGA for large earthquakes because it cannot make full use of the fault continuity rupture information hidden in the time-varying process of ground motion. In this paper, a continuous PGA prediction long short-term memory (LSTM) neural network model is proposed. The model takes eight sequential features of stations that are proxies of the energy and other physical parameters as input and provides the recorded PGA at the station as the target output. A total of 5961 records from 119 earthquakes recorded by the Japanese Strong-Motion Earthquake Network (K-NET) in Japan are used to train the neural network and 3433 records from 73 earthquakes are used as the test set to verify the model's generalization ability. The results show that within the same data set, the residuals of the predicted PGA for the proposed model are smaller than those of the Pd-PGA model and that the problem of PGA underestimation is resolved. The prediction accuracy also improves with increasing sequence length, which indicates that the LSTM neural network learns the rules hidden in the time series. To further verify the model's generalization ability, the model performance is analyzed for an M 7.3 earthquake that was not included in the training or test data sets. The results show that the residuals of the predicted PGA for the event are consistent with those for the test data set, indicating that the model has good generalization ability.