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A Real-Time Seismic Intensity Prediction Model Based on Long Short-Term Memory Neural Network
Hu Jinjun, Ding Yitian, Zhang Hui, Jin Chaoyue, Tang Chao
PDF(1992 KB)
PDF(1992 KB)
A Real-Time Seismic Intensity Prediction Model Based on Long Short-Term Memory Neural Network
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Real-time intensity prediction can estimate the maximum possible impact of an earthquake based on P-wave before the arrival of destructive seismic waves. Earthquake early warning targets can take measures to reduce the potential damage. Peak P-wave displacement amplitude is a parameter that effectively estimates the peak ground motion, however, it is difficult to fully characterize the information in ground motion by a single or multiple parameters. Meanwhile, the calculation of the parameter requires the determination of the time window size, and continuous prediction cannot be achieved. To solve the above problems, a prediction model based on long short-term memory network is proposed in this paper. The model is constructed based on K-NET data from 2010‒2021, and the M JMA 7.3 earthquake event in March 2022 is selected as a case to validate the model. The results show that the intensity can be predicted at each time step of the record after the P-wave arrival, and the accuracy in the test set is 96.47% at 3 seconds after P-wave arrival. The LSTM model proposed in this paper improves the accuracy and continuity of intensity prediction and can provide a scientific basis for earthquake early warning and emergency response.
seismic intensity / real time / neural network / deep learning / earthquake early warning / engineering geology
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