基于长短期记忆神经网络的实时地震烈度预测模型

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 Pwave 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.