基于长短期记忆神经网络的实时地震烈度预测模型

A Real-Time Seismic Intensity Prediction Model Based on Long Short-Term Memory Neural Network

实时烈度预测可在破坏性地震波到达前,根据P波估计地震可能造成的最大影响.预警对象可以采取措施,降低可能造成的损失.P波位移幅值是一种有效估计地震动峰值的参数,然而单个或多个参数难以全面表征地震动中的信息.同时,参数的计算需要确定时间窗大小,无法实现连续预测.为了解决上述问题,提出了一种基于长短期记忆网络的实时地震烈度预测模型.基于2010-2021年K-NET数据构建模型,并选取2022年3月MJMA7.3地震事件作为案例验证模型.结果表明,P波到达后可以在记录的每个时间步预测烈度,P波到达3 s时在测试集中准确率为96.47%.提出的LSTM模型改善了烈度预测的准确性和连续性,可为地震预警、应急响应等提供科学依据.

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 MJMA 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.