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基于长短期记忆神经网络的实时地震烈度预测模型
胡进军, 丁祎天, 张辉, 靳超越, 汤超
PDF(1992 KB)
PDF(1992 KB)
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
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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M JMA7.3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 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.
地震烈度 / 实时 / 神经网络 / 深度学习 / 地震预警 / 工程地质
seismic intensity / real time / neural network / deep learning / earthquake early warning / engineering geology
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