基于混合神经网络的高速铁路地震预警方法  

Earthquake Early Warning for High-speed Railway Based on Hybrid Neural Network

作　　者：朱景宝 孙文韬 周学影 姚鹍鹏 李山有[1,2] 宋晋东 ZHU Jingbao;SUN Wentao;ZHOU Xueying;YAO Kunpeng;LI Shanyou;SONG Jindong(Key Laboratory of Earthquake Engineering and Engineering Vibration,Institute of Engineering Mechanics,China Earthquake Administration,Harbin 150080,China;Key Laboratory of Earthquake Disaster Mitigation,Ministry of Emergency Management,Harbin 150080,China;Railway Science and Technology Research and Development Center,China Academy of Railway Sciences Corporation Limited,Beijing 100081,China;Department of Security Products,Henan Splendor Science&Technology Co.,Ltd.,Zhengzhou 450012,China)

机构地区：[1]中国地震局工程力学研究所地震工程与工程振动重点实验室,黑龙江哈尔滨150080 [2]地震灾害防治应急管理部重点实验室,黑龙江哈尔滨150080 [3]中国铁道科学研究院集团有限公司铁道科学技术研究发展中心,北京100081 [4]河南辉煌科技股份有限公司安防产品部,河南郑州450012

出　　处：《铁道学报》2025年第3期178-190,共13页Journal of the China Railway Society

基　　金：中国地震局工程力学研究所基本科研业务费(2024C05);国家自然科学基金(U2039209);中国铁道科学研究院集团有限公司科研项目(2022YJ149)。

摘　　要：破坏性地震对高速铁路安全会造成严重的影响。为使高速铁路地震预警系统快速获得可靠的Ⅰ级地震警报,基于日本K-NET台网记录的强震动数据,提出一种基于震级阈值(M=5.5)和峰值地震加速度阈值(PGA=40 cm/s^(2))的深度学习高速铁路现地阈值地震预警方法。设计一种结合卷积神经网络、循环神经网络和注意力机制的混合神经网络架构,并在该网络架构的基础上,训练两个独立的模型,分别用于预测震级和峰值地震加速度是否超过阈值。同时,根据震级阈值和峰值地震加速度阈值设置4个警报预测级别。基于混合神经网络模型对震级和峰值地震加速度的预测,每个台站将发布相应的警报预测级别。警报预测级别4表示台站附近有高震级事件且存在潜在破坏。在P波到达后3 s,对于相同的测试数据集,和基线模型相比,混合神经网络模型对于震级预测和峰值地震加速度预测有更好的性能,且震级预测的准确率达到97.26%,PGA预测的准确率达到98.89%,警报预测级别的准确率到达96.31%。将该方法应用于2021年2月13日发生于日本福岛的7.3级地震,结果表明,在P波到达后10 s内,警报预测级别的准确率达到90%,平均预警时间超过19 s。Destructive earthquakes can have a serious impact on the safety of high-speed railways.To quickly and reliably obtain first-level earthquake alerts for the high-speed railway earthquake warning(EEW)system,based on the strong motion data recorded by the K-NET network in Japan,this paper proposed a deep learning on-site threshold EEW method for high-speed railway based on the magnitude threshold(M=5.5)and peak ground acceleration threshold(PGA=40 cm/s^(2)).A hybrid neural network architecture was designed combining convolutional neural network,recurrent neural network and attention mechanism.Based on the architecture,two separate models were trained to respectively predict the magnitude and PGA exceeding the thresholds.Meanwhile,four alert prediction levels were set based on the magnitude threshold and PGA threshold.Based on the predictions of magnitude and peak ground acceleration by the hybrid neural network models,each station will issue corresponding alert prediction levels.Alert prediction level 4 indicates the presence of a high magnitude event and potential damage near the station.At 3 s after the arrival of the P-wave,for the same test dataset,compared with the baseline models,the hybrid neural network models have better performance in magnitude prediction and peak ground acceleration prediction,with the accuracy of magnitude prediction of 97.26%,peak ground acceleration prediction prediction accuracy of 98.89%,and alert prediction level accuracy of 96.31%.To verify the reliability of the proposed method,the method was applied to the Off Fukushima earthquake(M=7.3)that occurred on February 13,2021 in Japan.The results show that within 10 s after the arrival of the P-wave,the accuracy of the alert prediction level reaches 90%,with the average warning time exceeding 19 s.

关 键 词：高速铁路 地震预警 神经网络 震级 峰值地震加速度 

分 类 号：U238[交通运输工程—道路与铁道工程] P315.7[天文地球—地震学]