摘要
2022年6月1日四川省芦山县发生6.1级地震,6月10日四川省马尔康市又相继发生了5.8级和6.0级地震。该文提出了基于机器学习的地震预测框架,用于地震预警震级连续估计和台站现地仪器地震烈度预测。基于机器学习中的支持向量机方法,为了探索该地震预测框架对这3次地震的可行性,该文利用这3次地震获得的加速度记录,对地震预警震级连续估计和台站现地仪器地震烈度预测进行离线模拟。在此基础上,通过设置不同的仪器地震烈度阈值,分析了这3次地震报警的准确性。结果展示:对于这3次地震事件,与现有地震预警系统相比,该文的支持向量机方法在震级估计中表现出更加鲁棒的性能;且在首台触发后10s内,震级估计误差不超过±0.6震级单位。P波到达后3 s,现地仪器地震烈度预测误差在±1度的百分比达到了75.9%,平均绝对误差为0.7度;仪器地震烈度报警阈值为Ⅵ度和Ⅶ度时,报警成功的百分比分别达到了98.4%和100.0%。该文的研究结果表明:机器学习方法在中国地震预警系统中存在应用的潜力,也为地震预警系统的升级换代提供了参考。
On June 1,2022,a M6.1 earthquake occurred in Lushan County,Sichuan,and on June 10,another M5.8 earthquake and M6.0 earthquake occurred in Ma erkang City,Sichuan.This paper proposes an earthquake prediction framework based on machine learning,which is used for the continuous magnitude estimation and the prediction of on-site instrumental seismic intensity in earthquake early warning(EEW).Based on the support vector machine(SVM)method,to explore the feasibility of the framework for the three earthquakes,this paper uses the acceleration records obtained from these three earthquakes to perform offline simulation.Meanwhile,this paper sets different instrumental seismic intensity thresholds to analyze the alarm accuracy.The results show that for the three earthquakes,compared with the existing EEW system,the SVM method exhibits more robust performance in magnitude estimation.And within 10 s after the first station is triggered,the magnitude estimation error is within±0.6 magnitude units.At 3 s after the P-wave arrival,the percentage of the prediction error(within±1)of on-site instrumental seismic intensity reaches 75.9%,and the average absolute error is 0.7;When the alarm thresholds of instrumental seismic intensity are VI and VII,the percentage of successful alarm reaches 98.4% and 100.0%,respectively.The results indicate that the machine learning method has the potential for application in Chinese EEW system,and provides a reference for the upgrading of EEW system.
作者
朱景宝
教聪聪
韦永祥
宋晋东
李山有
ZHU Jing-bao;JIAO Cong-cong;WEI Yong-xiang;SONG Jin-dong;LI Shan-you(Key Laboratory of Earthquake Engineering and Engineering Vibration,Institute of Engineering Mechanics,China Earthquake Administration,Harbin,Heilongjiang 150080,China;Key Laboratory of Earthquake Disaster Mitigation,Ministry of Emergency Management,Harbin,Heilongjiang 150080,China;Earthquake Administration of Shanghai Municipality,Shanghai 200062,China;Shanghai Sheshan National Geophysical Observatory,Shanghai 200062,China;Fujian Earthquake Agency,Fuzhou,Fujian 350003,China)
出处
《工程力学》
北大核心
2025年第4期56-68,共13页
Engineering Mechanics
基金
黑龙江省自然科学基金项目(LH2021E119)
国家自然科学基金项目(42304074,U2039209,51408564)
中国铁道科学研究院集团有限公司科研项目(2022YJ149)
地震科技星火计划项目(XH23027YB)
国家重点研发计划项目(2018YFC1504003)。
关键词
地震预警
机器学习
震级
现地
仪器地震烈度
earthquake early warning
machine learning
magnitude
on-site
instrumental seismic intensity
