摘要
随着地震动数据数量的增长和质量的提高,将基于数据驱动的机器学习方法应用到地震动模拟中有重要意义。以2021年5月21日云南漾濞M_(S)6.4地震为例,利用主成分析方法从前震及余震地震动记录中提取特征母波时程,将地震动三要素作为模拟误差约束,在求解母波的线性组合系数时使用多目标优化算法寻优,最终找到帕累托最优解作为模拟目标台站记录时的组合系数,得到模拟地震动时程。结果表明:主成分析法在对实际地震动记录进行特征提取后,得到的特征母波时程可以在一定程度上保留原始数据的主要信息;考虑幅值、频谱和持时这三要素的角度去控制模拟误差,可以使得模拟的地震动时程更加接近真实记录。提出的基于特征提取的地震动模拟方法可以为基于小震数据合成大震地震动提供参考。
With the increase of the quantity and quality of ground motion data,it is of great significance to apply data-driven machine learning method to ground motion simulation for the development of simulation methods.Taking the Yangbi M_(S)6.4 earthquake on May 21,2021 as an example,the principal component analysis method is used to extract the characteristic mother wave time histories from the foreshock-aftershock records.The three elements of ground motion are taken as the simulation error constraints,and the multi-objective optimization algorithm is used to solve the linear combination coefficients of the mother waves.Finally,the pareto optimal solution is found as the combined coefficients of the simulated target stations,and the simulated ground motion time histories are obtained.From the simulation results,it can be seen that:After feature extraction of actual ground motion records by principal component analysis,the characteristic mother waves obtained can retain the main information of original data;To control the simulation errors from the perspective of amplitude,spectrum and duration can make the simulated time histories of ground motion more close to the real records;The whole process of ground motion simulation can provide reference for the synthesis of large earthquake ground motion records based on small earthquake data.
作者
靳超越
胡进军
胡磊
王中伟
JIN Chaoyue;HU Jinjun;HU Lei;WANG Zhongwei(Key Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics,China Earthquake Administration, Harbin 150080, China)
出处
《世界地震工程》
CSCD
北大核心
2021年第4期73-80,共8页
World Earthquake Engineering
基金
国家自然科学基金重点项目(U1939210
52078470)
国家重点研发计划(2017YFC1500403)。
关键词
漾濞M_(S)6.4地震
地震动模拟
机器学习
特征提取
序列型地震
Yangbi M_(S)6.4 earthquake
simulation ground motion
machine learning
feature extraction
sequence-type ground motions
