胡安冬,
张海明^,
北京大学地球与空间科学学院地球物理系, 北京 100871

基金项目: 国家自然科学基金项目（4187407141）资助


详细信息

作者简介: 胡安冬, 男, 1993年生, 北京大学硕士研究生.E-mail:huandong@pku.edu.cn

通讯作者: 张海明, 男, 1976年生, 北京大学副教授, 2004年获得北京大学博士学位, 主要从事震源动力学与理论地震学研究.E-mail:zhanghm@pku.edu.cn

中图分类号: P315

收稿日期:2019-02-26
修回日期:2020-01-12
上线日期:2020-07-05

Application of machine learning to magnitude estimation in earthquake emergency prediction system

HU AnDong,
ZHANG HaiMing^,
School of Earth and Space Sciences, Peking University, Beijing 100871, China



More Information

Corresponding author: ZHANG HaiMing,E-mail:zhanghm@pku.edu.cn

MSC: P315

--> Received Date: 26 February 2019
Revised Date: 12 January 2020
Available Online: 05 July 2020


摘要
摘要:地震预警是地震减灾工作的重要途径，而震级预估是整个地震紧急预警系统中重要且较为困难的一个环节.目前，世界上多个国家和地区都已建立了各自的地震预警系统，并且形成了特征频率（τ_p和τ_c等）相关和特征振幅（P_d等）相关的两类震级紧急预警的方法，但各有局限性.本文在已有的方法和理论基础上，运用机器学习算法，将日本KIK和KNET台网从2015年至2017年所记录到的843条地震目录，55426条记录作为全数据集，设计、训练出一套用于常见震级范围的机器学习震级预估模型.与已有方法的预估结果相比，机器学习方法不仅使预估的整体误差和方差下降，同时多台联合评估单一地震事件的截面方差也更低.本研究的结果显示了机器学习算法在震级紧急预估问题上具有较广阔的应用前景，同时也为较为复杂的深度学习类算法框架下端到端模型提供了实践基础和研究可能.
关键词: 地震紧急预警/
震级预估/
机器学习/
深度学习

Abstract:Earthquake early warning (EEW) is an important way for earthquake disaster reduction,and magnitude estimation is an important and difficult part of the entire EEW system. Nowadays,many countries and regions around the world have established their own EEW systems,and two types of magnitude emergency warning methods,characteristic frequency (τ_p and τ_c,etc.) and characteristic amplitude (P_d and others),have been presented. Based on the existing methods and theories,we applied the machine learning algorithm to 55426 records for 843 earthquakes recorded by the KIK and KNET networks in Japan from 2015 to 2017. By using these records as a full data set,a set of machine learning magnitude prediction models have been designed and trained for common magnitude ranges. Compared with the estimated results of the existing methods,the machine learning method may reduce not only the estimated overall error and variance,but also the cross-sectional variance of multiple joint seismic events. The results of this study show that machine learning algorithm has a broad application prospect in earthquake magnitude emergency estimation,and provides a practical basis and research possibilities for end-to-end model of more complex deep learning algorithm framework as well.
Key words:Earthquake early warning/
Magnitude estimation/
Machine learning/
Deep learning



PDF全文下载地址:

http://www.geophy.cn/data/article/export-pdf?id=dqwlxb_15512