基于卷积神经网络的地震震级快速估算方法

位栋梁 王延伟 王自法 廖吉安 赵登科

位栋梁,王延伟,王自法,廖吉安,赵登科. 2022. 基于卷积神经网络的地震震级快速估算方法. 地震学报,44(2):316−326 doi: 10.11939/jass.20210198
引用本文: 位栋梁,王延伟,王自法,廖吉安,赵登科. 2022. 基于卷积神经网络的地震震级快速估算方法. 地震学报,44(2):316−326 doi: 10.11939/jass.20210198
Wei D L,Wang Y W,Wang Z F,Liao J A,Zhao D K. 2022. A fast estimation method of earthquake magnitude based on convolutional neural networks. Acta Seismologica Sinica,44(2):316−326 doi: 10.11939/jass.20210198
Citation: Wei D L,Wang Y W,Wang Z F,Liao J A,Zhao D K. 2022. A fast estimation method of earthquake magnitude based on convolutional neural networks. Acta Seismologica Sinica44(2):316−326 doi: 10.11939/jass.20210198

基于卷积神经网络的地震震级快速估算方法

doi: 10.11939/jass.20210198
基金项目: 国家自然科学基金(51978634,51968016)资助
详细信息
    作者简介:

    位栋梁,硕士,主要从事地震监测预警相关研究,e-mail:wei_dl1921@163.com

    通讯作者:

    王自法,博士,研究员,主要从事巨灾风险相关研究,e-mail: zifa@iem.ac.cn

  • 中图分类号: P135.69

A fast estimation method of earthquake magnitude based on convolutional neural networks

  • 摘要: 地震发生后震级的快速准确估算是确保地震预警减灾效果的最重要部分,而基于经验参数的传统方法在准确性和时效性方面各自存在局限性。通过建立多全连接层卷积神经网络模型,选用日本KiK-net和K-NET台网1997年至2019年记录到的3 065次地震的16万4 547条初至波在3—9 s不同时段的频域数据、对应地震事件的震源信息(震中距和震源深度)以及场地信息(vS30)作为全数据集,对提出的模型进行训练并对估算效果予以评估。结果显示:当初至波截取时段为3 s时,模型震级预测的整体准确率为89.92%,并且随着初至波长度的增大,估算震级的准确率持续提高;当截取时段为9 s时,整体准确率达到96.08%。与传统Pd方法的预估结果相比,结果表明:基于本文提出的多全连接层卷积神经网络模型估算的震级精度有所改善,具有绝对误差标准差和均值更小、时效强等特性,实现了基于单台站记录的端到端震级持续快速估算,能更好地增强地震预警的减灾效果。

     

  • 图  1  震级、场地剪切波速及记录数随震源距的变化

    Figure  1.  Distribution of magnitude,site shear wave velocity and event counts with hypocentral distance

    图  2  训练集与测试集分布情况对比图

    Figure  2.  Distribution of training and test datasets

    图  3  CNN模型架构图

    Figure  3.  Architecture of CNN model proposed in this study

    图  4  模型训练五折交叉验证示意图

    Figure  4.  Schematic diagram of five-fold cross-validation in model training

    图  5  3 s时窗下CNN估算震级(a)、Pd估算震级(b)与实际震级的比较分析

    Figure  5.  Comparison of the estimated magnitude by CNN (a) and by Pd (b) with actual magnitude for the time window of 3 s

    图  6  3 s时窗下震级估算的误差分布(a)和误差均值图(b)

    Figure  6.  Error distribution (a) and error mean (b) of CNN and P d magnitude estimation with 3 s time window

    图  7  不同时间窗长t下CNN (上)与Pd (下)估算震级与实际震级的比较分析

    Figure  7.  Comparison of the estimated magnitude by CNN (upper) and Pd (lower) with actual ones for different time windows

    表  1  场地条件分类(引自Zeynalov et al,2013

    Table  1.   Site condition classification (after Zeynalov et al,2013

    场地分类场地分类vS30/(m·s−1
    A硬土 [ 600,+∞)
    B中土 [ 200,600)
    C软土(0,200)
    注:vS30代表地表下30 m土体的平均剪切波速。
    下载: 导出CSV

    表  2  神经网络(CNN)模型和Pd参数方法在不同时窗下的预测结果统计

    Table  2.   Prediction result statistics of neural network model (CNN) and Pd with different time windows

    时窗长度/s误差标准差误差平均值准确率
    CNNPdCNNPdCNNPd
    4 0.234 0 0.329 1 0.224 4 0.419 8 92.46% 66.09%
    5 0.218 0 0.305 9 0.222 8 0.396 7 93.32% 68.83%
    6 0.197 8 0.282 9 0.199 3 0.376 6 94.91% 71.17%
    7 0.185 2 0.267 5 0.194 7 0.361 7 95.41% 73.02%
    8 0.173 9 0.259 1 0.189 7 0.351 5 95.78% 74.43%
    9 0.164 6 0.254 4 0.191 3 0.343 1 96.08% 75.52%
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-11-05
  • 录用日期:  2022-03-09
  • 修回日期:  2022-01-04
  • 网络出版日期:  2022-04-22
  • 刊出日期:  2022-04-24

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