基于两步卷积神经网络的地震事件分类

IF 1.6 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Long Yue, Junhao Qu, Shaohui Zhou, Bao’an Qu, Yanwei Zhang, Qingfeng Xu
{"title":"基于两步卷积神经网络的地震事件分类","authors":"Long Yue,&nbsp;Junhao Qu,&nbsp;Shaohui Zhou,&nbsp;Bao’an Qu,&nbsp;Yanwei Zhang,&nbsp;Qingfeng Xu","doi":"10.1007/s10950-023-10153-9","DOIUrl":null,"url":null,"abstract":"<div><p>The identification of unnatural earthquake events is one of the tasks of earthquake rapid report. The identification accuracy is of great significance for improving the quality of earthquake catalog and seismological research. In this study, a 7-layer convolutional neural network model was constructed to identify unnatural earthquakes. First, the three-component seismic waveform was input to obtain the waveform image classifier, and then, the time–frequency spectrum of blasting and collapse was input to obtain the time–frequency spectrum classifier. The two classifiers were used to identify natural earthquake, blasting, and collapse. The model was trained and tested using 3386 seismic events of Shandong seismic network from 2017 to 2022. The events identified as blasting by the waveform image classifier were reidentified by the time–frequency spectrum classifier. Finally, the identification accuracy of natural earthquake, blasting, and collapse is 97.50%, 95.87%, and 86.84%, respectively, with an average accuracy rate of 96.13%. The experimental results show that the two-step convolutional neural network can extract the characteristics of seismic signals from multiple angles, which get a good result in seismic event classification.</p></div>","PeriodicalId":16994,"journal":{"name":"Journal of Seismology","volume":"27 3","pages":"527 - 535"},"PeriodicalIF":1.6000,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10950-023-10153-9.pdf","citationCount":"0","resultStr":"{\"title\":\"Seismic event classification based on a two-step convolutional neural network\",\"authors\":\"Long Yue,&nbsp;Junhao Qu,&nbsp;Shaohui Zhou,&nbsp;Bao’an Qu,&nbsp;Yanwei Zhang,&nbsp;Qingfeng Xu\",\"doi\":\"10.1007/s10950-023-10153-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The identification of unnatural earthquake events is one of the tasks of earthquake rapid report. The identification accuracy is of great significance for improving the quality of earthquake catalog and seismological research. In this study, a 7-layer convolutional neural network model was constructed to identify unnatural earthquakes. First, the three-component seismic waveform was input to obtain the waveform image classifier, and then, the time–frequency spectrum of blasting and collapse was input to obtain the time–frequency spectrum classifier. The two classifiers were used to identify natural earthquake, blasting, and collapse. The model was trained and tested using 3386 seismic events of Shandong seismic network from 2017 to 2022. The events identified as blasting by the waveform image classifier were reidentified by the time–frequency spectrum classifier. Finally, the identification accuracy of natural earthquake, blasting, and collapse is 97.50%, 95.87%, and 86.84%, respectively, with an average accuracy rate of 96.13%. The experimental results show that the two-step convolutional neural network can extract the characteristics of seismic signals from multiple angles, which get a good result in seismic event classification.</p></div>\",\"PeriodicalId\":16994,\"journal\":{\"name\":\"Journal of Seismology\",\"volume\":\"27 3\",\"pages\":\"527 - 535\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2023-06-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://link.springer.com/content/pdf/10.1007/s10950-023-10153-9.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Seismology\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10950-023-10153-9\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"GEOCHEMISTRY & GEOPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Seismology","FirstCategoryId":"89","ListUrlMain":"https://link.springer.com/article/10.1007/s10950-023-10153-9","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOCHEMISTRY & GEOPHYSICS","Score":null,"Total":0}
引用次数: 0

摘要

非自然地震事件的识别是地震快速报告的任务之一。识别精度的提高对提高地震目录质量和地震学研究具有重要意义。在本研究中,构建了一个7层卷积神经网络模型来识别非自然地震。首先输入三分量地震波形,得到波形图像分类器,然后输入爆破和塌陷的时频谱,得到时频谱分类器。这两种分类器分别用于识别自然地震、爆破和坍塌。利用2017 - 2022年山东地震台网3386个地震事件对模型进行了训练和检验。将波形图像分类器识别出的爆破事件用时频谱分类器重新识别。最后,对自然地震、爆破和塌方的识别准确率分别为97.50%、95.87%和86.84%,平均准确率为96.13%。实验结果表明,两步卷积神经网络可以从多个角度提取地震信号的特征,在地震事件分类中取得了较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Seismic event classification based on a two-step convolutional neural network

Seismic event classification based on a two-step convolutional neural network

The identification of unnatural earthquake events is one of the tasks of earthquake rapid report. The identification accuracy is of great significance for improving the quality of earthquake catalog and seismological research. In this study, a 7-layer convolutional neural network model was constructed to identify unnatural earthquakes. First, the three-component seismic waveform was input to obtain the waveform image classifier, and then, the time–frequency spectrum of blasting and collapse was input to obtain the time–frequency spectrum classifier. The two classifiers were used to identify natural earthquake, blasting, and collapse. The model was trained and tested using 3386 seismic events of Shandong seismic network from 2017 to 2022. The events identified as blasting by the waveform image classifier were reidentified by the time–frequency spectrum classifier. Finally, the identification accuracy of natural earthquake, blasting, and collapse is 97.50%, 95.87%, and 86.84%, respectively, with an average accuracy rate of 96.13%. The experimental results show that the two-step convolutional neural network can extract the characteristics of seismic signals from multiple angles, which get a good result in seismic event classification.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Seismology
Journal of Seismology 地学-地球化学与地球物理
CiteScore
3.30
自引率
6.20%
发文量
67
审稿时长
3 months
期刊介绍: Journal of Seismology is an international journal specialising in all observational and theoretical aspects related to earthquake occurrence. Research topics may cover: seismotectonics, seismicity, historical seismicity, seismic source physics, strong ground motion studies, seismic hazard or risk, engineering seismology, physics of fault systems, triggered and induced seismicity, mining seismology, volcano seismology, earthquake prediction, structural investigations ranging from local to regional and global studies with a particular focus on passive experiments.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信