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

Seismic event classification based on a two-step convolutional neural network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Seismic event classification based on a two-step convolutional neural network Long Yue, Junhao Qu, Shaohui Zhou, Bao’an Qu, Yanwei Zhang, Qingfeng Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2433400/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Jun, 2023 Read the published version in Journal of Seismology → Version 1 posted 7 You are reading this latest preprint version Abstract 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 catalogue and seismological research. In this work, a seven-layer convolution neural network model was constructed to identify unnatural earthquakes. First, the three-component seismic waveform was used as input to obtain the waveform image classifier, and then the time-frequency spectrum of explosion and collapse was used as input to obtain the time-frequency spectrum classifier. The two classifiers were used to identify earthquake, explosion and collapse. The model was trained and tested using 3386 seismic events of Shandong seismic network from 2017 to 2022. The events identified as explosion events by the waveform image classifier were reidentified by the time-frequency spectrum classifier. Finally, the identification accuracy of natural earthquake, explosion and collapse is 97.50%, 95.87% and 86.84% respectively, with an average identification accuracy of 96.13%. The experimental results show that the two-step convolution neural network can extract the characteristics of seismic signals from different angles, and get a good result in seismic event classification. natural earthquake·explosion·collapse convolutional neural network time–frequency spectrum Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 Jun, 2023 Read the published version in Journal of Seismology → Version 1 posted Editorial decision: Major revision 14 Feb, 2023 Reviews received at journal 12 Feb, 2023 Reviewers agreed at journal 25 Jan, 2023 Reviewers invited by journal 23 Jan, 2023 Editor assigned by journal 03 Jan, 2023 Submission checks completed at journal 03 Jan, 2023 First submitted to journal 01 Jan, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2433400","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":164635908,"identity":"27ac18d6-e24b-49a3-8a58-5851be11019b","order_by":0,"name":"Long Yue","email":"","orcid":"","institution":"Shandong Earthquake Agency","correspondingAuthor":false,"prefix":"","firstName":"Long","middleName":"","lastName":"Yue","suffix":""},{"id":164635910,"identity":"5d59a743-daca-4c33-95fd-2698fc624b70","order_by":1,"name":"Junhao 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