Real-time warning method and application of microseismic multi-feature parameters rockburst based on PSO-GRNN model

Real-time warning method and application of microseismic multi-feature parameters rockburst based on PSO-GRNN model | 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 Real-time warning method and application of microseismic multi-feature parameters rockburst based on PSO-GRNN model wenxuan dong, Qinghe Zhang, xiaorui wang, hepeng dong, chuanbing wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6774317/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Oct, 2025 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract Rockburst disasters in the construction of deep-buried tunnels seriously threaten the safety of underground projects. Traditional monitoring methods have limitations in the analysis of nonlinear and small-sample microseismic data. To effectively reduce the risk of rockburst disasters, a microseismic multi-parameter monitoring method based on the PSO-GRNN model is proposed. Meanwhile, a field sound and light alarm system is independently developed to provide real-time feedback on the prediction results. This method collects the characteristic parameters of microseismic signals in real time and combines the dynamic comprehensive hazard index W Z ( t ) of the grey correlation degree method to construct a multi-parameter early warning criterion standard, effectively solving the scenarios of nonlinearity and small samples of microseismic data in deep-buried tunnels. The developed method and systems are applied on-site in the DJ Tunnel in western China, with good results. The early warning accuracy rate is 92.8%. A complete closed loop of data collection-intelligent analysis-multi-level early warning- emergency response is constructed, providing valuable references for on-site rockburst early warning. Microseismic PSO-GRNN model rockburst grey correlation degree analysis Full Text Cite Share Download PDF Status: Published Journal Publication published 23 Oct, 2025 Read the published version in Natural Hazards → Version 1 posted Reviewers agreed at journal 20 Jun, 2025 Reviewers invited by journal 20 Jun, 2025 Editor invited by journal 19 Jun, 2025 Editor assigned by journal 30 May, 2025 First submitted to journal 29 May, 2025 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. 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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-6774317","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":473938289,"identity":"b8acf7f4-8f47-4129-9c27-87fe3dcc7d9b","order_by":0,"name":"wenxuan dong","email":"","orcid":"","institution":"Anhui University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"wenxuan","middleName":"","lastName":"dong","suffix":""},{"id":473938290,"identity":"ffaf9e4a-0282-4ff1-8057-d3cb7606e0dc","order_by":1,"name":"Qinghe 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Traditional monitoring methods have limitations in the analysis of nonlinear and small-sample microseismic data. To effectively reduce the risk of rockburst disasters, a microseismic multi-parameter monitoring method based on the PSO-GRNN model is proposed. Meanwhile, a field sound and light alarm system is independently developed to provide real-time feedback on the prediction results. This method collects the characteristic parameters of microseismic signals in real time and combines the dynamic comprehensive hazard index \u003cem\u003eW\u003c/em\u003e\u003csub\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/sub\u003e(\u003cem\u003et\u003c/em\u003e) of the grey correlation degree method to construct a multi-parameter early warning criterion standard, effectively solving the scenarios of nonlinearity and small samples of microseismic data in deep-buried tunnels. The developed method and systems are applied on-site in the DJ Tunnel in western China, with good results. 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