Vector characteristics of electromagnetic radiation and anomalous source response patterns during coal excavation

Vector characteristics of electromagnetic radiation and anomalous source response patterns during coal excavation | 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 Vector characteristics of electromagnetic radiation and anomalous source response patterns during coal excavation Peiwu Liao, Liming Qiu, Xueqiu He, Dazhao Song, Xinchuan Fan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8465988/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Coal and gas outburst represents one of the most catastrophic dynamic disasters in deep coal mining. Accurate identification of its precursors and precise localization of hazard sources are essential for effective early warning and safe mining operations. In this study, a novel six-dimensional feature fusion framework is proposed, based on a self-developed triaxial electromagnetic radiation (EMR) vector monitoring system. Continuous online EMR monitoring was carried out during excavation at the 2615 working face of Juji Coal Mine. Within successive ten-minute time windows, EMR amplitude peaks were extracted, and the following key parameters were calculated: horizontal deviation, vertical deviation, amplitude deviation, and their corresponding permutation entropies. The resulting feature vectors were classified using the K-nearest neighbor (K-NN) algorithm against six prototypical event vectors to enable rapid identification of outburst-related events. Identified events were categorized as either coal-seam internal fracturing or interface disturbance, and localized via two single-sensor strategies: for internal fracturing, the stress-peak location derived from numerical simulation was used; for interface disturbance, localization was achieved through deviation-vector extrapolation to plane intersection. The average localization errors were approximately 1.34 m (0.073 rad) for internal fracturing and 2.18 m (0.005 rad) for interface disturbance. The proposed "single-sensor + numerical-model" framework is easy to deploy in underground settings and supports rapid spatial localization of high-risk outburst events, offering a practical and efficient technical solution for mine safety monitoring and early warning. Coal mine Electromagnetic vector field Signal processing Feature‑fusion classification Hidden disaster source detection Full Text Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8465988","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576826617,"identity":"ff527961-d1c6-411d-bc0a-0ea1feea1133","order_by":0,"name":"Peiwu 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Accurate identification of its precursors and precise localization of hazard sources are essential for effective early warning and safe mining operations. In this study, a novel six-dimensional feature fusion framework is proposed, based on a self-developed triaxial electromagnetic radiation (EMR) vector monitoring system. Continuous online EMR monitoring was carried out during excavation at the 2615 working face of Juji Coal Mine. Within successive ten-minute time windows, EMR amplitude peaks were extracted, and the following key parameters were calculated: horizontal deviation, vertical deviation, amplitude deviation, and their corresponding permutation entropies. The resulting feature vectors were classified using the K-nearest neighbor (K-NN) algorithm against six prototypical event vectors to enable rapid identification of outburst-related events. Identified events were categorized as either coal-seam internal fracturing or interface disturbance, and localized via two single-sensor strategies: for internal fracturing, the stress-peak location derived from numerical simulation was used; for interface disturbance, localization was achieved through deviation-vector extrapolation to plane intersection. The average localization errors were approximately 1.34 m (0.073 rad) for internal fracturing and 2.18 m (0.005 rad) for interface disturbance. The proposed \"single-sensor\u0026thinsp;+\u0026thinsp;numerical-model\" framework is easy to deploy in underground settings and supports rapid spatial localization of high-risk outburst events, offering a practical and efficient technical solution for mine safety monitoring and early warning.\u003c/p\u003e","manuscriptTitle":"Vector characteristics of electromagnetic radiation and anomalous source response patterns during coal excavation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 06:47:33","doi":"10.21203/rs.3.rs-8465988/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e8c4e033-c225-44d9-a01b-c3bb106a1d8f","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T16:56:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 06:47:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8465988","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8465988","identity":"rs-8465988","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}