Intelligent Identification of Overburden Fractures in Physical Simulation Experiments Based on Improved U-Net Deep Learning and Multimodal Data Fusion

Intelligent Identification of Overburden Fractures in Physical Simulation Experiments Based on Improved U-Net Deep Learning and Multimodal Data Fusion | 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 Article Intelligent Identification of Overburden Fractures in Physical Simulation Experiments Based on Improved U-Net Deep Learning and Multimodal Data Fusion Pei Zhang, Yibo Wei, Liang Li, Zhoucheng Lu, Zhuo Li, Liqiang Dong, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7281191/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract To enhance the intelligence level of overburden fracture recognition in physical similarity simulation experiments, this study proposes an automatic fracture identification method based on an improved U-Net deep learning model integrated with multimodal data fusion. A comprehensive intelligent recognition system was developed, incorporating real-time inference, visual interaction, and 3D reconstruction, enabling dynamic tracking and accurate extraction of fracture features from high-resolution images. The method integrates RGB imagery, FMI resistivity data, and multispectral images, and employs physically driven normalization strategies such as Z-score standardization, linear scaling, and band-wise normalization. To further improve recognition accuracy under complex backgrounds, a sliding-window strategy and attention gating mechanism are introduced. Structurally, the network employs ResNet-34 as the encoder backbone, coupled with a progressive decoder and dual attention modules (CBAM), effectively enhancing multi-scale semantic information fusion. Experimental results demonstrate that the system achieves a real-time processing speed of 200 frames per second and a Dice coefficient of 0.91, significantly outperforming traditional manual interpretation and static segmentation methods. In addition, 3D point cloud reconstruction and geometric quantification of fractures expand the model’s applicability in scenarios such as strata pressure analysis, gas drainage, and ground pressure monitoring. This research provides both theoretical foundations and engineering pathways for the digital identification of overburden fractures and the intelligent upgrade of physical simulation platforms, promoting a transition in mining engineering from manual, experience-based decisions to intelligent and quantitative control, with promising prospects for broader adoption. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Crack identification Physical similarity simulation Deep learning Multi-modal data fusion U-Net Mining engineering Overlying strata fractures Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 30 Nov, 2025 Reviews received at journal 25 Nov, 2025 Reviewers agreed at journal 17 Nov, 2025 Reviews received at journal 16 Oct, 2025 Reviewers agreed at journal 09 Oct, 2025 Reviewers invited by journal 17 Sep, 2025 Editor assigned by journal 10 Sep, 2025 Editor invited by journal 02 Sep, 2025 Submission checks completed at journal 13 Aug, 2025 First submitted to journal 13 Aug, 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. 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-7281191","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":519326776,"identity":"265d59cd-fa6a-45c7-a7d9-54ab16c119e0","order_by":0,"name":"Pei Zhang","email":"","orcid":"","institution":"Xi'an University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Pei","middleName":"","lastName":"Zhang","suffix":""},{"id":519326778,"identity":"f9cae2c2-f10c-480e-a6de-155a2fcd2ffc","order_by":1,"name":"Yibo 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Fusion","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Crack identification, Physical similarity simulation, Deep learning, Multi-modal data fusion, U-Net, Mining engineering, Overlying strata fractures","lastPublishedDoi":"10.21203/rs.3.rs-7281191/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7281191/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo enhance the intelligence level of overburden fracture recognition in physical similarity simulation experiments, this study proposes an automatic fracture identification method based on an improved U-Net deep learning model integrated with multimodal data fusion. A comprehensive intelligent recognition system was developed, incorporating real-time inference, visual interaction, and 3D reconstruction, enabling dynamic tracking and accurate extraction of fracture features from high-resolution images. The method integrates RGB imagery, FMI resistivity data, and multispectral images, and employs physically driven normalization strategies such as Z-score standardization, linear scaling, and band-wise normalization. To further improve recognition accuracy under complex backgrounds, a sliding-window strategy and attention gating mechanism are introduced. Structurally, the network employs ResNet-34 as the encoder backbone, coupled with a progressive decoder and dual attention modules (CBAM), effectively enhancing multi-scale semantic information fusion. Experimental results demonstrate that the system achieves a real-time processing speed of 200 frames per second and a Dice coefficient of 0.91, significantly outperforming traditional manual interpretation and static segmentation methods. In addition, 3D point cloud reconstruction and geometric quantification of fractures expand the model\u0026rsquo;s applicability in scenarios such as strata pressure analysis, gas drainage, and ground pressure monitoring. This research provides both theoretical foundations and engineering pathways for the digital identification of overburden fractures and the intelligent upgrade of physical simulation platforms, promoting a transition in mining engineering from manual, experience-based decisions to intelligent and quantitative control, with promising prospects for broader adoption.\u003c/p\u003e","manuscriptTitle":"Intelligent Identification of Overburden Fractures in Physical Simulation Experiments Based on Improved U-Net Deep Learning and Multimodal Data Fusion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-27 17:51:08","doi":"10.21203/rs.3.rs-7281191/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-30T19:17:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-25T11:23:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67042795735096106809906847283223692890","date":"2025-11-17T14:01:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-16T08:43:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"214032012887462687531725719528167106555","date":"2025-10-09T07:50:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-18T00:37:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-11T00:22:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-02T07:07:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-13T10:49:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-13T10:46:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22408f60-fe85-4c0b-97bb-f2bc825ba987","owner":[],"postedDate":"September 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":55185985,"name":"Physical sciences/Energy science and technology"},{"id":55185986,"name":"Physical sciences/Engineering"},{"id":55185987,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-02-03T09:38:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-27 17:51:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7281191","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7281191","identity":"rs-7281191","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}