Semantic Segmentation Framework for Automated Rock Type Identification in Geological Imagery

Semantic Segmentation Framework for Automated Rock Type Identification in Geological Imagery | 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 Semantic Segmentation Framework for Automated Rock Type Identification in Geological Imagery Jian-hua Ma, Yong-zhang Zhou, Lu-hao He, Yu-qing Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6887052/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 Lithological identification plays a critical role in mineral exploration and geological modeling, contributing significantly to ore system interpretation and regional prospectivity analysis. Conventional approaches often depend on expert interpretation and microscopic examination, which limit scalability and automation. This study introduces a semantic segmentation framework for automated recognition of multiple lithology types from geological imagery. A custom image dataset was constructed, comprising nine common lithological classes—including basalt, shale, diatomite, and others—acquired from core samples and outcrop photographs. To enhance model generalization, the dataset was expanded using data augmentation techniques such as rotation, mirroring, and color perturbation. The proposed method achieved high segmentation accuracy, with an [email protected] of 89.6%, a recall of 92.1%, and a processing speed of 45 FPS under GPU conditions. The model demonstrated strong adaptability in handling blurred boundaries and fine-scale textures, which are common in geological images. This approach is well suited for lithological interpretation of drill cores, segmentation of remote sensing images, and automated identification of prospective metallogenic zones, offering a scalable solution for intelligent geological information extraction. Lithological classification Deep learning Mineral exploration Geological automation Semantic segmentation Full Text Additional Declarations No competing interests reported. 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. 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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-6887052","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":495940099,"identity":"d5c92d13-43d9-4a9e-8bc5-b8ed2cccec9a","order_by":0,"name":"Jian-hua Ma","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"prefix":"","firstName":"Jian-hua","middleName":"","lastName":"Ma","suffix":""},{"id":495940100,"identity":"0ee76953-5558-47a1-9466-a5c381b8c7f9","order_by":1,"name":"Yong-zhang Zhou","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"prefix":"","firstName":"Yong-zhang","middleName":"","lastName":"Zhou","suffix":""},{"id":495940101,"identity":"b0a257ff-9cee-41a8-a19a-b5a28bec5a69","order_by":2,"name":"Lu-hao He","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACAwbmNoYPFQwMbOzMBxCC+LUwtjHOOAPUwsyWQLwWZt42IIuZxwAhiA+Ysze2PZw5b5s8HzODAePPtrrEBvbmbRIMNXdwarHsOdhu8HHbbcM2ZoYEoHWHExt4jpVJMBx7htthNxLbJGduuw10HsMBZsa2A4kNEjlmEowNh3Fruf+wTZp3zm37NmbGBojD5N8Q0HKDEail4XZiGzMzAwNvGzPQFh4CWs4AHTbj2O3kNmY2hsM85w4bt/GkFVskHMOj5fjhYxIfam7bzm/v//jwR1mdbD/74Y03PtTg1oICDjCyAeMUxEogTgMI/CFe6SgYBaNgFIwcAABwc1WpXOEK9wAAAABJRU5ErkJggg==","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":true,"prefix":"","firstName":"Lu-hao","middleName":"","lastName":"He","suffix":""},{"id":495940102,"identity":"9d7ee94c-4e01-4f80-af46-be44573d8773","order_by":3,"name":"Yu-qing Zhang","email":"","orcid":"","institution":"Sun Yat-Sen University","correspondingAuthor":false,"prefix":"","firstName":"Yu-qing","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-13 09:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6887052/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6887052/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89958262,"identity":"c92fdb6b-602f-4152-bbc5-99056303bfa1","added_by":"auto","created_at":"2025-08-27 00:01:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1964581,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscriptv1.0.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6887052/v1_covered_2eb02dba-402b-4f39-85b8-b1646f3286cf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Semantic Segmentation Framework for Automated Rock Type Identification in Geological Imagery","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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},"keywords":"Lithological classification, Deep learning, Mineral exploration, Geological automation, Semantic segmentation","lastPublishedDoi":"10.21203/rs.3.rs-6887052/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6887052/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLithological identification plays a critical role in mineral exploration and geological modeling, contributing significantly to ore system interpretation and regional prospectivity analysis. Conventional approaches often depend on expert interpretation and microscopic examination, which limit scalability and automation. This study introduces a semantic segmentation framework for automated recognition of multiple lithology types from geological imagery. A custom image dataset was constructed, comprising nine common lithological classes\u0026mdash;including basalt, shale, diatomite, and others\u0026mdash;acquired from core samples and outcrop photographs. To enhance model generalization, the dataset was expanded using data augmentation techniques such as rotation, mirroring, and color perturbation. The proposed method achieved high segmentation accuracy, with an [email protected] of 89.6%, a recall of 92.1%, and a processing speed of 45 FPS under GPU conditions. The model demonstrated strong adaptability in handling blurred boundaries and fine-scale textures, which are common in geological images. This approach is well suited for lithological interpretation of drill cores, segmentation of remote sensing images, and automated identification of prospective metallogenic zones, offering a scalable solution for intelligent geological information extraction.\u003c/p\u003e","manuscriptTitle":"Semantic Segmentation Framework for Automated Rock Type Identification in Geological Imagery","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 02:55:17","doi":"10.21203/rs.3.rs-6887052/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":"248ab380-2cf4-4bf7-9119-67c632f905cc","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-26T23:53:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-08 02:55:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6887052","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6887052","identity":"rs-6887052","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}