Synergizing AI with Geology: Exploring VisionTransformers for Rock Classification

Synergizing AI with Geology: Exploring VisionTransformers for Rock Classification | 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 Synergizing AI with Geology: Exploring VisionTransformers for Rock Classification Anfal Maheen, Vidya Vijayan, Shailesh Sivan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4805227/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 Automated rock identification is a critical domain within geosciences, offering significant advancements over traditional manual methods. This study explores the benefits of automation in rock identification, highlighting improvements in speed, efficiency, consistency, and accuracy. Such advancements facilitate large-scale geological surveys and allow for the seamless integration of various geoscientific data, yielding novel insights into Earth’s geology and resources. The research presents a robust model that achieves an impressive accuracy rate of 88.00%, outperforming conventional Convolutional Neural Networks (CNN) and Transfer Learning approaches. A comprehensive analysis of performance metrics, confusion matrices, and ROC-AUC curves demonstrates the model’s robustness and precision in classifying different rock types. Despite its success, the study acknowledges the challenges in accurately classifying Basalt and suggests potential solutions such as data augmentation and incorporating domain-specific knowledge. The conclusion emphasizes the need for future research to address class imbalances and to adopt more advanced techniques for feature extraction and classification, aiming to further refine the model’s accuracy and applicability in automated rock identification rock classification deep learning vision transformer geoinformatic 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-4805227","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":341877030,"identity":"df5402a6-7902-4797-93f7-b732a90d9f22","order_by":0,"name":"Anfal Maheen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIiWNgGAWjYFACxgcMDxj+1e8/3gDkGFgQo4XZgCGB4QBjw5kDIC0SpGi5kQDiEaGFf3Yz44fEnDvMjDOfX93wo0CCgb+9OwGvFok7h5klErc9Y2OWzim72QN0mMSZsxvwW3Mj/wBQCzMPm3RO2g0eoBYDiVz8WuRvJDP/AGqR4JE8k3bzDzFaDG4kswFtOWwgIcF+7DZRthgCtVgkbktLMODJYbstYyDBQ9AvckCH3fi4zSbBgP34s5tv/tjI8bf3EvA+AvAYgElilYMA+wNSVI+CUTAKRsEIAgDPCEnTfegwQgAAAABJRU5ErkJggg==","orcid":"","institution":"Cambrian College","correspondingAuthor":true,"prefix":"","firstName":"Anfal","middleName":"","lastName":"Maheen","suffix":""},{"id":341877031,"identity":"10c43b85-7712-47b0-8ff5-68e62b12b0d5","order_by":1,"name":"Vidya Vijayan","email":"","orcid":"","institution":"KVM College of Engineering and Information Technology","correspondingAuthor":false,"prefix":"","firstName":"Vidya","middleName":"","lastName":"Vijayan","suffix":""},{"id":341877032,"identity":"ed7ad35e-3a66-448e-8ef0-6bd8d167742b","order_by":2,"name":"Shailesh Sivan","email":"","orcid":"","institution":"Cochin University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shailesh","middleName":"","lastName":"Sivan","suffix":""}],"badges":[],"createdAt":"2024-07-26 04:12:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4805227/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4805227/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":65473622,"identity":"3857d006-383a-48c5-ae5d-3eb65eb30dc5","added_by":"auto","created_at":"2024-09-28 01:53:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":429018,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureLaTeXTemplate2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4805227/v1_covered_e82f365b-1866-41d6-b5dc-3595d3c2116b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Synergizing AI with Geology: Exploring VisionTransformers for Rock Classification","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":"rock classification, deep learning, vision transformer, geoinformatic","lastPublishedDoi":"10.21203/rs.3.rs-4805227/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4805227/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Automated rock identification is a critical domain within geosciences, offering significant advancements over traditional manual methods. 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