Few-Shot Intelligent Identification of Rock Thin Sections Based on SAM

Few-Shot Intelligent Identification of Rock Thin Sections Based on SAM | 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 Few-Shot Intelligent Identification of Rock Thin Sections Based on SAM Yuan Zhou, Qing Li, Zhuofeng Zhang, Zhengyu Wei, Qiang Du, Xinlong Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7344612/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 Mar, 2026 Read the published version in Earth Science Informatics → Version 1 posted 14 You are reading this latest preprint version Abstract Thin section rock identification is a complex task, primarily constrained by the extraction of complex minerals and the acquisition of large-scale labeled data. This paper proposes a thin section rock identification method designed for few-shot labeled data, which enables the segmentation and identification of various rock minerals with minimal labeled data. The SAM model is used for mineral particle extraction, combined with the focal loss function, transfer learning, and the integration of multiple classification models to identify thin sections. The prediction process is evaluated at multiple levels. Ultimately, the method achieved the extraction and identification of 11 minerals using only 38 labeled data samples, with an identification accuracy of 91%. This approach significantly reduces the cost of manual labeling, requiring only a small amount of labeled data and minimal training effort to identify specific mineral classes.The source code of the proposed method are available at https://github.com/Xuerenbujianhua/SAMRocks Few-shot Rock thin section Semantic Segmentation Integrated learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 17 Mar, 2026 Read the published version in Earth Science Informatics → Version 1 posted Editorial decision: Revision requested 05 Oct, 2025 Reviews received at journal 04 Oct, 2025 Reviews received at journal 02 Oct, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 13 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers agreed at journal 12 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviews received at journal 10 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 07 Sep, 2025 Editor assigned by journal 07 Sep, 2025 Submission checks completed at journal 19 Aug, 2025 First submitted to journal 11 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. 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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-7344612","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513288701,"identity":"b3650716-cb7d-4c42-8b0d-f4c48631456e","order_by":0,"name":"Yuan Zhou","email":"","orcid":"","institution":"Hainan Institute of China University of Petroleum (Beijing)","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Zhou","suffix":""},{"id":513288702,"identity":"036d2c5b-4d0b-463f-ae85-078698a8a120","order_by":1,"name":"Qing 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This paper proposes a thin section rock identification method designed for few-shot labeled data, which enables the segmentation and identification of various rock minerals with minimal labeled data. The SAM model is used for mineral particle extraction, combined with the focal loss function, transfer learning, and the integration of multiple classification models to identify thin sections. The prediction process is evaluated at multiple levels. Ultimately, the method achieved the extraction and identification of 11 minerals using only 38 labeled data samples, with an identification accuracy of 91%. 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