3D Point Cloud Lithology Identification Based on Stratigraphically Constrained Continuous Clustering

3D Point Cloud Lithology Identification Based on Stratigraphically Constrained Continuous Clustering | 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 3D Point Cloud Lithology Identification Based on Stratigraphically Constrained Continuous Clustering Binqing Gan, Ran Jing, Yanlin Shao, Yuangang Liu, Xiaolei Duan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7045774/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract To address the challenges of boundary ambiguity and classification accuracy degradation in lithology identification of outcrop point clouds within complex geological settings, this study proposes a Stratigraphy-Guided Continuous Clustering (SGCC) method. Leveraging sedimentological principles of lateral continuity, a dynamic density-threshold hierarchical clustering algorithm is designed to optimize lithological unit boundaries through adjacency-based cluster merging criteria. A Patch-level Feature Aggregation Operator (PFAO) is introduced to construct a multimodal feature space by integrating geometric covariance matrices and spectral distribution entropy. A random forest classifier is then employed for lithology discrimination. Experimental validation on the Qingshuihe Formation outcrop dataset from the Junggar Basin, Xinjiang, demonstrates that the SGCC method achieves an overall accuracy (OA) of 94.64% and a mean intersection over union (MIOU) of 90.87%, outperforming traditional machine learning (SVM, XGBoost) and deep learning methods (PointNet) by 26.22%–68.36%. Notably, SGCC significantly enhances boundary recognition in sandstone-mudstone thin interbeds and conglomerate-sandstone transitional zones. Ablation experiments confirm the efficacy of stratigraphic constraints in suppressing noise and improving computational efficiency, reducing training memory by 83.3% and processing time by 85.7%. By deeply integrating geological principles with computational models, this method provides a high-precision and interpretable technical pathway for intelligent geological exploration. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Earth and environmental sciences/Solid earth sciences Geological outcrop Lithology identification Point cloud segmentation Machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Oct, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 Jul, 2025 Reviews received at journal 23 Jul, 2025 Reviews received at journal 20 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers agreed at journal 11 Jul, 2025 Reviewers invited by journal 11 Jul, 2025 Editor assigned by journal 11 Jul, 2025 Editor invited by journal 11 Jul, 2025 Submission checks completed at journal 08 Jul, 2025 First submitted to journal 08 Jul, 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. 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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-7045774","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":484992949,"identity":"115aa880-4583-4022-88a1-37a8702cf777","order_by":0,"name":"Binqing Gan","email":"","orcid":"","institution":"Yangtze University School of Geosciences","correspondingAuthor":false,"prefix":"","firstName":"Binqing","middleName":"","lastName":"Gan","suffix":""},{"id":484992950,"identity":"bf33b484-d0b6-44eb-88c2-9f3396dac4a8","order_by":1,"name":"Ran 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identification, Point cloud segmentation, Machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7045774/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7045774/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo address the challenges of boundary ambiguity and classification accuracy degradation in lithology identification of outcrop point clouds within complex geological settings, this study proposes a Stratigraphy-Guided Continuous Clustering (SGCC) method. Leveraging sedimentological principles of lateral continuity, a dynamic density-threshold hierarchical clustering algorithm is designed to optimize lithological unit boundaries through adjacency-based cluster merging criteria. A Patch-level Feature Aggregation Operator (PFAO) is introduced to construct a multimodal feature space by integrating geometric covariance matrices and spectral distribution entropy. A random forest classifier is then employed for lithology discrimination. Experimental validation on the Qingshuihe Formation outcrop dataset from the Junggar Basin, Xinjiang, demonstrates that the SGCC method achieves an overall accuracy (OA) of 94.64% and a mean intersection over union (MIOU) of 90.87%, outperforming traditional machine learning (SVM, XGBoost) and deep learning methods (PointNet) by 26.22%–68.36%. Notably, SGCC significantly enhances boundary recognition in sandstone-mudstone thin interbeds and conglomerate-sandstone transitional zones. 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