A novel approach for analysis of outcrop fracture properties from 3D LiDAR point cloud data

A novel approach for analysis of outcrop fracture properties from 3D LiDAR point cloud data | 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 A novel approach for analysis of outcrop fracture properties from 3D LiDAR point cloud data Chunyuan Bai, Yungui Xu, Ronghu Zhang, Guikang Chen, Lei Wu, Xuri Huang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7183223/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract LiDAR(Light Detection and Ranging) technology is a fully automated,high-precision stereo scanning method.As one of many of its application in geoscience,it enables the rapid acquisition of precise 3D models of natural rock outcrops.However,extracting fracture information directly from 3D point cloud data is challenging,primarily due to the complexity introduced by the reflection of fractures on the surface of the rocks.This paper proposes a point cloud processing approach after the acquisition of point cloud data that transforms 3D point cloud data into 2D images using an innovative gridding method while preserving the maximum amount of fracture information.Noise reduction,Tensor Voting,and the rectangle bounding algorithm are seamlessly integrated into the processing workflow to enhance andquantitatively characterize fracture properties.Statistical data on fracture properties are derived,providing crucial parameters necessary for fractured hydrocarbon reservoir modeling.The feasibility of the novel approach is substantiating data from anoutcrop in the Yangxia Depression,validated by field photographs.By finetuning thresholds and processing parameters,this method can be adapted for use with various other outcrop point cloud datasets,making it an applicable tool for diverse applications. Physical sciences/Energy science and technology Physical sciences/Engineering Physical sciences/Mathematics and computing Earth and environmental sciences/Solid earth sciences LiDAR technology 3D point cloud data Natural rock outcrop Tensor Voting Fracture properties Yangxia Depression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 20 Aug, 2025 Reviews received at journal 18 Aug, 2025 Reviews received at journal 14 Aug, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers agreed at journal 08 Aug, 2025 Reviews received at journal 07 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviews received at journal 29 Jul, 2025 Reviewers agreed at journal 24 Jul, 2025 Reviewers invited by journal 24 Jul, 2025 Editor assigned by journal 24 Jul, 2025 Editor invited by journal 24 Jul, 2025 Submission checks completed at journal 23 Jul, 2025 First submitted to journal 23 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-7183223","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":491221905,"identity":"ee9a5946-d5f0-45ed-a884-c668779f76d7","order_by":0,"name":"Chunyuan Bai","email":"","orcid":"","institution":"Southwest Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Chunyuan","middleName":"","lastName":"Bai","suffix":""},{"id":491221906,"identity":"2fc3242c-aeae-4d99-b651-75220e5852cf","order_by":1,"name":"Yungui 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