Learning to Reconstruct 3D Porous Structures from 2D Images via Spatiotemporal Gating Networks

Learning to Reconstruct 3D Porous Structures from 2D Images via Spatiotemporal Gating Networks | 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 Learning to Reconstruct 3D Porous Structures from 2D Images via Spatiotemporal Gating Networks Yongpeng Tan, Mingliang Gao, Huosheng Hu, Meng Zhang, Hui Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8816139/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 T he three-dimensional (3D) micro structure of porous media serves as a vital bridge between microscopic morphology and macroscopic physical properties, with significant implications for fields such as energy exploration and biomedicine. Due to the inability of existing neural network models to effectively capture inter layer patterns and long-range temporal dependencies, reconstructing 3D structures from limited two-dimensional (2D) images using computational modeling remains a key challenge. To address this, we propose a spatiotemporal convolutional gate ,which integrates an enhanced dynamic memory bank to improve long-range feature extraction, an adaptive forget gate to balance temporal continuity and morphological diversity.Meanwhile,to enhance local spatial feature modeling around pore throats,it combines attention and residual networks. The model was validated using Bernheimer sandstone samples through morphological similarity metrics, multiscale statistical analyses, and physical property simulations, with four model variants confirming its robustness and structural diversity. These results demonstrate the effectiveness of the proposed approach and establish a new 3D porous structure reconstruction method for digital rock modeling in petroleum engineering. 3D micro structure reconstruction reservoir characterization porous media modeling spatiotemporal convolutional gate adaptive forget gate dynamic memory bank Full Text Additional Declarations No competing interests reported. Supplementary Files data.zip 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-8816139","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596410794,"identity":"411b2f26-763b-4305-8d2a-f03828af8db2","order_by":0,"name":"Yongpeng Tan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yongpeng","middleName":"","lastName":"Tan","suffix":""},{"id":596410795,"identity":"7d6fda26-46e5-4e7c-b371-4026b68921d8","order_by":1,"name":"Mingliang 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