Physically Interpretable Unsupervised Thermographic Clustering for Structural Alteration Diagnostics in Ancient Jade Artefacts

Physically Interpretable Unsupervised Thermographic Clustering for Structural Alteration Diagnostics in Ancient Jade Artefacts | 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 Physically Interpretable Unsupervised Thermographic Clustering for Structural Alteration Diagnostics in Ancient Jade Artefacts Haifeng Tang, Xue Yang, Jue Lian, Jin Li, Zhenglin Xie, Beichen Chen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8299475/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Mar, 2026 Read the published version in npj Heritage Science → Version 1 posted 9 You are reading this latest preprint version Abstract Ancient jade artefacts often exhibit multilayered weathering, mineralogical heterogeneity and complex subsurface alteration that cannot be fully assessed through visual inspection. This study proposes a physically interpretable unsupervised thermographic framework by integrating pulsed thermography (PT), long-pulsed thermography (LPT) and Self-Organizing Map (SOM) clustering for structural diagnostics in jade. To enhance depth-sensitive feature separation, a differential Thermographic Signal Reconstruction (TSR) encoding strategy was adopted: raw TSR signals from PT preserved emissivity-dependent surface information, whereas first-derivative TSR signals from LPT suppressed surface effects and highlighted deeper thermophysical contrasts. Operating directly on high-dimensional thermal sequences, SOM preserved the topological evolution of thermal responses and enabled clustering of surface mineralisation, layered weathering and internal heterogeneity without labelled data. Validation on a reference sample confirmed the method’s ability to distinguish shallow and deep features. When applied to a Shang-dynasty jade dagger, the framework mapped spatial variations in alteration depth and identified structurally vulnerable regions, and most importantly it revealed previously undocumented subsurface traces consistent with hafting or use-related contact that recover material evidence of ancient handling which cannot be detected through conventional examination. The proposed workflow offers a non-invasive, label-free and physically interpretable diagnostic pathway for mineral-based cultural heritage and provides a transferable approach for analysing semi-transparent or heterogeneously weathered artefacts. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 12 Mar, 2026 Read the published version in npj Heritage Science → Version 1 posted Editorial decision: Revision requested 09 Jan, 2026 Reviews received at journal 09 Jan, 2026 Reviews received at journal 02 Jan, 2026 Reviewers agreed at journal 14 Dec, 2025 Reviewers agreed at journal 12 Dec, 2025 Reviewers invited by journal 09 Dec, 2025 Editor assigned by journal 09 Dec, 2025 Submission checks completed at journal 09 Dec, 2025 First submitted to journal 07 Dec, 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. 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-8299475","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":558517608,"identity":"4003bf0e-c00d-42ba-a31a-fc4e7f647dd5","order_by":0,"name":"Haifeng Tang","email":"","orcid":"","institution":"Capital Normal University","correspondingAuthor":false,"prefix":"","firstName":"Haifeng","middleName":"","lastName":"Tang","suffix":""},{"id":558517609,"identity":"7b2f65b7-bbd8-47ca-88a1-3593f40c1acf","order_by":1,"name":"Xue 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