Connecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning

Connecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning | 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 Connecting Meteorite Spectra to Lunar Surface Composition Using Hyperspectral Imaging and Machine Learning Fatemeh Fazel Hesar, Mojtaba Raouf, Amirmohammad Chegeni, Peyman Soltani, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7453959/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 We present an innovative, cost-effective framework integrating laboratory Hyperspectral Imaging (HSI) of the BECHAR 010 lunar meteorite with ground-based lunar HSI and supervised Machine Learning (ML) to generate high-fidelity mineralogical maps. A \SI{3}{\milli\metre} thin section of BECHAR 010 was imaged under a microscope with a \SI{30}{\milli\metre} focal length lens at \SI{150}{\milli\metre} working distance, using 6x binning to increase the signal-to-noise ratio, producing a data cube (X $\times$ Y $\times$ $\lambda$ = $791 \times 1024 \times 224$, \SI{0.24}{\milli\metre} $\times$ \SI{0.2}{\milli\metre} resolution) across \SIrange{400}{1000}{\nano\metre} (224 bands, \SI{2.7}{\nano\metre} spectral sampling, \SI{5.5}{\nano\metre} FWHM spectral resolution) using a Specim FX10 camera.Ground-based lunar HSI was captured with a Celestron 8SE telescope (\SI{3}{\kilo\metre}/pixel), yielded a data cube ($371 \times 1024 \times 224$). Solar calibration was performed using a Spectralon reference (\SI{99}{\percent} reflectance \SI{<2}{\percent} error) ensured accurate reflectance spectra. A Support Vector Machine (SVM) with a radial basis function kernel, trained on expert-labeled spectra, achieved \SI{93.7}{\percent} classification accuracy (5-fold cross-validation) for olivine (\SI{92}{\percent} precision, \SI{90}{\percent} recall) and pyroxene (\SI{88}{\percent} precision, \SI{86}{\percent} recall) in BECHAR 010. Local Interpretable Model-agnostic Explanations (LIME) identified key wavelengths (e.g., \SI{485}{\nano\metre}, \SI{22.4}{\percent} for M3; \SI{715}{\nano\metre}, \SI{20.6}{\percent} for M6) across 10 pre selected regions (M1 to M10), indicating olivine-rich (Highland-like) and pyroxene-rich (Mare-like) compositions. Spectral Angle Mapper (SAM) analysis revealed angles from \SI{0.26}{\radian} to \SI{0.66}{\radian}, linking M3 and M9 to Highlands and M6 and M10 to Mares. K-means clustering of lunar data identified 10 mineralogical clusters (\SI{88}{\percent} accuracy), validated against Chandrayaan-1 Moon mineralogy Mapper ($\rm M^3$) data (\SI{140}{\metre}/pixel, \SI{10}{\nano\metre} spectral resolution). A novel push-broom HSI approach with telescope, achieves 0.8 arcsec resolution for lunar spectroscopy, inspiring full-sky multi-object spectral mapping. Physical sciences/Physics Earth and environmental sciences/Solid earth sciences Full Text Additional Declarations No competing interests reported. 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. 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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-7453959","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":529715032,"identity":"8f658ae4-de1b-4b7d-a4fa-4c430d8dd227","order_by":0,"name":"Fatemeh Fazel Hesar","email":"","orcid":"","institution":"Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Fatemeh","middleName":"Fazel","lastName":"Hesar","suffix":""},{"id":529715033,"identity":"f11dd7d6-7d97-4641-b101-ab3248d3e539","order_by":1,"name":"Mojtaba Raouf","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIie3OMWvCQBTA8SdZA65PDPoVngim4pc5EXRJS8cMJc3ULn6AG0o/g5PzPR7oku4ZOrSLk0PcOjj0MFiyXNqxw/3huHfH/eAAfL7/Wppeto4BCi6TsStqJUUBWL9skLCVvD1dCQQ/l04SP8sO+TXLYq3AVPezQYy3LCG8O0lULJfIW8GoVMCaVuOpvlOWHJwEIZn0T1uD2FcgIcl8UybE2s5O0j1OkF+ympxJHn8nmFiSBzUBEkWWmKqVHBY3Zic9PfzMeU2r0aY4WkJtH1twaR6yLobCH1/n2ZD2ybhSqQxcpFEnbxzoD8Dn8/l8zr4BprNY+mybHEoAAAAASUVORK5CYII=","orcid":"","institution":"Leiden University","correspondingAuthor":true,"prefix":"","firstName":"Mojtaba","middleName":"","lastName":"Raouf","suffix":""},{"id":529715034,"identity":"dedfd960-e96f-4239-9f1a-915cbd6128b1","order_by":2,"name":"Amirmohammad Chegeni","email":"","orcid":"","institution":"University of Padua","correspondingAuthor":false,"prefix":"","firstName":"Amirmohammad","middleName":"","lastName":"Chegeni","suffix":""},{"id":529715035,"identity":"fa0d816c-4b8d-443d-88e4-6846eb582710","order_by":3,"name":"Peyman Soltani","email":"","orcid":"","institution":"Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Peyman","middleName":"","lastName":"Soltani","suffix":""},{"id":529715036,"identity":"2364837e-3eb9-4794-b6d4-0e99d3d63311","order_by":4,"name":"Bernard Foing","email":"","orcid":"","institution":"Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Bernard","middleName":"","lastName":"Foing","suffix":""},{"id":529715037,"identity":"3d78605b-a865-4b0f-83e4-e4ea30c9b1eb","order_by":5,"name":"Elias Chatzitheodoridis","email":"","orcid":"","institution":"National Technical University of Athens","correspondingAuthor":false,"prefix":"","firstName":"Elias","middleName":"","lastName":"Chatzitheodoridis","suffix":""},{"id":529715038,"identity":"735c307e-6915-4669-a9f4-11814ebe7239","order_by":6,"name":"Michiel J.A. de Dood","email":"","orcid":"","institution":"Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Michiel","middleName":"J.A.","lastName":"de Dood","suffix":""},{"id":529715039,"identity":"78147569-bb02-405c-bb6a-c760902668bc","order_by":7,"name":"Fons J. 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A \\SI{3}{\\milli\\metre} thin section of BECHAR 010 was imaged under a microscope with a \\SI{30}{\\milli\\metre} focal length lens at \\SI{150}{\\milli\\metre} working distance, using 6x binning to increase the signal-to-noise ratio, producing a data cube (X $\\times$ Y $\\times$ $\\lambda$ = $791 \\times 1024 \\times 224$, \\SI{0.24}{\\milli\\metre} $\\times$ \\SI{0.2}{\\milli\\metre} resolution) across \\SIrange{400}{1000}{\\nano\\metre} (224 bands, \\SI{2.7}{\\nano\\metre} spectral sampling, \\SI{5.5}{\\nano\\metre} FWHM spectral resolution) using a Specim FX10 camera.Ground-based lunar HSI was captured with a Celestron 8SE telescope (\\SI{3}{\\kilo\\metre}/pixel), yielded a data cube ($371 \\times 1024 \\times 224$). Solar calibration was performed using a Spectralon reference (\\SI{99}{\\percent} reflectance \\SI{\u003c2}{\\percent} error) ensured accurate reflectance spectra. A Support Vector Machine (SVM) with a radial basis function kernel, trained on expert-labeled spectra, achieved \\SI{93.7}{\\percent} classification accuracy (5-fold cross-validation) for olivine (\\SI{92}{\\percent} precision, \\SI{90}{\\percent} recall) and pyroxene (\\SI{88}{\\percent} precision, \\SI{86}{\\percent} recall) in BECHAR 010. Local Interpretable Model-agnostic Explanations (LIME) identified key wavelengths (e.g., \\SI{485}{\\nano\\metre}, \\SI{22.4}{\\percent} for M3; \\SI{715}{\\nano\\metre}, \\SI{20.6}{\\percent} for M6) across 10 pre selected regions (M1 to M10), indicating olivine-rich (Highland-like) and pyroxene-rich (Mare-like) compositions. Spectral Angle Mapper (SAM) analysis revealed angles from \\SI{0.26}{\\radian} to \\SI{0.66}{\\radian}, linking M3 and M9 to Highlands and M6 and M10 to Mares. K-means clustering of lunar data identified 10 mineralogical clusters (\\SI{88}{\\percent} accuracy), validated against Chandrayaan-1 Moon mineralogy Mapper ($\\rm M^3$) data (\\SI{140}{\\metre}/pixel, \\SI{10}{\\nano\\metre} spectral resolution). 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