Development of a Machine‑Learning Predictive Model for Radiogenic Heat Production in Nigerian Basin Rocks

Development of a Machine‑Learning Predictive Model for Radiogenic Heat Production in Nigerian Basin Rocks | 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 Development of a Machine‑Learning Predictive Model for Radiogenic Heat Production in Nigerian Basin Rocks Samuel O. Sedara, Olusegun O. Alabi, Ayobami Daniel Daramola, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7203749/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 Radiogenic heat production (RHP) is a key control on the lithospheric thermal structure, yet direct measurements are sparse and unevenly distributed. We assemble 1,232 published radiometric measurements from six Nigerian geological provinces—encompassing sedimentary, metamorphic, and igneous terrains—and use potassium, uranium, and thorium concentrations as predictors in a suite of machine learning algorithms, including generalized linear models (GLM), support vector regression (SVR), decision tree regression (DTR), gradient-boosted regression trees (GBRT), random forests (RFR), extreme gradient boosting (XGBR), and a voting-based ensemble model (EM). Models were trained on 60% of the data and tested on the remaining 40%. Performance was assessed via RMSE, MAE, and R². The EM achieved the best balance of accuracy and generalization (R² = 0.98, MAE = 0.03 µW m⁻³, RMSE = 0.22 µW m⁻³), outperforming classical RHP formulae and single-algorithm predictors. We derive a new empirical RHP equation as a function of rock dry density and radionuclide concentrations and generate a high-resolution RHP map for the Nigerian Basin. This dataset reveals coherent geothermal anomaly patterns with greater spatial completeness than previous models. Our ML framework offers a robust approach for RHP estimation in data-poor regions. Radiogenic Heat Production Machine Learning Ensemble Model Geothermal Energy Nigeria Basins Full Text Additional Declarations The authors declare no competing interests. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7203749","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":490212983,"identity":"b7ddc1aa-4028-422e-bf7b-991edf98b201","order_by":0,"name":"Samuel O. Sedara","email":"","orcid":"","institution":"Physics and Electronics Department, Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"O.","lastName":"Sedara","suffix":""},{"id":490213284,"identity":"b26dfb0f-c638-4734-910e-69f33a335698","order_by":1,"name":"Olusegun O. 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We assemble 1,232 published radiometric measurements from six Nigerian geological provinces\u0026mdash;encompassing sedimentary, metamorphic, and igneous terrains\u0026mdash;and use potassium, uranium, and thorium concentrations as predictors in a suite of machine learning algorithms, including generalized linear models (GLM), support vector regression (SVR), decision tree regression (DTR), gradient-boosted regression trees (GBRT), random forests (RFR), extreme gradient boosting (XGBR), and a voting-based ensemble model (EM). Models were trained on 60% of the data and tested on the remaining 40%. Performance was assessed via RMSE, MAE, and R\u0026sup2;. The EM achieved the best balance of accuracy and generalization (R\u0026sup2; = 0.98, MAE\u0026thinsp;=\u0026thinsp;0.03 \u0026micro;W m⁻\u0026sup3;, RMSE\u0026thinsp;=\u0026thinsp;0.22 \u0026micro;W m⁻\u0026sup3;), outperforming classical RHP formulae and single-algorithm predictors. We derive a new empirical RHP equation as a function of rock dry density and radionuclide concentrations and generate a high-resolution RHP map for the Nigerian Basin. This dataset reveals coherent geothermal anomaly patterns with greater spatial completeness than previous models. Our ML framework offers a robust approach for RHP estimation in data-poor regions.\u003c/p\u003e","manuscriptTitle":"Development of a Machine‑Learning Predictive Model for Radiogenic Heat Production in Nigerian Basin Rocks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 04:34:11","doi":"10.21203/rs.3.rs-7203749/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09e16dc4-3ae8-41ec-a1f5-adeb98a30bd8","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-25T04:34:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 04:34:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7203749","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7203749","identity":"rs-7203749","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}