Interpretable Machine Learning Framework for Geochemical Classification: Advancing Mineral and Geothermal Resource Assessment

Interpretable Machine Learning Framework for Geochemical Classification: Advancing Mineral and Geothermal Resource Assessment | 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 Interpretable Machine Learning Framework for Geochemical Classification: Advancing Mineral and Geothermal Resource Assessment Thien Thuan Huynh, Quoc Lap Nguyen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8062402/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 Accurate lithological classification from geochemical data is fundamental to quantitative resource exploration, evaluation, and risk reduction. This study develops an explainable ensemble learning framework that integrates Random Forest, XGBoost, CatBoost, and Multi-Layer Perceptron models to classify 3,868 igneous rock samples using major oxide compositions. The CatBoost model achieved the highest performance with 89.9% accuracy and 85.7% F1-macro score, outperforming other optimized models. Explainability analysis using SHAP (SHapley Additive exPlanations) quantitatively validated model outputs against petrological theory: SiO2 emerged as the dominant discriminator (importance: 1.026), followed by CaO and MgO, accurately reflecting magmatic differentiation processes. The framework integrates prediction confidence to quantify geological uncertainty in resource assessment contexts. This approach enhances efficiency in mineral and geothermal resource evaluation by enabling rapid, interpretable geochemical classification that supports subsurface mapping and reduces exploration uncertainty. With sub-second inference times, the framework provides operational feasibility for field deployment in exploration programs. By bridging machine learning outputs with geological understanding, this work advances quantitative resource geoscience through transparent, high-accuracy classification suitable for mineral prospectivity mapping, geothermal reservoir characterization, and exploration risk assessment. Geochemistry Artificial Intelligence and Machine Learning Rock classification Geochemistry Machine learning Explainable AI Quantitative geoscience Mineral prospectivity 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. 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