rare earth elements

DEEP-SEAM: an explainable semi-supervised deep learning framework for mineral prospectivity mapping

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Abstract. The global transition to clean energy is sharply increasing demand for rare earth elements (REEs), yet discovery rates are declining, especially in areas concealed by younger cover. Deep learning (DL) offers new opportunities for mineral prospectivity mapping (MPM), but its application is challenged by sparse labelled mineral occurrences, strong class imbalance, and limited model … Read more…

Applied Geochemistry: Multivariate statistical analysis and bespoke deviation network modeling for geochemical anomaly detection of rare earth elements

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Rare earth elements (REEs), a significant subset of critical minerals, play an indispensable role in modern society and are regarded as “industrial vitamins,” making them crucial for global sustainability. Geochemical survey data proves highly effective in delineating metallic mineral prospects. Separating geochemical anomalies associated with specific types of mineralization from the background reflecting geological processes … Read more…