Rare Earth Elements – Multiphysics AI-aided Autonomous Prospecting (REE -MAP)

The growing demand for Rare Earth Elements and Critical Minerals (REE-CM) has heightened interest in extracting these elements from secondary resources, such as coal waste. Similar to traditional mining, resource mapping and prospecting to identify high concentration “hot zones” is key to prioritizing extraction efforts. Mapping REE-CM in unconventional sources is challenging due to low and variable concentrations and complex material characteristics. This study introduces an AI-aided, drone based multi-physics approach to rapidly characterize REE-CM hot zones in coal mine tailings. Our methodology integrates geophysical, radiological, hyperspectral and other technologies deployed on drones, complemented by other ground and laboratory analytical techniques. AI algorithms are key for integrating and interpreting complex multi-physics datasets to identify REE hot zones and optimize sensor selection and deployment. Field demonstrations at coal refuse and ash sites in Pennsylvania were carried out to validate the practical feasibility of this approach. The results revealed promising links between drone-acquired multi-physical signals and REE concentrations, and REE predictions with AI were validated with ground truth. Our study validated the feasibility of using drone-based multi-physics surveys to map REE concentrations in coal wastes to enhance their economic viability for recovery and guide extraction prioritization.