Machine Learning Estimates of Geothermal and Critical Mineral Prospectivity of the Great Basin
V.V. Vesselinov, T. Kliphuis
Stanford Geothermal Workshop2026DOI 10.13140/RG.2.2.11951.19360
Summary
We discusses the use of machine learning to estimate geothermal and critical mineral prospectivity in the Great Basin, integrating geological, geochemical, and geophysical data to support resource assessment. The presentation highlights the application of supervised and unsupervised ML methods to identify prospective locations, extract key features, and inform exploration strategies for geothermal energy and critical minerals in this geologically complex region.