Machine learning in subsurface physical properties and lithofacies prediction in a mining context.

ABSTRACT: To address energy challenges linked to decarbonization, geosciences are focusing more on advancing mineral resource exploration and exploitation. This study uses the Iberian Pyrite Belt, one of the world's largest metallogenic provinces, as a test site to: (a) develop predictive models of physical properties of rock (PPR) and (b) classify lithological units based on these PPR. Over 1,000 surface rock samples and six boreholes from the Riotinto mine were analyzed, providing a comprehensive dataset. A robust quality control process, aided by Machine Learning models (ML), refined the PPR data, ensuring accuracy and reliability. Both traditional statistical models and cuttingedge ML models were used for PPR prediction and lithofacies classification. Our study revealed that geological evolution

SUBMITTER: Balaguera A 

PROVIDER: S-EPMC12279942 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature