2025/07/21 by Albert Balaguer, Montserrat Torné, R. Carbonell +7 · 1 voice
Computer Science · Earth and Planetary Sciences · Engineering · #Geochemistry and Geologic Mapping #Geophysical and Geoelectrical Methods #Hydrocarbon exploration and reservoir analysis
paper · pdf · doi:10.1038/s41598-025-11953-4
openalex publication_date 2025/07/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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 can lead to significant overlaps in PPR across different lithologies, making traditional models insufficient for accurate predictions. However, ML models, such as Random Forest, XGBoost, k-Nearest Neighbors, and Support Vector Regression, demonstrated over 80% accuracy in predicting PPR and classifying lithofacies. This approach redefines how lithofacies are identified and establishes an innovative methodology for subsurface lithological characterization. Results highlight the potential of ML models in mining and geology, paving the way for more accurate 3D characterization of lithological units through integrating geophysical data and direct measurements.