Integrate physics-driven dynamics simulation with data-driven machine learning to predict potential targets in maturely explored orefields: A case study in Tongguangshan orefield, Tongling, China

Abstract

The physics-driven dynamics simulation (DS) and data-driven machine learning (ML) are two general approaches to predict complex systems whose complexity is a hardship impediment to prediction. Based on the 3D geological modeling (GD), we embedded the DS into ML to predict high potential targets and to evaluate ore-controlling and ore-indicating factors in the Tongguangshan (TGS) skarn orefield that has undergone intensive exploration and 4 Cu and Au deposits discovered. The 3D geological models show that the heterogeneous distribution of orebodies around intrusions is associated with the wall rock lithology and contact zone (CZ) characteristics of intrusions, and the resistivity can only provide some ambiguous clues for interpretation of underground geological architectures rather than a direct ore-indicator. The DS results show heterogeneous distribution of temperature, pore pressure, differential stress, volume strain and shear strain, among which the volume strain is closest associated with ore formation. Based on the prediction of Random Forest (FR) model of which the feature variables are combination of DS and 3D modeling results, the SHAP valuing results show a descending importance rank of ore-controlling factors and ore-indicators as lithology, volume strain, distance to CZ, distance to Devonian-Carboniferous interface, curvature of CZ, pressure, temperature, CZ azimuth, resistivity, differential stress, shear strain and CZ dip. The DS results are more important than the resistivity. We have run 6 RF models, consisting of different feature variables which were assigned by DS and 3D modeling, to predict ore-formation favor spaces. The prediction performances on test data sets suggest that, integrating of geological features with dynamics features can enhance performance of RF prediction, the RF model consisting of pure dynamics features can predict mineralization different from the training samples. All RF models' predictions support that there are no significant high potentials at the depth of the orefield, except one small target at its eastern south corner.