Three-Dimensional Mineral Prospectivity Modeling with the Integration of Ore-Forming Computational Simulation in the Xiadian Gold Deposit, Eastern China

Abstract

Finding new, effective predictive variables for 3D mineral prospectivity modeling is both important and challenging. The 3D ore-forming numerical modeling quantitively characterizes the complex coupling-mineralization process of the structure, fluid, heat, and wall rock, which may be potential indicators for mineral exploration. We here conducted 3D mineral prospectivity modeling with the integration of ore-forming computational simulation information in the Xiadian orogenic gold deposit, China, to examine whether the simulation data input can improve the reliability of prospectivity modeling. First, we constructed the 3D models of the orebody and fault to extract the fault geometric features using spatial analysis, as they are always considered to be the crucial controls of gold distribution. Second, we performed 3D numerical modeling of the deformation–fluid–heat-coupling process of the structurally controlled hydrothermal Au system using the FLAC3D platform. Finally, the fault-geometry features (buffer, dip, dip variation, and undulation) and the ore-formation-simulation indices (volume strain, shear strain, temperature variation, and fluid flux) were integrated using Bayesian decomposition modeling, which has a promising nonlinear model ability and a flexible variable-integration ability. The prospectivity modeling results demonstrated that the model generated by combining geometry and simulation variables achieved significantly higher AUC, precision, accuracy, Kappa, and F1 scores compared to other models using a single-predictor-variable dataset. This suggests that the joint use of geometry and simulation variables construct a comprehensive association between gold and its ore-controlling factors, thereby resulting in a highly reliable prospectivity model. Thus, the approach of 3D mineral prospectivity modeling aided by ore-forming numerical simulation proves to be more useful in guiding mineral exploration, especially in the condition of fewer variables. Based on the prospectivity modeling outcomes, we identified four gold targets at depth in the Xiadian district that warrant focused exploration efforts.

Deep gold prospectivity modeling in the Jiaojia gold belt, Jiaodong Peninsula, eastern China using machine learning of geometric and geodynamic variables

“…However, the information implied in the simulation geodynamic data may be potential variables for mineral exploration, like the geometric features of 3D models extracted by spatial analysis. As far as we learned, there are only few attempts of prospectivity modeling with concerns of ore-forming simulation (Wang et al, 2020;Liu et al, 2023;Xiao et al, 2024). How to reliably dig the ore-forming information in simulation geodynamic data and integrate it with spatial features to conduct 3D prospectivity modeling is still challenging.…”