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 FLAC^3D 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.

The Dayingezhuang orogenic gold deposit, located in the northwestern Jiaodong Peninsula, is hosted by the Zhaoping detachment fault, but the paleostress regime during the mineralization period remains poorly understood. In this study, a series of numerical modeling experiments with variable stress conditions were carried out using FLAC3D software to determine the orientation of paleostress and the fluid migration processes during the ore-forming period. The results show that the simple compression or tension stress model led to fluid downward or upward flow along the fault, respectively, accompanying the expansion deformation near the hanging wall or footwall of the Zhaoping fault, which is inconsistent with the known NE oblique mineralization distribution at Dayingezhuang. The reverse and strike-slip model shows that the shear stress was distributed in the gentle dip sites of the fault, and the expansion space occurred in the geometric depression sites of the fault, which is also inconsistent with the known mineralization distribution. The normal and strike-slip model shows that shear stress was distributed in the sites where the fault geometry transforms from steep to gentle. In addition, the expansion deformation zones appeared at sites with dip angles of 35~60° in the footwall and extended along with the NE-trending distribution from shallow to deep levels. The numerical results are quite consistent with the known mineralization, suggesting that the fault movement during the mineralization stage is a combination of the local strike-slip and the NW–SE extension in the Dayingezhuang deposit. Under this stress regime (σ₁ NE–SW, σ₂ vertical, σ₃ NW–SE), the NE dilation zones associated with fault deformation served as channels for the ore-forming fluid migration. Based on the numerical modeling results, the deeper NE levels of the No. 2 orebody in the Dayingezhuang deposit have good prospecting potential. Thus, our study not only highlights that gold mineralization at Dayingezhuang is essentially controlled by the detachment fault geometry associated with certain stress directions but also demonstrates that numerical modeling is a robust tool for identifying potential mineralization.

The three-dimensional (3D) geological models are the typical and key data source in the 3D mineral prospecitivity modeling. Identifying prospectivity-informative predictor variables from the 3D geological models is a challenging and tedious task. Motivated by the ability of convolutional neural networks (CNNs) to learn the intrinsic features, in this paper, we present a novel method that leverages CNNs to learn 3D mineral prospectivity from the 3D geological models. By exploiting the learning ability of CNNs, the presented method allows for disentangling complex correlation to the mineralization and thus opens a door to circumvent the tedious work for designing the predictor variables. Specifically, to explore the unstructured 3D geological models with the CNNs whose input should be structured, we develop a 2D CNN framework in which the geometry of geological boundary is compiled and reorganized into multi-channel images and fed into the CNN. This ensures an effective and efficient training of CNNs while allowing the prospective model to approximate the ore-forming process. The presented method is applied to a typical structure-controlled hydrothermal deposit, the Dayingezhuang gold deposit, eastern China, in which the presented method was compared with the prospectivity modeling methods using hand-designed predictor variables. The results demonstrate the presented method capacitates a performance boost of the 3D prospectivity modeling and empowers us to decrease work-load and prospecting risk in prediction of deep-seated orebodies.