3D mineral prospectivity modeling using deep adaptation network transfer learning: A case study of the Xiadian gold deposit, Eastern China

Abstract

Mineralization distribution is commonly heterogeneous in space due to the various geology and structure conditions or fluid flow and evoluation, causing the possible difference in mineralization distribution regularity between shallow and deep at a deposit. This presents important challenges for three-dimensional mineral prospectivity modeling (3D MPM) for deep zones with the scarcity data conditions. Transfer learning has shown promising generalization performance in tasks involving shifts in data distribution, reducing reliance on labeled samples and enhancing learning capability with limited data. In this study, we propose an approach of 3D MPM, namely DAN-CBAM, based on the deep adaptation network (DAN) augmented with the convolutional block attention module (CBAM). It theoretically can harmonize the distribution of ore-controlling features between shallow and deep zones of deposits while effectively extract critical high-dimensional features and spatial patterns. The Xiadian orogenic gold deposit was selected as a case study to validate the approach. The DAN integrates multi-layer, multi-kernel adaptation at the top layer of the CNN, resulting in improved alignment of marginal distributions across domains. Metrics for distribution similarity such as Wasserstein distance, were reduced by 0.250 and KL divergence decreased by 0.032. Additionally, the inclusion of the CBAM module led to a lower MK-MMD loss and a faster convergence rate. And the DAN-CBAM model achieves superior prediction accuracy (0.85) compared to traditional deep neural network (DNN) models (0.81). These highlight CBAM's effectiveness in enhancing the model's ability to capture spatial similarities in mineralization. Furthermore, the area under the curve (AUC) value of the DAN-CBAM model (0.869) significantly outperforms traditional machine learning methods, including CNN (0.786) and Random Forest (0.703) models, underscoring its superior predictive efficiency in 3D MPM for deep mineralization. Therefore, the proposed DAN-CBAM model is promising to be applied in the 3D MPM, in particular for the deposits with different mineralization distribution regularities in space.