  This study presents a novel multimodal fusion model for three-dimensional
mineral prospectivity mapping (3D MPM), effectively integrating structural and
fluid information through a deep network architecture. Leveraging Convolutional
Neural Networks (CNN) and Multilayer Perceptrons (MLP), the model employs
canonical correlation analysis (CCA) to align and fuse multimodal features.
Rigorous evaluation on the Jiaojia gold deposit dataset demonstrates the
model's superior performance in distinguishing ore-bearing instances and
predicting mineral prospectivity, outperforming other models in result
analyses. Ablation studies further reveal the benefits of joint feature
utilization and CCA incorporation. This research not only advances mineral
prospectivity modeling but also highlights the pivotal role of data integration
and feature alignment for enhanced exploration decision-making.
