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.

翻译：本研究提出了一种新型的多模态融合模型，用于三维矿产潜力制图（3D MPM），通过深度网络架构有效整合构造与流体信息。该模型利用卷积神经网络（CNN）和多层感知机（MLP），并采用典型相关分析（CCA）对齐与融合多模态特征。在焦家金矿数据集上的严格评估表明，该模型在区分含矿实例和预测矿产潜力方面性能优越，分析结果优于其他模型。消融实验进一步揭示了联合特征利用及引入CCA的益处。本研究不仅推动了矿产潜力建模的发展，也凸显了数据集成与特征对齐在增强勘探决策中的关键作用。