Prospectivity mapping based on deep learning typically requires substantial amounts of geological feature information from known mineral deposits. Due to the limited spatial distribution of ore deposits, the training of predictive models is often hampered by insufficient positive samples. Meanwhile, data-driven mineral prospectivity mapping often overlooks domain knowledge and expert experience, leading to poor interpretability of predictive results. To address this problem, we employed the Gaussian mixture model (GMM) for spatial feature classification to expand the number of positive samples. The approach integrated the embedding of geological map knowledge graphs with geological exploration data to enhance the knowledge constraints of the prospecting model, which enabled the integration of knowledge with data. Considering the complex spatial structure of geological elements, a bi-branch utilizing the 1-dimensional convolutional neural network (CNN1D) and graph convolutional network (GCN) was used to extract geological spatial features for model training and prediction. To validate the effectiveness of the method, a gold mineralization prediction study was conducted in the Wulonggou area (Qinghai province, western China). The results indicate that, when the number of GMM spatial feature classifications was 17, the positive-to-negative sample ratio was optimal, and the embedding of the knowledge graph controlled the prediction area distribution effectively, which demonstrated strong consistency between the prospecting area and the known mineral deposits. Compared with the predictions by CNN1D, the fused prediction model of CNN1D and GCN yielded higher accuracy. Our model identified 11 classes of mineralization potential areas and provides geological interpretations for different prediction categories.

基于深度学习的前景测绘通常需要来自已知矿床的大量地质特征信息。由于矿床空间分布有限，预测模型的训练常常因正样本不足而受到阻碍。与此同时，数据驱动的矿产前景测绘往往忽视领域知识和专家经验，导致预测结果的可解释性较差。为了解决这个问题，我们采用高斯混合模型（GMM）进行空间特征分类，以扩大正样本的数量。该方法将地质图知识图谱与地质勘探数据的嵌入相结合，增强了找矿模型的知识约束，实现了知识与数据的融合。考虑到地质要素复杂的空间结构，利用一维卷积神经网络（CNN1D）和图卷积网络（GCN）的双分支提取地质空间特征进行模型训练和预测。为了验证该方法的有效性，在青海省五龙沟地区进行了金矿化预测研究。结果表明，当GMM空间特征分类数为17时，正负样本比例最优，知识图谱的嵌入有效控制了预测区域分布，表现出探矿区域与预测区域的一致性较强。已知的矿藏。与 CNN1D 的预测相比，CNN1D 和 GCN 的融合预测模型具有更高的准确率。 我们的模型确定了 11 类矿化潜力区域，并为不同的预测类别提供了地质解释。