Three-Dimensional (3D) Lithological Modeling via Hybrid Attentional Mechanism Deep Learning Model: A Case Study of Jiaojia Gold Field
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摘要:
矿田尺度深部地质结构“透明化”是矿产勘查与成矿预测的核心，三维岩性建模是实现这一目标的关键技术.然而，当前矿田尺度三维岩性建模主要依赖效率较低的显式建模方法，难以满足多阶段矿产勘查和矿山实时生产的需求，因此亟须研发高精度、高效率的三维岩性隐式建模方法.针对上述问题，本研究以三维卷积神经网络（3D Convolutional Neural Network，3D CNN）为基础，融合卷积注意力机制（Convolutional Block Attention Module，CBAM）与自注意力机制（Self-Attention Module，SAM）构建混合注意力机制深度学习算法（Hybrid Attentional Mechanism deep learning model，HAM），并基于该算法挖掘多源地质‒地球物理数据中的深层次特征，确定建模所需地质体边界，实现既能捕捉局部上下文、又能表征全局上下文的三维岩性隐式建模方法.为验证HAM算法有效性，本研究选择胶东半岛焦家金矿田作为研究区，开展对比实验与消融实验.结果表明，相较于随机森林（Random Forest，RF）和3D CNN等基线算法，本次研究提出的HAM算法在三维岩性建模的准确率、宏平均精确率、召回率、宏平均F1分数和混淆矩阵上表现出显著优势，对推动深部找矿和矿山生产具有重要意义.
Abstract:Enhancing the transparency of deep geological structures at the ore-field scale is critical for subsurface mineral exploration and prospectivity modeling, and three-dimensional (3D) lithological modeling serves as a critical technology for this objective. However, existing ore-field-scale modeling workflows rely on explicit modeling approaches with relatively low efficiency, which can hardly meet the demands of multi-stage mineral exploration and real-time mining. Consequently, high-precision and high-efficiency implicit 3D lithological modeling methods are urgently needed. To address this issue, a Hybrid Attentional Mechanism deep learning model (HAM) is constructed on the basis of the 3D Convolutional Neural Network (3D CNN), integrating the Convolutional Block Attention Module (CBAM) and the Self-Attention Module (SAM). Based on this algorithm, deep representations within multi-source geological and geophysical data are mined to determine the boundaries of geological bodies required for modeling, thereby achieving a 3D lithological implicit modeling method capable of capturing both local details and long-range dependencies. To validate the effectiveness of the proposed hybrid attentional mechanism model, the Jiaojia gold field in the Jiaodong Peninsula was selected as the study area, and comparative and ablation experiments were conducted. Relative to baseline models‒Random Forest (RF) and a vanilla 3D-CNN, HAM markedly improves the macro-averaged accuracy, precision, recall, macro-averaged F1 score and confusion matrix of ore-field-scale implicit 3D lithological modeling, with direct implications for subsurface mineral exploration and mining operations.
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图 1 研究区大地构造位置（a；改自朱日祥等, 2015）；胶东半岛（b；改自张智强, 2022）和研究区（c）地质简图
Fig. 1. Tectonic setting of the study area (a; modified from Zhu et al., 2015); simplified geological map of the Jiaodong Peninsula (b; modified from Zhang, 2022) and the study area (c)
图 2 研究区用于三维岩性建模的钻孔分布
Fig. 2. Borehole distribution map of the study area for 3D lithologic modeling
图 3 焦家金矿田剩余重力异常（a）和化极磁异常（b）(改自Zhang et al., 2023b)
Fig. 3. Bouguer gravity anomaly (a) and reduced-to-the-pole magnetic anomaly (b) in the Jiaojia gold ore field (modified from Zhang et al., 2023b)
图 4 焦家金矿田主要岩石物性箱型图密度（a）、磁化率（b）和电阻率（c）（改自Zhang et al., 2023b）
Fig. 4. Box plots of major rock physical properties in the Jiaojia gold ore field density (a), magnetic susceptibility (b) and resistivity (c) (modified from Zhang et al., 2023b)
图 5 研究区三维密度模型（a）、三维磁化率模型（b）和三维电阻率模型（c）
Fig. 5. Three-dimensional models of the study area: 3D density model (a), 3D magnetic susceptibility model (b) and 3D resistivity model (c)
图 7 模型学习曲线（损失与准确率）
Fig. 7. Plot of model learning curves depicting loss and accuracy metrics
图 8 混淆矩阵，其中0、1、2分别代表新太古代胶东群、玲珑花岗岩、郭家岭花岗闪长岩
Fig. 8. Confusion matrix, where 0, 1, and 2 represent the Neoarchean Jiaodong Group, Linglong granite, and Guojialing granodiorite, respectively
图 9 三维岩性预测模型: RF（a）；3D CNN（b）；3D CNN-CBAM（c）；3D CNN-SAM（d）；HAM（e）
Fig. 9. Three-dimensional lithological prediction model: RF(a), 3D CNN(b), 3D CNN-CBAM(c), 3D CNN-SAM(d) and HAM(e)
表 1 不同模型训练过程性能对比
Table 1. Performance comparison of different models
模型 准确率 宏平均
精确率召回率 宏平均
F1分数RF 0.969 0 0.918 9 0.920 7 0.919 5 3D CNN 0.975 8 0.874 1 0.944 4 0.905 5 3D CNN-CBAM 0.983 4 0.901 3 0.972 5 0.933 2 3D CNN-SAM 0.984 2 0.896 2 0.969 8 0.928 6 HAM 0.988 3 0.928 5 0.974 1 0.949 5 
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