3D Reconstruction of Fluid Migration Pathways of Hydrothermal Gold Systems and Prospecting Prediction
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摘要:
热液成矿流体运移通道控制了热液流体的空间运移与汇聚成矿，三维重建流体通道对理解矿床成因、开展找矿预测至关重要.然而现有研究受限于采样成本高昂、研究方法定性和地质特征复杂，难以在矿床尺度重建成矿流体运移通道三维结构.本研究以胶东夏甸金矿床为研究对象，设计了一种知识‒数据协同驱动的流体通道三维重建方法框架.该框架基于矿床地质调查、勘查数据（例如钻孔、中段编录数据）确定流体通道识别标志，通过图卷积深度网络模型构建流体通道空间概率分布模型，基于马尔科夫链模型定量表征三维成矿流体运移通道.实验结果表明：（1）该模型在小样本数据条件下表现出优异的特征识别能力（AUC=0.956 9），推断的流体运移通道高概率区域与成矿流体流动认识相符；（2）夏甸金矿成矿流体来源于深部，沿招平断裂带向上呈分支扩散状运移；（3）主干流体通道分布主要受招平断裂深部产状变化控制，而密集发育的次级通道网络受次级断裂、节理、裂隙等构造控制，反映了流体运移、矿质沉淀的协同控矿作用.本研究为理解构造‒流体‒矿化耦合机制提供了新视角，揭示了夏甸金矿“主干通道运移‒分支末端沉淀”的流体流动模式，基于流体运移通道特征圈定了夏甸金矿深部2处成矿有利区.
Abstract:Three-dimensional reconstruction of fluid pathways is critical to understanding ore genesis and guiding exploration since hydrothermal fluid migration pathways exert a fundamental control on the transport, concentration, and precipitation of ore-forming fluids. However, robust three-dimensional reconstruction of fluid pathways at the deposit scale remains challenging due to complex structural overprinting, sparse sampling, and limited quantitative tools. Here, we present an integrated knowledge and data-driven framework to reconstruct the 3D hydrothermal fluid migration pathways of the Xiadian gold deposit in the Jiaodong Peninsula. Geological indicators related to fluid flow were extracted from drill-hole and mine-level datasets and incorporated into a spatial probability model using a Graph Convolutional Network (GCN). A Markov chain model was subsequently applied to quantitatively trace three-dimensional migration trajectories. The GCN demonstrates strong predictive performance under small-sample conditions (AUC=0.956 9), delineating high-probability fluid pathways that are consistent with established metallogenic models. The reconstructed pathways indicate that ore-forming fluids originated at depth and migrated upward along the Zhaoping Fault Zone, exhibiting a branching and diffusive architecture. Major fluid conduits are primarily controlled by deep-seated structural variations of the main fault, whereas dense terminal branch networks are dominated by secondary faults and fracture systems, reflecting a synergistic structural control on fluid migration and mineral precipitation. The results confirm the existence of a conceptual model of "transport along major conduits and precipitation within terminal branches" in Xiadian gold deposit, providing new insights into the coupling between tectonics, fluid flow, and mineralization. On this basis, two prospective targets for deep exploration within the Xiadian deposit are identified.
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图 1 夏甸金矿地质简图（a）与代表性地质剖面（b）
Fig. 1. Simplified geology map of the Xiadian gold deposit (a) and its representative cross-section (b)
图 2 夏甸金矿成矿概念模型（Liu et al.，2021a）
Fig. 2. Conceptual metallogenic model of the Xiadian gold deposit (Liu et al., 2021a)
图 4 基于图卷积网络生成流体通道位置概率模型流程
Fig. 4. Workflow for generating the fluid pathway probability model based on a graph convolutional network
图 5 已知区地质体三维模型
a. 金矿体；b. 招平断裂；c. 绢英岩蚀变带
Fig. 5. Three-dimensional geological models of the study area
图 6 GCN建模指标集
a. 距招平断裂距离；b. 走向；c. 陡缓；d. 蚀变带厚度；e. 金品位值；f. 通道标记
Fig. 6. Indicator datasets used for GCN modeling
图 7 损失曲线（a）、验证集准确率曲线（b）和ROC曲线（c）
Fig. 7. Training loss curve (a), validation accuracy curve (b), and ROC curve (c)
图 8 夏甸金矿床成矿流体通道概率分布（高概率区）（a）和研究区距断裂面10 m内通道概率分布（b）
Fig. 8. Fluid pathway probability in the Xiadian gold deposit (high) (a), fluid pathway probability within 10 m of the fault surface in the study area (b)
图 9 夏甸金矿床成矿流体运移通道重建
a. 成矿流体运移通道与已知矿体；b. 成矿流体运移通道与成矿流体通道高概率分布区；c. 推断流体来源方向
Fig. 9. Reconstruction of ore-forming fluid migration in the Xiadian gold deposit
表 1 GCN节点属性（$ X $）和几何输入特征（$ F $=8）
Table 1. GCN node attributes (X) and geometric input features (F=8)
特征类别 特征名称 意义 流体运移驱动 距控矿断裂距离 表达控矿断裂的作用场 断裂走向 表达控矿断裂的几何学特征 断裂陡缓 流体作用产物 蚀变场强（厚度） 表达流体与围岩作用 金品位值 直接反映矿质的沉淀结果 三维坐标 X、Y、Z 提供空间位置信息 
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表 2 流体运移通道推断模型参数设置
Table 2. Parameter settings used in the fluid pathway inference model
表达式 描述 权重（$ W $） 作用分析 $ {P}_{\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}} $ 归一化通道概率 $ {W}_{\mathrm{P}}=4.0 $ 核心驱动力，路径优先进入高概率体元 $ F{D}_{\mathrm{n}\mathrm{o}\mathrm{r}\mathrm{m}} $ 归一化距断层距离 $ {W}_{\mathrm{F}\mathrm{D}}=1.0 $ 构造约束，惩罚远离断层的路径 $ \mathrm{c}\mathrm{o}\mathrm{s}\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{ }\mathrm{ }\left(\theta \right) $ 流体运移方向是否顺应断裂上倾方向 $ {W}_{\mathrm{D}\mathrm{I}\mathrm{R}}=0.5 $ 极弱宏观约束，保持向上趋势，但允许局部转折 $ \Delta Z $ 向上Z轴分量（≥0） $ {W}_{\mathrm{D}\mathrm{Z}}=1.5 $ 重力势能奖励，偏好整体向上运移 $ \mathrm{N}\mathrm{o}\mathrm{i}\mathrm{s}\mathrm{e} $ 随机扰动项 $ {W}_{\mathrm{N}\mathrm{o}\mathrm{i}\mathrm{s}\mathrm{e}}=0.5 $ 曲折度控制，引入自然曲折
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