【摘要】 随着找矿工作全面向深部与隐伏区拓展，传统预测方法与单一机器学习模型面临泛化能力弱、缺乏地质可解释性等严峻挑战。为破解上述难题，本文系统梳理了“数据与知识双驱动”智能找矿范式的发展脉络，并构建了包含“数据-知识融合层、智能建模-解构层、应用验证-反馈层”的三层理论架构。本文深入剖析并凝练了打破“黑箱”壁垒的关键技术路径，指出基于知识图谱嵌入与图注意力机制的协同约束是当前实现数据与知识深度融合的核心机制。研究系统阐明了该机制的工作逻辑：通过地质本体的硬约束剔除空间无关噪声，并利用协同赋权的软约束引导模型自适应关注高致矿特征，从而建立了从野外实证到模型迭代优化的完整反馈闭环。综合分析表明，双驱动模式有效实现了人类专家成矿逻辑与机器算力的高效协同，显著提升了找矿模型的可解释性与预测精度。本研究可为推动地质找矿向智能化决策跨越、培育矿业新质生产力提供系统的理论参考与指引。更多还原

【Abstract】 As mineral exploration increasingly targets deep-seated and concealed deposits, conventional prospecting methods and standalone machine learning models face critical limitations in generalization and geological interpretability. This study systematically delineates the evolution of the data-knowledge dual-driven intelligent paradigm and proposes a three-tier architecture: data-knowledge fusion, intelligent modeling-deconstruction, and application validation-feedback. Investigating technical pathways to open the “black box” of predictive models, this study identifies collaborative constraints—achieved through Knowledge Graph Embedding(KGE) and Graph Attention Mechanisms(GAT)—as the pivotal mechanism for deep data-knowledge synthesis. The operational logic involves a dual-constraint strategy: hard constraints derived from geological ontologies filter spatially irrelevant noise, while soft constraints via collaborative weighting enable models to adaptively capture high-probability metallogenic features. This framework establishes a closed-loop feedback system that integrates field empirical evidence with iterative model refinement. Results indicate that this dual-driven approach harmonizes expert metallogenic logic with machine computational power, significantly enhancing the interpretability and predictive precision of mineral potential mapping. This research provides a robust theoretical foundation for advancing intelligent decision-making and fostering transformative productivity growth in the global mining sector.更多还原