Numerical Modeling of Ore-Forming Processes and Mineral Prospectivity Modeling for Magmatic-Hydrothermal Deposits
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摘要:
数值模拟方法为定量解析岩浆热液矿床的成矿过程提供了关键技术手段，对揭示控矿机理与指导成矿预测研究具有重要意义.近年来，伴随计算地球科学的迅速发展，成矿过程数值模拟研究取得了显著进展，在多个层面为成矿预测提供了有力支撑.本文系统梳理了成矿过程数值模拟的基本理论与方法，综合评述了当前数值模拟在刻画成矿过程、解析控矿机理以及支撑成矿预测等方面的研究现状，并对数值模拟方法在未来成矿预测中的发展方向作出展望.未来研究将在力‒热‒化‒流全耦合模拟、高性能数值算法开发以及多元信息智能融合等方面持续深化，共同推动成矿预测向物理机制与数据协同驱动的新范式发展.
Abstract:Numerical modeling provides a key approach for quantitatively analyzing the ore-forming processes, revealing ore location mechanisms, and facilitating mineral prospectivity studies for magmatic-hydrothermal deposits. In recent years, with the rapid advancement of computational geosciences, significant progress has been made in numerical modeling of ore-forming processes, which provides critical support for metallogenic prediction in multiple aspects. We summarize the fundamental theories and methods of numerical modeling, provide a comprehensive review of current research regarding advances in simulating ore-forming processes, analyzing ore location mechanisms, and facilitating metallogenic prediction. Finally, we conclude with an outlook on the future development of numerical modeling in advancing metallogenic prediction. We propose that future research should focus on advancing coupled mechanical-thermal-chemical-fluid processes modeling, developing efficient numerical methods, and promoting the intelligent integration of multi-source data. These efforts will collectively drive the evolution of mineral prospectivity modeling toward a new paradigm characterized by mechanism-data synergistic modeling.
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图 1 形变‒流体流动‒热传递‒化学反应耦合反馈关系（据Ord et al.，2012）
Fig. 1. Feedback relations in the fully coupled deformation-fluid flow-thermal transfer-chemical reaction (after Ord et al., 2012)
图 2 斑岩铜矿成矿过程
a、b.岩浆流体羽流及其相态；c.斑岩铜矿体富集潜力；据Weis et al.（2012）
Fig. 2. Ore-forming process of porphyry copper deposit
图 3 控矿机理的揭示和成矿预测
a.侵入体形态对温度场影响；b.矿石沉淀引起的渗透率变化；c.矿化潜力的预测；据肖凡和王恺其（2021）、Gao et al.（2024）、Hu et al.（2020）
Fig. 3. Analyze ore location mechanisms and facilitate metallogenic prediction
图 4 融合数值模拟信息的综合信息三维成矿预测
a.安庆月山地区；b.宣城狸桥地区；据Li et al.（2019）、谢先岗等（2024）
Fig. 4. 3D mineral prospectivity modeling integrating numerical modeling data
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