Geological and geophysical models are essential for developing reliable mine designs and mineral processing flowsheets. For mineral resource assessment, mine planning, and mineral processing, a deeper understanding of the orebody's features, geology, mineralogy, and variability is required. We investigated the gold-bearing Black Reef Formation in the West Rand and Carletonville goldfields of South Africa using approaches that are components of a transitional framework toward fully digitized mining: (1) high-resolution 3D reflection seismic data to model the orebody; (2) petrography to characterize Au and associated ore constituents (e.g., pyrite); and (3) 3D micro-X-ray computed tomography (µCT) and machine learning to determine mineral association and composition. Reflection seismic reveals that the Black Reef Formation is a planar horizon that dips < 10° and has a well-preserved and uneven paleotopography. Several large-scale faults and dikes (most dipping between 65° and 90°) crosscut the Black Reef Formation. Petrography reveals that gold is commonly associated with pyrite, implying that µCT can be used to assess gold grades using pyrite as a proxy. Moreover, we demonstrate that machine learning can be used to discriminate between pyrite and gold based on physical characteristics. The approaches in this study are intended to supplement rather than replace traditional methodologies. In this study, we demonstrated that they permit novel integration of micro-scale observations into macro-scale modeling, thus permitting better orebody assessment for exploration, resource estimation, mining, and metallurgical purposes. We envision that such integrated approaches will become a key component of future geometallurgical frameworks.

地质和地球物理模型对于开发可靠的矿山设计和选矿流程至关重要。对于矿产资源评估、矿山规划和矿物加工，需要更深入地了解矿体的特征、地质、矿物学和可变性。我们研究了南非西兰德和卡尔顿维尔金田的含金黑礁组，使用的方法是向全数字化采矿过渡的框架的组成部分：（1）高分辨率 3D 反射地震数据来模拟矿体；(2) 岩相学以表征金和相关的矿石成分（例如，黄铁矿）；(3) 3D 微型 X 射线计算机断层扫描 (µCT) 和机器学习以确定矿物组合和成分。反射地震表明，黑礁组是一个倾角<10°的平面层位，具有保存完好且不均匀的古地形。几个大型断层和岩脉（大部分倾角在 65° 和 90° 之间）横切黑礁组。岩相学显示黄金通常与黄铁矿有关，这意味着 µCT 可用于以黄铁矿为代表来评估黄金品位。此外，我们证明机器学习可用于根据物理特征区分黄铁矿和黄金。本研究中的方法旨在补充而不是取代传统方法。在这项研究中，我们证明了它们允许将微观尺度观测新整合到宏观尺度建模中，从而可以更好地评估勘探、资源估计、采矿、和冶金目的。我们设想这种综合方法将成为未来地质冶金框架的关键组成部分。