Constraints on the Geometry and Gold Distribution in the Black Reef Formation of South Africa Using 3D Reflection Seismic Data and Micro-X-ray Computed Tomography

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 (mu CT) and machine learning to determine mineral association and composition. Reflection seismic reveals that the Black Reef Formation is a planar horizon that dips < 10 degrees and has a well-preserved and uneven paleotopography. Several large-scale faults and dikes (most dipping between 65 degrees and 90 degrees) crosscut the Black Reef Formation. Petrography reveals that gold is commonly associated with pyrite, implying that mu CT 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.

Automated summary

Geological and geophysical models were used to study the gold-bearing Black Reef Formation in South Africa. The study utilized high-resolution 3D reflection seismic data, petrography, and 3D micro-X-ray computed tomography (mu CT) to understand the orebody's features and mineral association. Machine learning was also employed to discriminate between pyrite and gold. These approaches can enhance orebody assessment and be integrated into future geometallurgical frameworks.