Merging 3D geological modeling and stochastic simulation to foster waste rock upstream management

Three-dimensional geological modeling is an efficient tool to visualize ore body features during both exploration and operation phases of mines. Repurposing the 3D geological modeling for mine waste management allows for the visualization of hazardous metal(loid)s distribution throughout an ore body and its host rock. With this information, a mine manager could seamlessly carry out waste rocks management based upon their classification. The major prerequisite to such an approach is to procure sufficiently large datasets in order to ensure high interpolation quality and suitable resolution. Apart from metals of economic interest, non-economic and deleterious elements usually do not undergo exhaustive geochemical analyses throughout the footwall and the hanging wall of ore bodies. Based on that premise, the ?Ele?onore mine site provided restricted grades of arsenic, the most hazardous element within the mine setting, to create a 3D spatial model of arsenic content. A stochastic process coupled with the geological logging of drill cores was created to fulfill the 3D modeling prerequisite with known margins of error. The outcome of this work consists of a multi-realization 3D spatial model of arsenic content across the ore deposit and the host rock. Each realization was assessed using available chemical analyses to underline the model?s reliability. The results revealed a spacious geochemical halo of arsenic extending up to 500 m away from the gold deposit, with up to 94% of arsenic grades exceeding 50 ppm. The process developed in this work will enable mine waste classification before stripping, thereby providing the opportunity for the formulation of proactive upstream mine waste management options that could prevent future environmental liabilities.

Automated summary

Three-dimensional geological modeling is an efficient tool for visualizing ore body features and can be repurposed for mine waste management. Adequate datasets are essential to ensure quality and resolution in this process.