Optimizing Infill Drilling Decisions Using Multi-Armed Bandits: Application in a Long-Term, Multi-Element Stockpile

Abstract

Mining operations face a decision regarding additional drilling several times during their lifetime. The two questions that always arise upon making this decision are whether more drilling is required and, if so, where the additional drill holes should be located. The method presented in this paper addresses both of these questions through an optimization in a multi-armed bandit (MAB) framework. The MAB optimizes the best infill drilling pattern while taking geological uncertainty into account by using multiple conditional simulations for the deposit under consideration. The proposed method is applied to a long-term, multi-element stockpile, which is a part of a gold mining complex. The stockpiles in this mining complex are of particular interest due to difficult-to-meet blending requirements. In several mining periods grade targets of deleterious elements at the processing plant can only be met by using high amounts of stockpiled material. The best pattern is defined in terms of causing the most material type changes for the blocks in the stockpile. Material type changes are the driver for changes in the extraction sequence, which ultimately defines the value of a mining operation. The results of the proposed method demonstrate its practical aspects and its effectiveness towards the optimization of infill drilling schemes.