Every mining operation faces a decision on additional drilling at some point during its lifetime. The two questions that always arise with this decision are if 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 for the best infill drilling pattern while taking geological uncertainty into account by using multiple conditional simulations for the deposit under consideration. MAB formulations are commonly used in many applications where decisions have to be made between different alternatives with stochastic outcomes, such as Internet advertising, clinical trials and others. In these fields MABs have proven their effectiveness in dealing with ``Big Data", massive quantities of data also being faced in the mining industry.
The proposed method is applied to a multi-element, long-term stockpile, which is a part of a gold mining complex in Nevada, USA. Particular interest goes to the stockpiles in this mining complex because of difficult-to-meet blending requirements. In many periods grade targets of deleterious elements at the processing plant can only be met by using high amounts of stockpile material. Therefore, sufficient information is required to accurately assess the uncertainty of materials in the stockpiles. The conditional simulations are generated by a direct block-support sequential simulation technique using Minimum/Maximum Autocorrelation Factors to cope with multiple elements. 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 extraction sequence, which ultimately defines the value of a mining operation. Therefore, these are used to assess the value of additional information. The results of the proposed method demonstrate its efficacy and applicability.
Paru en novembre 2016 , 28 pages