Dirkx Rein, Dimitrakopoulos Roussos
COSMO-Stochastic Mine Planning Laboratoy, Department of Mining and Materials Engineering, McGill University, FDA Building, 3450 University Street, Montreal, QC H3A 0E8 Canada.
Math Geosci. 2018;50(1):35-52. doi: 10.1007/s11004-017-9695-9. Epub 2017 Jul 17.
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.
采矿作业在其生命周期中会多次面临关于额外钻探的决策。做出此决策时总会出现的两个问题是是否需要更多钻探，如果需要，额外的钻孔应位于何处。本文提出的方法通过在多臂赌博机（MAB）框架中进行优化来解决这两个问题。MAB通过对所考虑的矿床进行多次条件模拟，在考虑地质不确定性的同时优化最佳加密钻探模式。所提出的方法应用于一个长期的多元素堆场，该堆场是一个金矿综合体的一部分。由于混合要求难以满足，该金矿综合体中的堆场特别受关注。在几个采矿阶段，只有使用大量堆存材料才能满足加工厂有害元素的品位目标。最佳模式是根据对堆场中的矿块造成最多的材料类型变化来定义的。材料类型变化是提取顺序变化的驱动因素，而提取顺序最终决定了采矿作业的价值。所提出方法的结果展示了其实际应用方面以及在优化加密钻探方案方面的有效性。
