![]() ( 2001) also propose a method based on conditional simulations to assess the value of additional drill holes. Therefore, simulations are used in this approach to infill drilling optimization and the assessment of additional patterns under geological grade uncertainty. Ravenscroft ( 1992) shows that working with geological simulations is the best way to represent variability in a deposit and that estimated orebody models give a flawed representation of the true variability in a deposit. A flaw in using kriging variance as a measure of variability is that it only captures the geometric part of the uncertainty for a drilled deposit and does not take into account the variability of grades (Goovaerts 1997 Journel and Kyriakidis 2004 Rossi and Deutsch 2014). ( 1988), Delmelle and Goovaerts ( 2009) and others explore infill drilling optimization by focusing on minimizing the kriging variance or trying to locate zones of high kriging variance as prime spots for additional drilling or alternative approaches. This effect is strengthened if multiple processing streams are considered in combination with the blending of material from different sources, as in the application presented below.īarnes ( 1989), Diehl and David ( 1982), Gershon et al. This makes it impossible to evaluate misclassification errors without rescheduling the deposit. ![]() ( 2007) show that it is optimal to use a variable cut-off grade that comes directly from the schedule optimization. However, this is usually not the case in a mining complex. This misclassification refers to ore versus waste based on a fixed cut-off grade and one processing stream. ![]() ( 2005) indirectly do this via a misclassification cost of material. This is hard to quantify for a mineral deposit. In the aforementioned works, the value of future information is a monetary value. ( 2008) valuate information gathering campaigns on the sealing capacity of a fault system. In the mineral industry, Chorn and Carr ( 1999) assess the value of future information for decisions on additional capital investments, and Prange et al. However, the requirement of a payment to retrieve this information might render the total value negative, causing a need to valuate this future information properly. In terms of the economic utility theory, Schlee ( 1991) proves that perfect information always has a positive value. The more common term for this problem used across these various industries is the valuation of future information (under uncertainty). Optimizing an infill drilling pattern can be linked to a more general problem present in many different applications such as stock markets, research effort direction, project development, and others. ![]() The results of the proposed method demonstrate its practical aspects and its effectiveness towards the optimization of infill drilling schemes. Material type changes are the driver for changes in the extraction sequence, which ultimately defines the value of a mining operation. The best pattern is defined in terms of causing the most material type changes for the blocks in the stockpile. 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 stockpiles in this mining complex are of particular interest due to difficult-to-meet blending requirements. The proposed method is applied to a long-term, multi-element stockpile, which is a part of a gold mining complex. 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 method presented in this paper addresses both of these questions through an optimization in a multi-armed bandit (MAB) framework. 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. Mining operations face a decision regarding additional drilling several times during their lifetime.
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