Uncertainty Integration in Dynamic Mining Reserves

Abstract

In a mining routine, the uncertainty assessment of resources can be evaluated through stochastic simulation methods that allow the characterisation of local probability density functions on point or block support and, consequently, the spatial uncertainty of grades per ore type. After the characterisation of mean grades and uncertainty per block, the main role of mine planning consists of characterising the time scheduling of reserves to arrive at a mining sequence. This paper seeks to transform the estimated block grades and uncertainty into the temporal production flow of average grades and consequent temporal uncertainty. The most straightforward approach consists of calculating the mining sequence for each of the simulated realisations of blocks, followed by accessing the uncertainty of each period in each sequence, and finally opting for the optimal sequence, according to an objective function. However, this approach needs to retain the N simulated models and calculate the mining sequence for each one, which can be a cumbersome task, particularly if the dimension of the block model is high. Hence, this paper proposes two methods: one using a Gaussian mixture model, and the other using a quantile interpolation mixture model to aggregate each period’s block production forecast by converting each block’s static uncertainty into production dynamic uncertainty. This allows for the period production uncertainty to be used as an optimisation parameter in mine planning routines. A test on the Neves Corvo mine synthetic case study is presented, demonstrating the applicability of this method in the context of an internal blending strategy or a selective mining approach.