Abstract:

Conventional approaches to define the ultimate pits limit consider an assessment of a single economic block model based on estimated attributes. Integrating risk in the definition of the ultimate pit limit requires accounting for several realizations of the attribute and extending the conventional techniques to consider the variability in the final pit definition. We propose and develop a heuristic approach that transfers the uncertainty in the block model attributes into probabilities of ore-waste classification and probability of each block to belong to the ultimate pit. Considering the attribute spatial distribution, the ore-waste management strategy, and the economic and processing parameters, a space of economic responses is computed over each pair of probability thresholds. By using economic risk metrics, optimum policies are drawn in terms of these probability thresholds. We demonstrate the technique over a copper deposit. We compare results of conventional approaches based on ordinary kriging and the proposed technique based on sequential Gaussian simulations.

Biography of the presenter: Sebastian Avalos. Mining Engineering from University of Chile (2012) and MSc in Mining - University of Chile (2014). PhD Mining Engineering candidate (third year) at Queen's University. Seven years working on predictive modelling and resource modelling in the mining industry.

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Mr. Sebastian Avalos