Ultimate pit policy via sequential Gaussian simulation

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Avalos, Sebastian
Ortiz, Julian M.
In open pit projects, defining the limits of an ultimate pit is required to quantify the total mineral reserves. A single economic block model assessment along with spatial constrains serve as input to conventional ultimate pit limit algorithms. Thus, considering only estimated block model attributes has become an accepted practice. Dealing with several possible realizations of a single attribute has led to the development of new algorithms that implement conventional methods and account for uncertainty in the attributes. In this work, we propose a heuristic algorithm that transfers the uncertainty in the block model attributes into probabilities of ore-waste classification and inclusion in the ultimate pit limit. Based on the possible distribution of each attribute, a solution space of economic responses is computed over each policy in terms of probability thresholds. By using economic risk analysis, an optimum policy can be found. We demonstrate the feasibility of the methods over a porphyry copper deposit by comparing the results of a conventional approach based on kriging estimation and the proposed methods based on sequential Gaussian simulations.
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