Optimally Locating Additional Drill holes to Increase the Accuracy of Ore/Waste Classification

Abstract

Over the life of a mine it is often necessary to drill additional holes to address new, or changing, objectives. Most previous algorithms proposed for this purpose have aimed at reducing the block model uncertainty and enhancing the value of the drilling information as their objective functions, and have paid little or no attention to improving the accuracy of the ore/waste classification. In this paper the authors have used the misclassification probability parameter to define an objective function for the optimization of the location of additional drill holes. Using the simulated annealing method, the efficiency of the proposed objective function has been validated and proven in optimally locating additional drill holes for an application in a phosphate mine. An advantage of this objective function, compared with the usual ones, is that in addition to having a direct relationship to the kriging variance, it depends highly on the estimated and cut-off grades; the greater the difference between these grades, the less will be the misclassification probability. Since these two grades remain unchanged as the number of the drill holes increases, the only way to reduce the misclassification probability is to reduce the estimation variance by drilling the additional drill holes in appropriate locations.