Optimal Grade Control Using Geostatistics And Economics: Methodology And Examples (0cbcdec1-207d-4fc8-8d49-cd716d6fcdc7)

An important problem in mine operations is the classification of material as waste, low-grade stockpile, and ore. This classification must often be made with blasthole data that are widely spaced and that have sampling errors. Geo-statistical simulation techniques combined with basic economic principles allows a procedure for classification that maximizes expected profit. Geostatistical simulation methods (Gaussian, indicator, or annealing-based) allow the integration of hard and soft data in the creation of alternative, equally-probable, realizations of the mineral grade(s). At each location, for each realization, we calculate the "profit" if the block were to be classified as ore or waste The optimal classification is the one that maximizes expected profit (a Maximum Profit Se-lection (MPS) procedure). We discuss the theoretical justification of the method and implementation details. The use of blasthole data from different types of mineral deposits, that is, different levels of continuity, is considered. We also show the efficacy of the procedure with different levels of sampling error. The increased revenue due to the MPS procedure and improved sampling is shown. We show the geostatistical simulation procedure and the uncertainty in block grades that result from incomplete and imperfect sampling. The optimal classification is presented. Optimal block classifications are transferred to realizable dig-limits by hand-drawing polygonal boundaries. We compare the results of the proposed method to classification based on kriging. Limitations and areas of future work are identified.