Optimised ore selection from conditionally simulated block models

This study considers realistic data for a planned open-pit iron ore mine, but is applicable to any open pit situation. By interpolating drill hole data, grades are generated for a rectangular block model. Each block's grade vector has components for each analyte (chemical element or compound) influencing ore value. Deriving the average (E-type) over many simulations leads to each block being assigned its expected value, and thus underestimates the overall grade variability. Alternatively, interpolation by means of conditional simulation is a method that implements random sampling from an infinite population of solutions. Each conditional simulation has appropriate overall grade variability, but estimating any block's mean and variance requires sampling from multiple conditional simulations. For a block model, an ore/waste selection criterion maximises the expected tonnage at a target grade. This criterion is a linear composite of the grade components, with positive coefficients for the beneficial analyte (Fe) and negative coefficients for the deleterious analytes (such as SiO2, Al2O3 and P). Although conditional simulation often leads to a similar expected grade as kriging for each block, the expected maximum tonnage of ore selectable at a target grade may differ from that obtainable from the Etype solution. We apply the linear composite selection criterion to each of 25 conditional simulations, as well as to the E-type block model. Simulation confirms the distribution of product tonnage to have an expected tonnage that is over 20% greater than that of the E-type model. The method also enables a selection probability to be computed for each block, and thus a probabilistic pit boundary distribution to be identified and used in mine planning. Proposed extensions to this method will consider risk-based scheduling of the multiple selection solutions through minimisation of a derived stress factor and treating the mining process as an iterative system with actual or artificial depletions modelled in line with the mine plan, using the updated state (with new information) to re-evaluate the mine plan for subsequent periods. © Institute of Materials, Minerals and Mining and The AusIMM 2014.