Mineral Resources Evaluation with Mining Selectivity and Information Effect

Abstract

The most common approach used in the mining industry for mineral resources modeling is to estimate the grades using ordinary kriging and report the recoverable resources based on this deterministic estimated model. Mineral resources calculated with kriging are a smooth representation of the actual distribution of grades and do not provide an assessment of uncertainty. Unlike kriging, simulation reproduces the variability of the grades in the mineral deposit and provides an assessment of uncertainty. Reporting mineral resources directly on high-resolution simulation results would assume perfect knowledge of the grade at the time of mining and selectivity at the scale of the data. There will always be uncertainty left at the time of mining, so assuming perfect knowledge of the grade in the future is incorrect. There are two concerns when geostatistical simulation is used for resources modeling: the information and the mining selectivity effects. A new framework for resource estimation is proposed with two separate modules to address those concerns. The information effect is accounted for by anticipating the additional production data that will be available at the time mining to guide the destination for the mined material. The mining selectivity effect is addressed by mimicking the grade control procedure to get mineable dig limits at a chosen selectivity, represented by a minimum mineable unit size. In addition to a prediction of recoverable resources that will be closer to the material mined in the future, the framework proposed provides an assessment of local and global uncertainty for risk management.