Application of conditional simulations to resource classification schemes
Geostatistical conditional simulations are becoming increasingly popular as tools that provide models of uncertainty at different stages of a mining project. They have been used as grade control tools in daily operations to assess the uncertainty of mineable reserves at the project’s feasibility stage, and to assess mineralization potential in certain settings. Other applications include assessment of recoverable reserves, resource and reserve classification, and drill hole spacing optimization studies. All large-scale applications of conditional simulations intend to benefit from the correct (or accurate) characterization of uncertainty, adequately describing the variability observed from the data and its effect on the final objective. The objective of this paper is to compare the ad-hoc uncertainty model provided by a traditional classification scheme with the probability intervals resulting from a conditional simulation model. It is argued that the uncertainty model derived from the use of classification schemes is of little practical use to the mining company or operation. The paper emphasizes differences in uncertainty based on the size of the mining unit considered, the extraction method, and local geological differences. Most importantly, it discusses confidence intervals for these mining units, as compared to the relative precision envisioned by a typical classification scheme. The application described in this paper is taken from a medium-sized Cu open pit and underground mine in northern Chile, and demonstrates the impact of several variables on the resulting conditional simulation models. It also compares a more traditional resource classification scheme applied, and the wide difference in local confidence intervals for each mining unit.
Geostatistical conditional simulation, Resource classification, Probability intervals, Open pit, Underground mine.