ABSTRACT
Stochastic conditional simulation techniques are increasingly being used to build 3D models of grades to assess the risk of mining projects. The advantage of stochastic simulation over interpolation is that it provides alternate model realisations that reproduce the univariate (mean, variance, and histogram) and bivariate (variogram, cross-variogram) statistics inferred from the sampled data. The set of alternative realisations provides a quantitative measure of spatial uncertainty, and, through the application of a transfer function measures the space of uncertainty. The results of a case study are presented for sharing the experience in modelling the estimation error uncertainty measure in form of grade tonnage curves for the simulated models. Subsequent use of a transfer function, and analysis of the cashflow and NPV provides an understanding of the potential risk in this venture as a direct result of the stochastic conditional simulation.
