Application of joint conditional simulation to uncertainty quantification and resource classification

Abstract

Identifying and quantifying grade uncertainty is important in mineral resource classification from an economic perspective. Conditional simulation techniques can be used to this end. In multi-element deposits, for which spatial correlations are important, techniques such as principal component analysis and minimum/maximum auto-correlation factors can be used to transform the multivariate simulation problem into a series of univariate problems. In this study, a methodology is presented for classifying mineral resources and for assessing the uncertainty in grade–tonnage curves, with an application to a porphyry copper deposit located in the central part of Iran with three correlated variables (grades of Cu, Mo, and Ag). The resources are classified based on the simulated copper grades, considering the so-called 15 % rule. This means that the estimated grade, tonnage, and metal content in a production period will have at most 15 % error at 90 % confidence level.