Abstract

Constructing an interpretive geological model of the subsurface based on site-specific geological, geophysical and geochemical sampling information is a primary step in the prospection, exploration and modeling of ore deposits, in order to understand geological heterogeneity and to predict the rock properties in the subsurface, which is critical for all the following stages of the mining process. To improve the accuracy of such geological models, a geostatistical approach based on the conditional simulation of quantitative variables followed with their classification into geological categories is proposed. The novelty is the use of coregionalization analysis that allows decomposing the quantitative information into uncorrelated components associated with different spatial scales and filtering the undesired components from the simulation outcomes, in particular, the nugget effect that represents small-scale variability and measurement errors.