Modeling Geospatial Uncertainty of Geometallurgical Variables with Bayesian Models and Hilbert–Kriging

In mine planning, geospatial estimates of variables such as comminution indexes and metallurgical recovery are extremely important to locate blocks for which the energy consumption at the plant is minimized and for which the recovery of minerals is maximized. Unlike ore grades, these variables cannot be modeled with traditional geostatistical methods, which rely on the availability of a large number of samples for variogram estimation and on the additivity of variables for change of support, among other issues. Past attempts to build geospatial models of geometallurgical variables have failed to address some of these issues, and most importantly, did not consider adequate mathematical models for uncertainty quantification. In this work, we propose a new methodology that combines Bayesian predictive models with Kriging in Hilbert spaces to quantify the geospatial uncertainty of such variables in realistic industrial settings. The results we obtained with data from a real deposit indicate that the proposed approach may become an interesting alternative to geostatistical simulation.