Abstract
Abstract Most mining decisions are based on models estimated/simulated given the information obtained from samples. During the exploration stage, samples are commonly taken using diamond drill holes which are accurate and precise. These samples are considered hard data. In the production stage, new samples are added. These last are cheaper and more abundant than the drill hole samples, but imprecise and are here named as soft data. Usually hard and soft data are not sampled at the same locations, they form a heterotopic dataset. This article proposes a framework for geostatistical simulation with completely heterotopic soft data. The simulation proceeds in two steps. First, the variable of interest at the locations where soft data are available is simulated. The local conditional distributions built at these locations consider both hard and soft data and are obtained using simple cokriging with the intrinsic coregionalization model. Second, the variable of interest in the entire simulation grid using the original and previously simulated values at soft data locations is simulated. The results show that the information from soft data improved both the accuracy and precision of the simulated models. The proposed framework is illustrated by a case study with data obtained from an underground copper mine.
Highlights
Decisions in mining operations are generally based on estimated/simulated grade models
The conditional cumulative distribution functions required for p-field simulation were built with MultiGaussian cokriging using all the hard and soft data inside the search neighborhood
The direct and cross-covariances required for cokriging were obtained by the intrinsic coregionalization model (ICM), so that all the variograms are proportional to the variogram of the primary variable
Summary
Decisions in mining operations are generally based on estimated/simulated grade models. These experimental cross-correlograms may be very sensitive to their calculation parameters, such as lag spacing and tolerances Another problem is that the sequential Gaussian co-simulation requires solving the cokriging system at all the nodes of the simulation grid whose search neighborhood includes secondary data. Araujo et al (2019) proposed Bayesian Updating (Deutsch and Zanon 2004; Doyen et al 1996; Neufeld and Deutsch 2006) to build the local probability distribution at each location where a soft datum is available and sequential Gaussian simulation to complete the remaining grid nodes. The conditional cumulative distribution functions required for p-field simulation were built with MultiGaussian cokriging using all the hard and soft data inside the search neighborhood. The direct and cross-covariances required for cokriging were obtained by the intrinsic coregionalization model (ICM), so that all the variograms are proportional to the variogram of the primary variable.
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