Abstract
In mineral resource estimation, identification of the geological domains to be used for modeling, and the type of boundaries dividing them, is a major concern. Generally, the variables within a domain are estimated with an assumption of the hard boundaries (sharp contact). However, in many cases, the geologic structures that generate a deposit are transitional (overlapping of several geologic domains). Consequently, boundary identification of the geological domains is essential for an accurate estimate of resources. This paper considers a real application to examine whether the addition of geologic information benefits grade estimation in the presence of transitional boundaries. Results proved that the accuracy of the grade estimation can be improved by adding geological information and there is a significant sensitivity in grade estimation results in the existence of transitional boundaries.
Highlights
IntroductionGeo-engineering projects involve characterization of the geology of the area under study
Geo-engineering projects involve characterization of the geology of the area under study.Identifying the exact borders of geological domains and assessing the uncertainty of these borders are crucial steps
This paper describes the estimation methods applied to an iron deposit with transitional boundaries in which the sequential framework adopted added geological information at each step
Summary
Geo-engineering projects involve characterization of the geology of the area under study. Identifying the exact borders of geological domains and assessing the uncertainty of these borders are crucial steps. Geostatistics, as proposed by Matheron [1], takes into account the spatial variability and randomness inherent in any resource estimation. Kriging, a geostatistical estimation method, is used as an unbiased linear estimate of point values (point kriging) or block averages (block kriging) with minimum error variance [2]. Different variants of kriging estimators have been developed depending on the available source of information and spatial variability of the variable in question [3]. The literature on kriging methods and their applications is vast [3,4,5,6]
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