Block Modelling Based on Grade Domaining: Is It Reliable?
The mineral resource classification is one of the steps in feasibility assessment of a mining project. It involves a quantitative evaluation of hard da- ta obtained from exploratory drilling and sampling procedures. Several approaches are aimed to accurately predict spatial variability and complex relationships between cross- correlated variables. However, addressing the grade constraints, such as mining grade domains arrangement and respective boundaries uncertainty, meaningfully is still doubtful. In this study, continuous-categorical variables relationships were analyzed with respect to a Shubarkol coal deposit located in Kazakhstan. One of the common methodologies in mining is grade domaining, for which the grade of interest should be truncated into sub-domains. Each sub-domain introduces a homogenous area that can be considered as a container for grade estimation. For this study, the main variable of interest is ash content, varying from 0 to 90%. A cut-off value of 45% was set to the ash variable, thus forming two domains: low ash domain with Coal variable, and high ash domain with Waste variable. In this paper, we propose an integrative algorithm as an alternative methodology for coherent mineral resource estimation, comprising of sequential indicator simulation for categorical variables and turning bands simulation for continuous variables modelling. In contrast, a global geostatistical analysis of the deposit without domaining is presented as well. The resulting estimates of this research showed satisfying reproduction of the deposit structure free of grade domaining, which can be adopted to precisely estimate the volume of a coal mine deposit.
KeywordsGrade domaining Continuous-categorical variables Coal deposit
The authors are grateful to Nazarbayev University for funding this work via ‘‘Faculty Development Competitive Research Grants for 2018–2020 under Contract No. 090118FD5336.’’ The second author acknowledges the Social Policy Grant (SPG) supported by Nazarbayev University. The authors also thank the ERG Company in Kazakhstan for providing the dataset.
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