A Novel Index for Quantifying Small-Scale Resource Heterogeneity

The objective of the work presented in this paper is to provide a quantitative measure of grade heterogeneity at the selective mining unit scale within relatively large estimated resource blocks. The purpose of the work is to provide a means of evaluating the potential for selectively mining a resource block and thereby maximising recovery and minimising the processing of waste. Such a measure is effectively a downscaling of the geostatistically estimated heterogeneity at the resource block scale to that of the selective mining unit scale. The major challenge in modelling and predicting heterogeneity at this scale is that the degree of heterogeneity is a function of scale, where sample density usually limits estimation and prediction to spatial dependence approaches, namely those based on the concept that the similarity of grades depends on the (directional) distance that separates them. This work extends the concept of grade heterogeneity to include the degree of clustering of different classes of attribute values. A heterogeneity index based on these clusters provides a quantitative measure of the heterogeneity of these classes. Three synthetic case studies, based on a real mining operation, are used to evaluate the performances of various approaches. Of the methods evaluated, the local conditional simulation approach is the most suitable method for estimating the heterogeneity index.