Multivariate iron ore deposit resource estimation – a practitioner's guide to selecting methods

Abstract

When only wide-spaced drilling is available, for example at concept, pre-feasibility and feasibility stages, properly implemented linear estimation (including ordinary kriging) predicts grade–tonnage relationships that are distorted compared to final production estimates (and production). Non-linear estimation and conditional simulation (CS) are alternative geostatistical approaches that can provide more reliable estimates of the recoverable tonnage and grade (i.e. the ultimate production grade–tonnage relationships) from wide-spaced drilling. Non-linear estimation and CS are not commonly used on iron ore deposits. However, these techniques have had wide application in other commodities such as gold and base metals. Conditional simulation has been used in the iron ore industry; however, its use is outlined in this paper as a means of generating non-linear estimates rather than for variability and drill spacing analysis. As a rule, regardless of commodity, the decision to use non-linear geostatistics will necessitate increased skills and require more time. This decision must therefore be justified in terms of cost and benefit. Such cost-benefit analysis is not straightforward, and to help, an approach for determining when linear estimates are inadequate is presented. The global 'Discrete Gaussian Model' (DGM) of change of support is a well established non-linear geostatistical approach. This method is recommended as a tool to establish whether progressing to non-linear methods will materially improve prediction of the ultimate grade–tonnage relationships. The additional use of DGM as a block model validation tool is also discussed. One specific factor contributing to the lack of application of non-linear geostatistical methods for iron ore deposits is the added difficulties that arise when CS and non-linear estimates are required to reproduce the numerous and often important correlations between variables. For this reason, a synoptic review of non-linear estimation and simulation methodologies applicable to correlated variables is presented.