When only wide-spaced drilling is available, for example at concept, prefeasibility and feasibility stages, properly implemented linear estimation (including ordinary kriging) predicts grade tonnage relationships that are over-smoothed compared to final production estimates (and production). Non-linear estimation and conditional simulation are alternative geostatistical approaches that can provide more reliable estimates of the recoverable tonnage and grade from wide-spaced drilling._x000D_
Non-linear estimation and conditional simulation have been rarely 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 (eg Guibal et al, 1997); however, its use here is outlined as a means of generating non-linear estimates, not just its use for variability and drill spacing analysis._x000D_
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-benefit. Such cost-benefit analysis is not straightforward, and to help an approach for determining when linear estimates are inadequate is presented. Recommend in this paper is a well-established non-linear geostatistical approach, the global discrete Gaussian model' (DGM) of change of support, as a tool to establish whether moving from linear to non-linear estimates will materially improve estimate results. Also discussed is the additional use of DGM as a block model validation tool._x000D_
One specific factor contributing to the lack of application of non-linear geostatistical methods in iron is the added difficulties that arise when conditional simulation and non-linear estimates are required to reproduce the numerous and often important correlations between variables in iron ore deposits. Consequently, presented in the paper is a synoptic review of non-linear estimation and simulation methodologies applicable to correlated variables.