Details

Autor(en) / Beteiligte

• Leuangthong, O.
• Nowak, M.

Titel

Dealing with high-grade data in resource estimation

Ist Teil von

• Journal of the Southern African Institute of Mining and Metallurgy, 2015-01, Vol.115 (1), p.27-36

Ort / Verlag

The Southern African Institute of Mining and Metallurgy

Erscheinungsjahr

2015

Link zum Volltext

Beschreibungen/Notizen

• The impact of high-grade data on resource estimation has been a longstanding topic of interest in the mining industry. Concerns related to possible over-estimation of resources in such cases have led many investigators to develop possible solutions to limit the influence of high-grade data. It is interesting to note that the risk associated with including high-grade data in estimation appears to be one of the most broadly appreciated concepts understood by the general public, and not only professionals in the resource modelling sector. Many consider grade capping or cutting as the primary approach to dealing with high-grade data; however, other methods and potentially better solutions have been proposed for different stages throughout the resource modelling workflow. This paper reviews the various methods that geomodellers have used to mitigate the impact of high-grade data on resource estimation. In particular, the methods are organized into three categories depending on the stage of the estimation workflow when they may be invoked: (1) domain determination; (2) grade capping; and (3) estimation methods and implementation. It will be emphasized in this paper that any treatment of high-grade data should not lead to undue lowering of the estimated grades, and that limiting the influence of high grades by grade capping should be considered as a last resort. A much better approach is related to domain design or invoking a proper estimation methodology. An example data-set from a gold deposit in Ontario, Canada is used to illustrate the impact of controlling high-grade data in each phase of a study. We note that the case study is by no means comprehensive; it is used to illustrate the place of each method and the manner in which it is possible to mitigate the impact of high-grade data at various stages in resource estimation.