The use of indirect distributions of selective mining units for assessment of recoverable mineral resources designed for mine planning at Gold Fields’ Tarkwa Mine, Ghana
W Assibey-Bonsu, J Searra, M Aboagye

For new mining projects or for medium- to long-term areas of existing mines, drilling data is invariably on a relatively large grid. Direct estimates for selective mining units (SMUs), and also for much larger block units, will then be smoothed due to the information effects and the high error variance.
The difficulty is that ultimately, during mining, selection will be done on the basis of SMUs on the final close-spaced data grid (grade control), which will then be available, i.e. the actual selection will be more efficient, with fewer misclassifications. However, this ultimate mining position is unknown at the project feasibility stage and therefore has to be estimated. This estimation is required because any cash flow calculations made on the basis of the smoothed estimates will obviously misrepresent the overall economic value of the project, i.e. the average grade of blocks above cut-off will be underestimated and the tonnage overestimated for cut-off grades below the average grade of the orebody. Similarly, unsmoothed estimates will be conditionally biased and will give even worse results, particularly in local areas of short- and medium-term planning or mining.
This paper presents a case study of indirect post-processing and proportional modelling of recoverable resources designed for mediumand long-term mine planning at the Gold Fields’ Tarkwa Mine in Ghana. The case study compares long-term indirect recoverable mineral resource estimates based on typical widely spaced feasibility data to the corresponding production grade control model as well as the mine production. The paper also proposes certain critical regression slope and kriging efficiency control limits to avoid inefficient medium- to longterm recoverable estimates, and highlights the danger of accepting block estimates that have a slope of regression less than 0.5.
Keywords: indirect conditioning, smoothing effect, conditional biases, postprocessing, kriging efficiency, regression slope, information effect.