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.