Geoscience Reference
In-Depth Information
Fig. 7.9
Grade-Tonnage Curves for the Osvaldo Diez Vein, Cerro Van-
guardia Mine, Argentina. There is a high volume-variance effect. The
2 m composites distribution is shown along with the DG-predicted and
affine-predicted SMU distributions
The volume-variance correction of drill hole information
for each estimation domain can also provide a target global
distribution of blocks (SMUs), grade-tonnage curves that can
be used to calibrate and/or check the grade-tonnage curves
resulting from the resource block model, and in particular for
specific cutoffs. The comparison between the actual versus
target distributions can also be done through distribution pa-
rameters, such as the Coefficient of Variation (CV), a robust
measure of variability.
Figure
7.10
shows a comparison of the grade-tonnage
curves of the DGM-predicted SMU and the estimated block
model grades for the high enrichment units of the Escondida
Norte Porphyry Copper deposit. Note that for most cutoff
grades the estimated grades of the block model are slightly
smoother than the corresponding DG-predicted SMU distri-
bution. The conclusion from Fig.
7.10
is that the estimated
resource model is incorporating additional dilution, besides
the internal dilution represented by the DG model. In this
case, the SMU size is 20 × 20 × 15 m, 15 m composites were
used to estimate the block model, and the cutoffs of interest
are in the range of 0.3 to 0.7 % Cu.
Another application of the volume-variance correction is
to help define the selectivity of the mine. This can be ap-
proximated by quantifying the impact that different mining
equipment used in the operation has on dilution, and based
on changes in the volume of the SMU. Most commonly,
operations study the impact of changes in bench heights.
However, there are limitations to the use of volume-variance
methods to predict optimal bench heights, because of the
free and perfect selection assumptions.
7.4
Information Effect
The Information Effect describes the fact that, at the time of
mining, the information used to decide which portion of the
deposit is ore and which is waste is based on more informa-
tion than that available when obtaining a resource model.
Ore/waste selection is described in more detail in Chap. 13.
Although more data is available, the ore/waste selection is
always made with an estimate and not the true grades. This
is imperfect selection in the sense that an estimation error
is always present. Additionally, the selection process is not
free, meaning that each SMU is not selected as ore or waste
independently of other SMUs in the vicinity. There may be
other geometrical and mining constrains that restrict the ac-
cessibility of each SMU. All these approximations and sourc-
es of error are implicit in the Information Effect.
The problem of selection can be mathematically described
by the following recovery equations:
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≥
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=
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v
c
i
(; )
u
z
v
c
0
if
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u
<
z
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v
c
