Geoscience Reference
In-Depth Information
The additional cost of the dedicated RC drilling should be
paid for by the increased economic benefit of the improved
grade control, since almost always blast holes still need to
be drilled for blasting. Grade control using RC drilling is a
fairly common practice in gold mines in Western Australia
and parts of Africa. It is generally applicable if the ore is
of high intrinsic value (such as high grade Au) and if the
higher-grade distribution is sub-vertical. Unfortunately, not
all operations perform a detailed cost-benefit analysis of the
use of RC drilling for grade control. The costs of using RC
drilling may be higher than the economic benefits derived
from the improved grade control.
In the case of underground mines, mining methods are
much less flexible and therefore there is generally little or
no opportunity for ore and waste selection at the time of ex-
traction. When a stope is defined as being ore, typically the
complete stope is considered ore (with the planned and un-
planned dilution as encountered). This implies that the grade
control data is actually the data used to design the stopes dur-
ing short-term planning. In such case, infill drilling is used to
decide what is ore and waste. The challenge for underground
mines is thus greater, because generally infill (or production)
data spacing is less than the equivalent blast hole grids in
open pit mines.
The modeling of grade control or infill data can be ac-
complished using conventional or geostatistical methods.
Among the latter, conditional simulations is usually the bet-
ter option, since ore/waste selection is dependent more on
the variability of the grade distribution than on its average
grade. Kriging-based methods can very easily fail (as can
the more conventional methods) because of its characteris-
tic smoothing effect which can lead to miss-classification.
Additionally, using minimum-variance estimation methods
imply penalizing the over- and underestimation errors equal-
ly, i.e., a symmetric Loss Function (Journel
1988
; Srivastava
1987
). This is generally inappropriate for mining scenarios,
since sending waste to the plant generally has a different cost
compared to sending ore to the waste dump.
Grade control models are dependent on mining practices
and methods. It is possible that more detailed and sophisti-
cated grade control methods can provide a better ore/waste
selection, but the mining method has to able to capitalize on
that opportunity. It may be an overkill to develop and im-
plement a sophisticated grade control method if the mining
method and operational practices are not good enough to
take advantage of the additional level of detail.
of the methods the reader is referred to Chap. 8. Here the
more common industry practices are discussed.
Unfortunately, even after major technological advances in
many aspects of grade control including geostatistical model-
ing, most operations still do not fully appreciate the impor-
tance of grade control, and devote insufficient resources and
thought to this task. The flexibility that open pit mines gener-
ally enjoy is not always fully utilized. Many operations work
with very simple methods that are not optimal. This is also
true for underground mines. Indeed, it is more difficult to per-
form effective grade control in underground mines because
of operational constraints, but still, too few operations have
profited from modeling advances over the last 20 or 30 years.
In open pit mines, probably the most commonly used
method to predict in-situ grades is a simple arithmetic aver-
age of the available blast holes. A block model is defined,
generally with the block size similar to the blast hole spac-
ing, and the predicted block grade is the arithmetic average
of the blast holes that fall within the block. Multiple vari-
ants exist, as for example the “four-corner” average method,
popular in some gold mines in Northern Nevada (Douglas
et al.
1994
), whereby the average of the four blast holes at the
corners is the block grade estimate.
Other commonly used methods include the nearest-
neighbor method and inverse-distance methods, implemented
in a number of variants. In all cases, the main characteristics
of the methods are that (a) a simple estimator is used to assign
grades to blocks, and (b) the blocks are relatively large with
respect to the average distance between sample points. The
second characteristic is unjustifiably common, and a major
source of inaccuracies, since the data density is generally
sufficient to justify much smaller blocks. Smaller blocks
would lead to better definitions of ore and waste boundaries.
13.3.2
Kriging-based Methods
Kriging-based grade control became popular in open pit
mines during the 1980s. Different types of kriging algo-
rithms were used, but most commonly ordinary and indicator
kriging were applied, for example in gold mines in Northern
Nevada.
In the case of ordinary kriging, the application of the method
is similar to those described as conventional methods above.
Ordinary kriging is used to provide an estimate of grades,
based on which the selection panels are drawn. The advan-
tages of kriging over other estimation methods were discussed
in Chap. 8 and include the minimization of the estimation
variance. In practice, kriging has been only marginally more
successful at grade control compared to conventional methods
because of the inherent smoothing and the use of inadequate
kriging plans. Also, the minimization of the estimation vari-
ance is not optimal for grade control (Srivastava
1987
).
13.3.1
Conventional Grade Control Methods
Conventional methods used for grade control include blast
hole averaging, inverse distance methods, and nearest-
neighbor-based methods. For the mathematical description
