Geoscience Reference
In-Depth Information
Fig. 13.12
A vertex is randomly
selected and moved, a new shape
obtained and new profit and
penalties obtained
week of work. The ore/waste selection process will normally
be based on a conventional method, perhaps some form of
Kriging duly restricted with geology. Blast hole sampling
should routinely provide acceptable samples for ore/waste
selection. Information from relevant prior blasts should
be used in defining current dig lines. Geologic mapping
should aid in the daily task of defining the dig lines, which
is generally a manual operation. Proper material accounting,
reconciliation procedures, and constant presence and con-
trol by the mine geologist in the field should minimize the
probability of making gross mistakes.
Good practice of medium- and short-term modeling
requires a well defined and consistent methodology for up-
dating the resource model, satisfying both the needs of short-
term mine planning department and the short-term prediction
of metallurgical performance. A sufficiently detailed study
would have determined all the important implementation
parameters and methodological details, including the proce-
dures required to update the geologic model. The short-term
models should be produced at regular time intervals, be al-
ways reconciled with recent past production, and compared
against the original long-term resource model for the same
areas. The model updating process should be semi-automat-
ic, although always fully validated. Good practice in ore/
waste selection requires the recognition of the limitation
of selecting on grade, and therefore the use of an optimal
selection method, with consideration of the basic economic
parameters. Dig lines are usually hand drawn, and control
and accounting procedures are strict. Reconciliation is usu-
ally kept on a blast-by-blast basis, and reported monthly.
Best practice in medium- and short-term modeling, in ad-
dition to the above, involves using conditional simulation
models to provide for the uncertainty model and the risk as-
sessment that short-term mine planners need. Other aspects
of the model updating should be similar to what is defined
as good practice, but the models are more likely to be sim-
ulation models. Similarly, the ore/waste selection should
have been fully optimized, including the possibility of
automatically drawing dig lines on a daily basis. In all cases,
reconciliation procedures should be in place, and should be
used to feed back and maintain an optimum implementation
of the method as mine conditions change.
In addition, best practice in long- and medium-term mod-
eling involves the development of dynamic models, which
are constantly updated, not only in terms of grade estimation,
but most importantly in terms of the geologic model. Pro-
duction data and infill drilling are used with production map-
ping (drift or bench) to update on a regular basis portions of
the long-term model that is therefore constantly up to date.
It amounts to merging the medium and long-term model into
a single model, updated, for example, on a monthly basis.
13.7
Exercises
The objective of this exercise is to review some concepts
related to grade control. Some specific (geo)statistical soft-
ware may be required. The functionality may be available
in different public domain or commercial software. Please
acquire the required software before beginning the exercise.
The data files are available for download from the author's
website—a search engine will reveal the location.
Consider the molybdenum data in
bh-data.dat
. You
will be asked to conduct a full geostatistical study from his-
tograms through simulation. The exercise will go quickly be-
cause the data are closely spaced and reasonably well behaved.
Question 1:
Plot a location map and histogram of the Mo
data. Comment on the spacing of the data. Your
inal estimation/simulation model should be at
a spacing of about 1/3 to 1/2 of the blasthole
spacing. We will not consider any volume aver-
aging in the simulation. Decluster the data if
you consider it necessary.
Question 2:
Calculate and it the variograms of the molyb-
denum grade and estimate a model with ordi-
nary kriging. Perform cross validation if time
permits and ensure that no conditional bias
exists in the estimates.
Question 3:
Calculate and it the variograms of the normal
scores transforms of molybdenum.
