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method are then estimated into blocks with exactly the same search criteria, method and
variogram parameters as those used to estimate the grade values. This insures that the kriging
weights used for the estimation of both the grade values and the declustering values are the
same. Declustering weight contour maps should be used to help select threshold declustering
weights to use for classification
Table 1 indicates a step-by-step procedure. The technique can be easily integrated into
most modern mining software’s estimation procedures.
4 CONCEPT
To help clarify how the declustering method works, a simplified two-dimensional base grid of
was generated. The base is divided into a series of 25 × 25 unit blocks in size to be estimated
by a pattern of sample locations. These sample locations are shown as colored dots in a
radially decreasing density, for this example, an isotropic variogram of 100 units is assumed.
Shown in Figure 3. Figure 4 shows the model with the decluster weights of each sample
Table 1. Declustering methodology.
Steps
Task description
1
Separate the data into estimation domains as in any estimation.
2
Calculate domain statistics.
3
Run the declustering algorithm. For an iterative algorithm, check the domain statistics
with the declustering weights.
4
Perform variography in each domain.
5
Develop estimation parameters.
6
Estimate the grade values.
7
Estimate the declustering weight values with the same search and variogram as the
prime variable.
8
Map (or contour) the modeled declustering weights against the sample locations to
compare average sample spacing with the weights.
9
Select threshold declustering weights to use for classification.
10
Use results to assist in classifying estimated resources into measured, indicated and
inferred.
Figure 3. Conceptual sampling grid.
Figure 4. Estimated declustering weights.
• •
0
•
•
• • • • •
•
•
0 .
0
• • • • •
•
0
0
•
•
•
• • • • •
. . . . . .....
•
•
0
• •
0
• • • • •
•
•
•
•
0
• • • • •
. . . . . . .... .
. . . . . .... .
.. .... ....
I
I
;
. -·
·-·
·-·
-·
method are then estimated into blocks with exactly the same search criteria, method and
variogram parameters as those used to estimate the grade values. This insures that the kriging
weights used for the estimation of both the grade values and the declustering values are the
same. Declustering weight contour maps should be used to help select threshold declustering
weights to use for classification
Table 1 indicates a step-by-step procedure. The technique can be easily integrated into
most modern mining software’s estimation procedures.
4 CONCEPT
To help clarify how the declustering method works, a simplified two-dimensional base grid of
was generated. The base is divided into a series of 25 × 25 unit blocks in size to be estimated
by a pattern of sample locations. These sample locations are shown as colored dots in a
radially decreasing density, for this example, an isotropic variogram of 100 units is assumed.
Shown in Figure 3. Figure 4 shows the model with the decluster weights of each sample
Table 1. Declustering methodology.
Steps
Task description
1
Separate the data into estimation domains as in any estimation.
2
Calculate domain statistics.
3
Run the declustering algorithm. For an iterative algorithm, check the domain statistics
with the declustering weights.
4
Perform variography in each domain.
5
Develop estimation parameters.
6
Estimate the grade values.
7
Estimate the declustering weight values with the same search and variogram as the
prime variable.
8
Map (or contour) the modeled declustering weights against the sample locations to
compare average sample spacing with the weights.
9
Select threshold declustering weights to use for classification.
10
Use results to assist in classifying estimated resources into measured, indicated and
inferred.
Figure 3. Conceptual sampling grid.
Figure 4. Estimated declustering weights.
• •
0
•
•
• • • • •
•
•
0 .
0
• • • • •
•
0
0
•
•
•
• • • • •
. . . . . .....
•
•
0
• •
0
• • • • •
•
•
•
•
0
• • • • •
. . . . . . .... .
. . . . . .... .
.. .... ....
I
I
;
. -·
·-·
·-·
-·