Geoscience Reference
In-Depth Information
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Table 14.23 Variograms for the normal score of the PCA head grade variables. A nugget (C0) and two spherical structures (C1 and C2) were
used with no plunge angle
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14.7.6
Details of the Sequential Gaussian
Simulation
An overall summary of the transformations used is shown
below:
This methodology assumes that the normal score val-
ues of the principal components are independent. The PCA
transform ensures that the components are uncorrelated, but
they may not be independent. Poor histogram reproduction is
seen in original units due to this lack of independence. There
are a large number of head assay samples which makes the
input histogram reliable; they should be reproduced in the
simulation. To obtain reasonable histogram reproduction the
final simulations are post processed to better match the de-
clustered input histogram (Fig. 14.66 ). This has little effect
on the correlations between variables and individual variable
variograms, but improves histogram reproduction.
Implementation of SGS requires the use of variograms for
each PCA variable as well as a number of other important
parameters. For all variables considered in this case study,
simulation was performed with 50 nearby data (25 data and
25 previously simulated nodes). Parameters for each var-
iogram can be found in Table 14.23 . Because of the large
number of variables, variogram fitting software was used
with a visual assessment to locate any major inconsistencies
with data.
Declustering was used on the 23 PCA variables to obtain
global histograms. A locally varying mean was used in the
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