Hello list, My original question was if anyone had opions on the use of
using classes of a continuous variable, not thresholds for indicator
Respones are as follows:
Pierre Goovaerts, as helpful as ever, suggested:
Benamghar and Sonnet. 1999. Performance comparison of cumulative and class
indicators approaches for pollution risk assessment. In
J.~G\'omez-Hern\'andez, A.~Soares, and R.~Froidevaux, editors, geoENV II -
Geostatistics for Environmental Applications, pages 357-368. Kluwer Academic
Wingle, W.L., and E.P. Poeter, 1998, Classes vs. Thresholds: A Modification
to Traditional Indicator Simulation, Advances in Geostatistics, 1998 AAPG
Annual Meeting, Salt Lake City, Utah, May 17-20, 1998, which is available at
The only potential drawback with class indicators is that the experimental
semivariograms will tend to be more erratic than for cumulative indicators
because they will be based on a smaller proportions of non-zero data. For
example, if you use decile thresholds to define 10 classes, each class
indicator variable will consist of 10% of 1 and 90% of 0, while for
cumulative indicators this ratio will be observed only for the first and
last threshold. It's the reason why the semivariogram for a median threshold
(50% of 1 and 50% of 0) is usually one of the best behaved.
Simon Kelly suggested:
Our software (Gemcom mining and geological software - www.gemcomsoftware.com
) performs MIK (Multiple Indicator Kriging) in the fashion you describe and
gives an estimate and associated probability for each grade bin for each
cell. However, the overall grade is not given by the grade with the highest
probability. The overall grade is the sum of the mean (median in the top
bin?) of the bin multiplied by the probability of achieving that grade.
Another way to see the indicator probabilities is as proportions of
different grade bins in each block. I hope I've understood you and that you
Bob Sandefur suggested:
Traditional IK (indicator kriging) will give delta classes if you subtract
the kriged indicators one from another ie prob(x>=0.1 and
I think (but don't know) that the experimental variograms of your suggested
intervals will be harder to interpret that i traditional IK variograms (I
suspect they will look like white noise variograms (ie flat)). Like in IK
where they check order relations you will have to check that the sum of your
indicators=1 (they won't exactly unless all your classes have same
I never heard of it until now but its worth a try (I would assume the early
IK people tried this and found it was harder that traditional IK) but in
mining where IK started we're usually interest in totals above a cutoff not
amount between cutoffs.
Thank you all very much. A very useful webpage about indicator kriging and
UNCERT software is : http://uncert.mines.edu/sisim/sisim.html
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