## Re: AI-GEOSTATS: Variogram behaviour

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• Hello, I have always lead the belief that it is worth while cleaning up a variogram, but with OK a poor variogram model for normally distributed data will give
Message 1 of 4 , Mar 16, 2001
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Hello,
I have always lead the belief that it is worth while cleaning up a variogram, but with OK a poor variogram
model for normally distributed data will give superior results than inverse distance methods, though with
lognormally distributed data fitting of a model to a poor variogram can result in highly erroneous estimates,
as for example the percentage error in your sill will result in an equal percentage error in your estimate.
Diverging from the topic a bit a method I preferred for lognormal data with poor variograms is to use inverse
distance or ordinary kriging with a topcut calculated as the topcut which will give you an arithmetic mean
equal to the sichel t estimator (an estimate of the true mean of a lognormal population). That way overestimation
due to lognormality is avoided without resorting to lognormal kriging. I beleive their may also be a method for
estimating the mean of a multiguassian distribution.
Regards Digby Millikan.

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• ... Actually, a percentage error in your semi-variogram sill will be an exponenial error in your final estimates. However, as with Normal distributions
Message 2 of 4 , Mar 16, 2001
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> ........... though with
> lognormally distributed data fitting of a model to a
> poor variogram can result in highly erroneous
> estimates,
> as for example the percentage error in your sill
> will result in an equal percentage error in your
> estimate.
Actually, a percentage error in your semi-variogram
sill will be an exponenial error in your final
estimates.

However, as with Normal distributions everywhere, your
total sill can be easily checked against your
estimated population variance.

> Diverging from the topic a bit a method I preferred
> for lognormal data with poor variograms is to use
> inverse distance or ordinary kriging with a topcut
> calculated as the topcut which will give you an
> arithmetic mean
> equal to the sichel t estimator (an estimate of the
> true mean of a lognormal population). That way
> overestimation
> due to lognormality is avoided without resorting to
> lognormal kriging.
For the last 30 years I have been advising people not
to apply top cuts because this is an avoidance of the
problem not a solution to it.

You can kill most exploration projects dead in one go
with this type of arbitrary rule. Of course, that is
always the safe way to go. If the project dies at
exploration stage, no-one ever knows what it would
really have been.

> I beleive their may also be a method for
> estimating the mean of a multiguassian distribution.
I am not sure what you mean by this. Do you mean mixed
Normals?

Isobel Clark

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