> ........... though with
> lognormally distributed data fitting of a model to a
> poor variogram can result in highly erroneous
> as for example the percentage error in your sill
> will result in an equal percentage error in your
Actually, a percentage error in your semi-variogram
sill will be an exponenial error in your final
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
> 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
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