- 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.

[Non-text portions of this message have been removed] > ........... though with

Actually, a percentage error in your semi-variogram

> 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.

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 the last 30 years I have been advising people not

> 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.

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

I am not sure what you mean by this. Do you mean mixed

> estimating the mean of a multiguassian distribution.

Normals?

Isobel Clark

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