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• ## AI-GEOSTATS: Kt estimation variance

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• Hello ai-geostats list-members Performing an universal kriging on chemical data (log transform of concentration), in some estimation points I obtained an
Message 1 of 2 , Oct 24, 2003
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Hello ai-geostats list-members

Performing an universal kriging on chemical data (log transform of
concentration), in some estimation points I obtained an estimation variance
larger (in some case three times) than the sill of residual variogram. From
what I know this is related to negative weights in the kriging matrix (as I
can see looking at the debug file). Now I'm wondering how (if it is
possible) I can use this estimation variance.
Or better: in which way I can interpret this estimation variance?
Thank you in advance for your help...

Sebastiano Trevisani

Ph. D. student --Geology Departement

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• Sebastiano Large kriging variances have nothing to do with negative weights. The two are completely different phenomena - especially in Universal Kriging. In
Message 1 of 2 , Oct 24, 2003
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Sebastiano

Large kriging variances have nothing to do with
negative weights. The two are completely different
phenomena - especially in Universal Kriging.

In Ordinary Kriging the estimation variance can become
as high as two times the total sill of the
semi-variogram. This is a theoretical fact(?) and you
can derive the answer by simply trying to estimate the
value at one point from a single sample at or beyond
the range of influence from this location. That is, a
weight of +1!!

In Universal Kriging, the weights often become
negative because the system is trying to force the
estimated point to lie on the trend of the samples.
Sometimes it can only do this by using negative
weights. There is no problem here, unless you have
extremely high 'erratic' residuals. In this case, you
should probably resolve that problem before trying
kriging.

If the variance becomes very high in Universal Kriging
it is probably because you are extrapolating into
sparsely sampled areas. Remember you are trying to
estimate the trend as well as the 'residual' value and
this contributes to higher variances. You should widen
your search radius to include more samples than with
Ordinary Kriging.

You can see how all this works with our free kriging
game. If your data is in Geo-EAS form or a simple CSV
file, you can read it into Krigame and see the
equations and the solutions. Then vary search
parameters etc to see how they are affected. You might
want to download our Tutorial 3 which discusses
Universal Kriging with the Wolfcamp data.

All available at
http://geoecosse.bizland.com/softwares with no
restriction on use or distribution.

Isobel

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