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AI-GEOSTATS: Kt estimation variance

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  • sebastiano trevisani
    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
      --Padova University --Italy



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    • Isobel Clark
      Sebastiano Large kriging variances have nothing to do with negative weights. The two are completely different phenomena - especially in Universal Kriging. In
      Message 2 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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