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Re: AI-GEOSTATS: Variogram behaviour

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  • Digby Millikan
    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.


      [Non-text portions of this message have been removed]
    • Isobel Clark
      ... 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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