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GEOSTATS: Nugget Effect

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  • William C. Thayer
    My latest request for input concerns the calculation of the value of the variogram at h = 0. I have a highly skewed data set with very high short scale
    Message 1 of 2 , Jun 1, 1998
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      My latest request for input concerns the calculation of the value of the
      variogram at h = 0. I have a highly skewed data set with very high
      short scale variability. This has caused problems with obtaining a good
      estimate of the nugget effect, primarily due to 2-3 high values. The
      data set I am working with contains the analytical results for 25
      duplicate samples. (I have used the highest of the dupe data in the
      variogram calculations). I have used this data (in a spreadsheet) to
      estimate the variogram value at h = 0. I would like to receive feedback
      on this approach - Is it a valid approach? What other approaches can I
      take?
      Thanks,
      Bill Thayer

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    • srahman@lgc.com
      ... (a) For highly skewed data, variograms become extremely sensitive to those high (possibly outlier?) values. Variability might be dependent on location as
      Message 2 of 2 , Jun 1, 1998
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        Bill Thayer wrote:

        >My latest request for input concerns the calculation of the value of the
        >variogram at h = 0. I have a highly skewed data set with very high
        >short scale variability. This has caused problems with obtaining a good
        >estimate of the nugget effect, primarily due to 2-3 high values. The
        >data set I am working with contains the analytical results for 25
        >duplicate samples. (I have used the highest of the dupe data in the
        >variogram calculations). I have used this data (in a spreadsheet) to
        >estimate the variogram value at h = 0. I would like to receive feedback
        >on this approach - Is it a valid approach? What other approaches can I
        >take?

        (a) For highly skewed data, variograms become extremely sensitive
        to those high (possibly outlier?) values. Variability might be dependent
        on location as well (proportional effect). Consider: (1) applying a
        transform
        to the data to smooth out high values, e.g. lognormal, but be wary of
        the pros and cons of simply applying a backtransform on kriged or simulated
        values to get the original raw data (there were some exchanges on this
        subject a couple of weeks ago that you might want to skim through in
        the ai-geostats archives); or (2) applying some sort of "robust"
        variography,
        e.g. the family of general or pairwise relative variograms or nonergodic
        covariance measures to filter out the spatial dependence of variability.

        (b) Out of curiousity, why use the highest value among the duplicates?
        Why not the mean? Or the mode? Or the median?

        Regards,

        Syed


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