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434Re: AI-GEOSTATS: Declustering

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  • Isobel Clark
    Nov 15, 2001
      > I would like to know what is the declustering, why
      > we used this
      > method and how we proceed to decluster a set of
      > data?
      If your data is clustered spatially (in location) this
      may bias any histograms or probability plots which you
      draw and, therefore, any conclusions you make about
      what kind of distribution the values come from.

      For example, in mining projects geologists tend to
      drill a lot more holes in the good bits than the bad
      bits. This means that a histogram contains a lot more
      samples than it should in the higher end.

      If you try to fit a model to such data, or use a
      transform or 'anamorphosis' it will not really reflect
      the values in the whole of the area. Backtransforms
      will be biassed like the original samples.

      Declustering is one way to get rid of the bias. There
      are various ways to decluster but the most common ones
      revolve around laying a grid of squares over your map
      area and either (a) selecting one sample per square or
      (b) averaging all the samples in each square. (b) is
      not very sensible given what we are trying to do with
      the data, but is very common (again) in mining. If you
      use (a) it is a good idea to choose which sample to
      'keep' in the histogram at random.

      You may still use all of the clustered sampling for
      geostatistical analysis, of course. The semi-variogram
      and kriging techniques are not affected by clustering.
      In fact, one of the main reasons for inventing kriging
      was to make full use of every one of clustered and/or
      preferentially sited sampling.

      The term 'declustering' became popular around 1982/83
      and is first used widely in the proceedings of the
      geostatistical congress held at Lake Tahoe in
      September 1983.

      Isobel Clark

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