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GEOSTATS: search radius question?

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  • Tom Charnock
    Hello I am investigating the use of geostatistical techniques for interpolating deposition of radioactive material following an accident. In such an event data
    Message 1 of 2 , Jun 10, 1998
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      Hello

      I am investigating the use of geostatistical techniques for
      interpolating deposition of radioactive material following an
      accident. In such an event data would be limited and so the
      experiments have been constrained to limited data.

      Initially I am trying ordinary kriging but will move on to more
      refined approaches. One problem is specifying an appropriate
      search radius and maximum/minimum number of points to include.

      As I see it there are arguments for and against a short seach
      radius:

      for:
      -allows local rescaling of the mean in ordinary kriging
      -doesn't require knowledge of the covariance at very large
      separations
      -reduces computational demands

      against
      -poor/patchy coverage of the final estimate surface
      -a datum may be beyond the search radius but it does provide
      information about the global but not local mean value

      I would be grateful for any advice or a rule of thumb for choosing
      an appropriate search radius for my purpose and advice on the effect
      of changing the minimum number of points that can be include in a
      kriging estimate.

      I will summarise replies.

      Tom

      Dr Tom Charnock
      National Radiological Protection Board
      Didcot, Chilton, Oxon, OX11 0RQ, UK.
      01235 822623
      Tom.Charnock@...
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    • Syed Abdul Rahman
      Some comments on Tom Charnock s queries: A) Generally, the rule of thumb is to limit the search neighborhood, to avoid stretching the stationarity assumption.
      Message 2 of 2 , Jun 12, 1998
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        Some comments on Tom Charnock's queries:

        A) Generally, the rule of thumb is to limit the search neighborhood,
        to avoid stretching the stationarity assumption. Of course, this is
        only fine and dandy if you have sufficient data. This could range
        from three to six wells in an offshore development to hundreds of
        thousands of points in a remote sensing problem.

        B) Consider that cross-validation can be used to fine tune
        search neighborhood parameters such as radii, points per
        quadrant, anisotropy ratios, and so forth.

        C) Limiting the number of CPU cycles can be a moot point with
        today's hardware capabilities. Also, techniques are available
        to increase computation speed, e.g. sparse matrix solvers in
        special kriging cases.

        D) Realistically speaking, 100 points is about the maximum for
        a problem involving a unique neighborhood (using all points
        instead of using a local neighborhood). Covariance matrix is
        inverted only once, and doesn't change from location to location.

        Regards,

        Syed


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