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Re: GEOSTATS: intro to splines

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  • Syed Abdul Rahman
    A couple of caveats: Before embarking on work using splines, or neural networks, etc, might be worth noting that all work on the same premise, i.e. an
    Message 1 of 4 , Oct 5, 1997
      A couple of caveats:

      Before embarking on work using splines, or
      neural networks, etc, might be worth noting that
      all work on the same premise, i.e. an underlying
      spatial correlation structure, and an interpolation
      component.

      Thin plate splines can be equivalent to same
      covariance models. Likewise radial basis function
      neural networks, which can be equivalent to
      kriging using a particular covariance model,
      depending on the radial basis function used.

      Unfortunately, splines and RBF networks are
      mostly packaged as black boxes (with some
      particular covariance model). Or, if "customization"
      is allowed, the spatial correlation structure can
      sometimes be modified, within certain limits,
      but it is still not as intuitive as graphical variogram
      modeling. The danger here is that the spatial
      analysis part -- the most important component
      in an interpolation exercise -- is relegated to the
      computer.

      Re: anisotropies:

      1) One can use the earlier suggestion, i.e. elliptical
      or ellipsoidal search neighborhoods when using
      splines or RBF networks, or

      2) Go back to the code, and modify the distance
      measures to include the anisotropy ratio, i.e.
      exagerating distances along directions of minimum
      continuity and compressing them along directions of
      maximum continuity.

      Note that a normal variography phase is still recommended
      to derive the anisotropy ratios in the first place.

      Regards,

      Syed

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