Re: GEOSTATS: intro to splines
- 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
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
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
Note that a normal variography phase is still recommended
to derive the anisotropy ratios in the first place.
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