Hi Isobel
I didn't know the existence of the two schools of thought! So thanks
for the clarification.
The point is the I interpret smoothing (filtering) properties of
kriging by means of the dual representation of kriging
interpolator given for example in Goovaerts's book Geostatistics for
Sebastiano, exactly.
So there are 2 sources of smoothing:
1) the nugget, whose purpose in interpolation is to account for unresolved
(by the sampling scheme) variability and / or data uncertainty, which
means, in effect, smoothing. If for some reason one does not want this,
simply set it to zero
A strong nugget may be the exception rather than the norm for thickness
data. You can try cross-validating kriged thickness results based on some a
priori variogram model to see whether your estimates of thicknesses can be
improved using a spatial correlation model.
Syed
On 2/20/08, Peter
Hi
Well, some time you have the impression that kriging is not an exact
interpolator because of
you have a high nugget effect and the interpolation grid nodes have
not the same location
of available data. The variability represented by the nugget effect
is filtered every time
an
Isobel,
thanks for clarification.
As to (1) vs. (2): I think it really depends on the physical nature of the
variable which one tries to model. If you have exact data (i.e.
intrinsic + longitudinal uncertainty very small compared to value or
abs(value)) == (1)
If you have intrinsic =
Dear list,
I'm graduate student in hydrogeology, I've to spatialize data of
reservoir thickness, and I need to achieve a map having exactly the
sampled value in the sampled localization (piezometers). I've little
experience in geostatatistics.
I had a look at kriging algorithms, but I did
Andrea
In theory kriging will honour the sample values provided your semi-variogram
model takes the value zero at zero distance.
Whether the data are honoured or not depends on which computer package you
use and what it does with the semi-variogram at zero. You can force this
