I would like to use this transformation to deal with a 3D data set characterized by a peculiarity (well, this is quite common!) in the horizontal spatial variability.
In particular if I divide the dataset in horizontal layers I see that horizontal variograms show a similar shape but with a re-scaled variance.
So, my idea, in order to speed up the process of interpolation, consists to calculate the standardized anomaly for each layer and use the same calculated variogram (well, now it is a kind of standardized variogram calculated using all layers)) during interpolation with a 3D routine. Yes, in reality this is only a trick ...because I`m simply performing a series of 2D interpolations along layers. This because of, once the data have been transformed, it is not reasonable to use during interpolation samples coming from different horizontal layers.........
Sincerely
Sebastiano
At 14.06 25/08/2006, Isobel Clark wrote:
Sebastiano
You will be fine so long as you actually have a "stationary" phenomenon. That is, there is a constant mean and standard deviation over your study area -- no trends, no discontinuities, no changes of behaviour. Such a transformation also assumes that your data follow a fairly symmetrical histogram.
Your semi-variogram will look exaclty the same as your 'raw' data semi-variogram but should have a sill around 1.
Isobel
http://www.kriging.com
Sebastiano Trevisani <[EMAIL PROTECTED]> wrote:
- Dear list member
- A procedural question for you.......
- I'm thinking to transform my data in a standardized anomaly [i.e.
- (raw datum- sample average)/sample standard deviation)] and then I`ll
- perfom the geostatistical analysis on these transformed data. At
- first glance, I don't see problem in the back-transformation of
- interpolated data and in the correct evaluation of estimation
- variance. Am I wrong?
- Sincerely
- Sebastiano
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