[ai-geostats] 3D cross-variograms fittind
- View SourceDear list members
Despite free opensource software (for example Gstat) could perform full
indicator Kriging (i.e. using cross variogram) I have not
found free software for fitting 3D cross variograms. Surely, commercial
software can do that, but it could
be a little bit expensive from the economic point of view !
Do you know any opensource software that performs fitting of a 3D
Or, have you an idea where to start in order to write a code to do that?
Let me know if you know some opensource software (better in C++, but also
in fortran 90 or c) that could be a starting point.
Thank you in advance for your help
- View SourceHi Luke
Yes, as you suggest very often in geology the third dimension (say z) is
more similar to a temporal dimension than to a spatial one.
(Think for example to quaternary sedimentary deposits). So is it so a big
deal to preform a 3d analisys? isn't it better to conduct
a stratified 2D analysis?
For this reason I`m glad if someone has some idea also only for 2D
At 12.19 01/03/2006, Luke Spadavecchia wrote:
>I have found similar problems. What kind of 3D model are you trying to
>fit? If it is a separable covariance model (of the product or product-sum)
>variety, a good place to start is the modified GSLIB code that appeared in
>the attached article. The code can be downloaded from the computers and
>geosciences website. The code will compute the empirical semivariance
>surface for you, but there is no fitting routine. I am about to embark on
>a major programming task (when I get the time) to fit a variogram surface
>to the output of GAMSURF, but in the meantime, I just fit my model in
>Excel, or some other spreadsheet. I have attached an example for you:
>although it is spatio-temporal, the same model is permissable in 3D space.
>Any nested variograms permitted in 2D can be used for the separable
>models. This is actually quite appealing, given the nature of the
>covariance in the vertical may be different in functional form from the
>horizontal covariance, since different processes may underlie the
>Hope that is helpful