stephan80
04-30-2010, 09:49 AM
Hi,
I use the kriging algorithm, together with the simple power-model as a variogram, exactly as suggested in chapter 3.7.
I tried the algorithm with random data scattered on a [0,1]x[0,1] square (as x-data) and random y-data. It works really well!
However, in my actual application, my data is highly anisotropic, i.e. I have data scattered on a square [0,10000] x [0,0.1]. This will then cause the algorithm to give nonsensical interpolations and I can only assume that the problem lies in the isotropy assumption of the power-model in the variogram. In this model, it is silently assumed, that the variogram depends only on the distance |r| and not on the direction.
I wonder how I could change that. Did anyone else realize a problem there? and maybe a quick solution?
Thanks a lot,
Stephan
I use the kriging algorithm, together with the simple power-model as a variogram, exactly as suggested in chapter 3.7.
I tried the algorithm with random data scattered on a [0,1]x[0,1] square (as x-data) and random y-data. It works really well!
However, in my actual application, my data is highly anisotropic, i.e. I have data scattered on a square [0,10000] x [0,0.1]. This will then cause the algorithm to give nonsensical interpolations and I can only assume that the problem lies in the isotropy assumption of the power-model in the variogram. In this model, it is silently assumed, that the variogram depends only on the distance |r| and not on the direction.
I wonder how I could change that. Did anyone else realize a problem there? and maybe a quick solution?
Thanks a lot,
Stephan