Hi Younes,
maybe take a (stratified) random sample of your data to reduce the
computational time for the variography.
For predictions you might want to use a local search neighbourhood if
that is a feasible approach for your problem.
Ulrich
Younes Fadakar wrote:
Hi everybody,
G'day.
How can we deal with large data, say, 200,000 samples in 3D?
- to doing variograpgy
- to estimate using kriging.
issue: it takes over 30 minutes for one omnidirectional variogram!
note: for whom recommending declustering, I think declustering may not be
useful since it changes the question.
Regards,
Younes
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