Hi Younes,
some brief ideas below.
Younes Fadakar wrote:
Ulrich said (suggested):
1- to take a (stratified) random sample of your data to reduce the computational time for the variography.
2- to use a local search neighborhood.
/// summary: random selection strategy
am interested in this idea. Since any decreasing in data has dramatically positive effects on computation cost (time).
Is anyone aware of this methodology has been published somewhere?
/// liked it and will try.
Vesper has a built in sub-sampling algorithm and can do local variogram
computation as well as local kriging. Not sure about 3D.
http://www.usyd.edu.au/agriculture/acpa/software/vesper.shtml
Gstat stand-alone has the options "every = value" for every nth
selection of regular sampling records and "prob = value" for random
sampling.
(www.gstat.org -> see manual)
Maybe have also a look to the sp package in R
(http://r-spatial.sourceforge.net). See function spsample().
Sampling algorithms for sampling designs are explained in e.g.
de Gruijter,J., Brus,D., Bierkens,M., Knotters,M. 2006. Sampling for
natural ressource monitoring. Springer.
Ulrich
______________________________________________________________________
Ulrich Leopold
Resource Centre for Environmental Technologies, Public Research Centre
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144, L-4002 Esch-sur-Alzette, Luxembourg
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