Re: AI-GEOSTATS: large dataset and variography...estimation...sim

[Ulrich Leopold]

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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Ulrich Leopold

Resource Centre for Environmental Technologies, Public Research Centre
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AI-GEOSTATS: Re: large dataset and variography...estimation...sim

[Isobel Clark]

Younes

You can try what we used to do in the bad old days when it took 20 minutes to 
calculate a semi-variogram on 1,000 samples -- moving windows.

Choose a sub-region size which includes about 1,000 samples. Calculate and 
graph from the samples in this window. Shift half-a-window in one direction. 
Repeat. Then display all of your graphs as a 'map' for each level. 

In 1981, I covered the floor of an empty meeting room with computer print out 
;-)

Thank god for graphics. This approach has the added advantage of being able to 
visually assess stationarity or lack-of. Only then should you consider 
modelling.

Isobel
http://www.kriging.com/shopping/EcoSSe_3D_details.htm



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