Re: [ai-geostats] kriging proportions
You can try our recent publication in the Journal "Soil Science" which compared several geostatistical techniques for simultaneously interpolating soil particle-size fractions while ensuring summation to a constant. The reference is:
Odeh IOA Todd AJ and Triantafilis J 2003. Spatial prediction of particle size fractions as compositional data. Soil Science 168, 501-515.
At 01:01 PM 10/06/2004 -0400, Marc-Olivier Gasser wrote:
Lets say we have measured three soil particule size values for clay, silt and sand, all adding to one.
cl + s i + sa = 1
What would be the best way to take into account each particule size, so the interpolated values still add up to one?
Is there any geostatistical process that can handle this?
I have tried interpolating parameters caracterising different particle size distribution functions (in the case where there are more than 10 particule sizes) but this adds errors to the modelling and some parameters don't necessarily exhibit spatial correlation.
Maximum autocorrelation factor kriging has been suggested such as in:
A. J. Desbarats and R. Dimitrakopoulos. Geostatistical Simulation of Regionalized Pore-Size Distributions Using Min/Max Autocorrelation Factors. Mathematical Geology, Vol. 32, No. 8, 2000.
but I haven't found many statistical packages implementig this procedure.
What other possibilities are there?
* By using the ai-geostats mailing list you agree to follow its rules
( see http://www.ai-geostats.org/help_ai-geostats.htm )
* To unsubscribe to ai-geostats, send the following in the subject or in the body (plain text format) of an email message to sympa@...