Re: AI-GEOSTATS: kriging variance and accuracy

[Gustavo G. Pilger]

Hi,

Indeed the kriging variance is only semi-variogram and spatial data  
configuration dependent. The kriging variance is calculated taking  
into account only the geometry of the samples, i.e. their spatial  
arrangement and the semi-variogram. Basically, kriging variance do not  
take into account the value of the samples, but only their location  
(and the semi-variogram), consequently ignoring the local variability.  
Therefore this parameter is not appropriate to measure uncertainty.  
For this purpose you should consider the use of conditional simulation  
methods.

I wrote some papers about this subject some years ago. For exemple:

PILGER, Gustavo G.; COSTA, Joao Felipe Coimbra Leite; KOPPE, Jair  
Carlos, 2001.  Additional Samples: Where they Should be Located?.  
Natural Resources Research, New York, v. 10, n. 3, p. 197-207.

I can send you a copy if you wish.

I hope this helps you.

Cheers.
<><><><><><><><><><><><><><><><><><><><><><><>
Gustavo G. Pilger, Mining Engineer, MSc, PhD
Senior Geostatistician
MBR - Brazil
<><><><><><><><><><><><><><><><><><><><><><><>



Hi.  I just read through Journel and Rossi's 1999 paper, "When do we
need a trend model in Kriging".  In the appendix they say "A kriging
variance is but a variogram-model dependent ranking of data
configurations; being data-value  independent, it is generally not a
measure of local accuracy...This fact is unfortunately not yet fully
appreciated by some practitioners".  Can someone explain the
implications of this in terms of determining cost-efficiency analysis
for sample designs?  Specifically, can we use kriging variance
estimates across potential sampling grids, (from modeled variograms
estimated from say a pilot study) to estimate the variability
associated with different sampling densities/configurations.  In
addition, can someone provide some references that address this topic.

Thanks,
Kerry
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