Hi Dr Goovaerts,
> It is not clear what you want to do with the kriging variance you obtain...
> Probably you want to quantify the degree of reliability of the allocation
> of a particular location to a given facies. This could be measured by the
> variance
> or entropy of the distribution of probabilities of occurrence of facies at
> that
> location, see my book page 354. This probability distribution is easily
> computed
> by indicator kriging or you can use truncated Gaussian simulation if there is
> any physical ordering of your facies.
I'm trying to get a measure of the smoothing effect related to a
particular algorithm (truncated kriging, truncated inverse square
distance, indicator kriging,...) and a particular algorithm set up
(searching conditions or number of neighbours used to obtain each facies
estimate), applied to interpolate facies distribution in a dense coal
mine dataset.
A good measure would be the variance of the estimated property, but
since I am working with a categorical property (i.e. facies), it is not
direct to get this variance (one must assume a certain facies ordering
and attribute values to facies, and I'm not sure which would be the
effect of this assumptions int the variance of measures). And therefore
I was looking for other options like kriging estimation variance.
>
> For your last question, look at Journel and Huijbregts "Mining Geostatistics"
> page 451 for the "smoothing relations" that link the average kriging variance
> to the
> variance of observations and the variance of kriging estimates.
thank you, I will take a look to this.
Oriol
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