- Dear List:Got two questions about Kriging variance, If i have an area of interest, many known points and I want to use kriging to estimate grid points all over the area.1. Suppose stationarity, should kriging variance following a normal distribution? or should it be random?2. Besides configuration of known samples, what are the other factors that may affect the magnitude of kriging variance at a give location?Thank you very much

Feng Liu I think if your data is normally distributed, your kriging variance is normally distributed.

You will have higher kriging variances for sam ples which have higher variogram values

Digby

**From:**Feng Liu [mailto:asherliu@...]

**Sent:**Wednesday, 2 November 2005 4:32 AM

**To:**'AI Geostats mailing list'

**Subject:**[ai-geostats] Kriging VarianceDear List:

Got two questions about Kriging variance, If i have an area of interest, many known points and I want to use kriging to estimate grid points all over the area.

1. Suppose stationarity, should kriging variance following a normal distribution? or should it be random?

2. Besides configuration of known samples, what are the other factors that may affect the magnitude of kriging variance at a give location?

Thank you very much

Feng Liu- Dear List,

A comment for discussion:

In the worlds leading mailing list on Geostatistics, we should be more precise

in wording, especially in the answers given by the experts and experienced.

I will use a current example for explanation. However this is a critic remark

on a general tendency in the list, and not aimed to an individual:

Somebody wrote to the list:> I think if your data is normally distributed, your kriging variance is

This sentence is a loose explanation of a simple fact:

> normally distributed.

Universal/ordinary/simple kriging errors on Gaussian random fields are

normally distributed.

Hower taken in literal sense, the sentence is pure nonsense:

-> kriging variance is not distributed at all, since it is a moment (a

variance) and not a random variable.

Footnote: {Kriging variance might be seen as a random variable, when we

consider the randomness of the estimation of the variogram. However this is

almost never considered and probably not the intended meaning. Especially is

that distribution of these positive number not a Gaussian one and has never

been calculated analytically.}

-> It is the kriging error , which is the random and unkown difference

\hat{Z(x)}-Z(x) of the predicted and the true value, which is normally

distributed under some circumstances.

-> It is not the data to be normally distributed, but the random field. The

dataset can easily be not normally distributed (e.g. multimodal), even when

the underlying random field is Gaussian.

Footnote: {When loosely speaking about applied statistics, this loose wording

identifing the random variable and the data is quite common, since in the end

both follow more or less the same distribution in case of independent

sampling. However things simply get wrong in case of geostats.)}

I would like to encourage the list on discussion on that and ask for opinions.

Dialectic Discussion:

Clearly this is a one sided view of a mathematician. And one could say from a

newbee perspective: It is to hard to understand all these complicated exact

language. I would ask: Is it really more easy to understand objectively

wrong sentences in the right way, than to understand true but complicated

information. I would suspect that it only feels more simple, as long as you

do not realize the problem or as long you know the truth anyway.

Futhermore some of our list members will use the answers, not only those to

their own questions, in their seminar and thesis works and need literally

correct wording there.

Best regards,

Gerald v.d. Boogaart

PS:

I will try to contribute to the actual problem in my way:

Am Montag, 7. November 2005 06:49 wrote Digby Millikan:> I think if your data is normally distributed, your kriging variance is

If the field is jointly normally distributed (it is a Gaussian random field),

> normally distributed.

the kriging error (the difference between prediction and true value) is

normally distributed.

The kriging variance has a very complicated distribution, which is never

considered.>

This assertation does not hold in general.

> You will have higher kriging variances for samples, which have higher

> variogram values

The kriging variance has a complex dependence on the local geometry of

observations, the shape of the variogram and its sill.

Clearly, when all else is equal larger the scaled variograms lead to a scaled

kriging variance.

Very loose wording again: Samples i.e. observations have no variogram value at

all. Probably the sentence is speaking on geostatistical datasets, however

it does not mention the necessary conditions.

>If you had a set of blocks is it possible to add their kriging variances

No, it is not correct to just add kriging variances to get the kriging

>together,

>to get a standard error for the mine head grade, is this something that is

>done in practice or has been done in the past to estimate possible deviations

> from akriged head grade?

variance of the sum of the blocks, since the kriging errors are not

independent. The correct procedure would be to add all kriging variances and

all kriging covariances:

with kriging errors:

k_i=\hat{Z}_i- Z_i

it holds:

\var(\sum_i k_i) = \sum_i \sum_j \cov(k_i,k_j)

> From: Feng Liu [mailto:asherliu@...]

It is not random at all and has no distribution.

> Sent: Wednesday, 2 November 2005 4:32 AM

>

> Got two questions about Kriging variance, If i have an area of interest,

> many known points and I want to use kriging to estimate grid points all

> over the area.

>

> 1. Suppose stationarity, should kriging variance following a normal

> distribution? or should it be random?

The kriging variance itself has no probabilistic distribution, since it

depends functionally on the locations of the observations and on the

variogram. Asking for the distribution of the kriging variance is the same as

asking for the distribution of values of \sin(x+y) on some grid.

>

The kriging variance depends functionally on the configeration of known

>

> 2. Besides configuration of known samples, what are the other factors that

> may affect the magnitude of kriging variance at a give location?

samples and the variogram.

However the kriging error can depend on more. E.g. will the kriging variance

underestimate the true conditional variance of error in regions of high local

variability e.g. in regions of high ore grades.

>

Best regards,

> Thank you very much

>

>

> Feng Liu

Gerald v.d. Boogaart

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