Hi all,
A few more references to the Covariance Vs. Semi-
variograme discussion:
To support Semi-variograme: Cressie N.A.C. (1993)
Statistics for spatial data. New York Wiley. ( Page 70-
73) I believe that the original discussion appears in:
Cressie A.C. Noel. and Grondona O. Martin (1992);
Hey Bill,
I'm going to have to take your geostats course, are you sure it's legal
Frank
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From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]]On
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Sent: Wednesday, May 23, 2001 2:55 PM
To: [EMAIL PROTECTED]
Subject: AI-GEOSTATS: entering the fr
Hi Pierre
Welcome back to the discussion.
I don't use simple kriging, except as a teaching
exercise on the way to ordinary kriging. However, it
is very commonly used in Africa and other parts of the
world. In particular, some major mining houses in
Southern Africa use a combination of the de Wij
What the h--- (this is more fun than jello wrestling):
Steve Zoraster wrote:
> 1) What manager in the mining or petroleum industry who has graduated
> from college hasn't taken a serious statistics course, including covariances
> and correlations?
Are we talking planet earth? As one that
Hi Steve
> 1)What manager in the mining or petroleum industry
> who has graduated
> from college hasn't taken a serious statistics
> course, including covariances and correlations?
Oh my, you obviously haven't visited many mines. I
can't speak for the petroleum engineers but few miners
(and eve
Hi guys,
I promised myself I would not waste more time
on this futile discussion about covariance and variogram,
but it seems that the discussion has drifted far away from
the initial comment by Isobel or that most people don't
remember what was the initial question.
Isobel's comment originated
1) What manager in the mining or petroleum industry who has graduated
from college hasn't taken a serious statistics course, including covariances
and correlations?
2) Surely when starting from scratch, educating someone about
geostatistics is more intuitive using covariances? (Just
Thank you, Marco!
My point exactly.
Isobel
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Dear Isobel:
In the case of intrinsic random functions the covariance (non ergodic covariance) and
the
correlogram are artifacts (see my example in ai-geostats).
I prefer to use the variogram. If you whish to have a symetric system, with no
problems of pivoting,
use the "Matheron System", and
>I think part of the difficulty in the semivariogram vs. covariance war
>is that modeling is subjective, and the notion of covariance has become
>more intuitive for statisticians, while the notion of semivariance has
>become more intuitive for geologists.
>
>Yetta
I'd go with that, speaking as a
Hi Yetta
Jump in, the water is lovely! All contributions
equally valid in my e-mail box ;-)
I have to confess that I have rarely used an unbounded
semi-variogram model. In mining applications, in my
experience (which is limited to 30 years in economic
mineralisations) semi-variograms which shoot
I'm hesitant to participate for fear of being jumped on, but
I think there is another aspect to the original question that
has not been addressed.
From a practical standpoint, we have complete discretion in choosing
a model. In theory, the properties of the sample do not inform the choice.
That
Dear Denis
I am sorry you think that I am being agressive. I
thought I was being quite reasonable, but perception
is a subjective thing. I think it is important for
readers of this list to understand that there are
different ways of coming to the same answer and that
there are different opinions
Excuse my persistence, but I think you are missing the
point here.
If you can produce a "covariance" function by
subtracting the semi-variogram from an arbitrary
constant AND if it makes no difference to the
resulting equations, you are simply constructing the
equations WITH the semi-variogram. N
> It is well known that when inverting a
> matrix it is much better (for numerical reasons)
> that the higher values
> are on the diagonal and the lower values far off the
> diagonal.
Have you not heard of "pivoting"?
The computational "problems" of using a matrix based
on the semi-variogram ra
> In 1971, I and 16 other people were taught in a short
> course at Fontainebleau given by Andre Journel and
> Charles Huijbregts, completely in terms of the
> semi-variogram with the covariance only being brought
> in as a special case when you could ensure
> stationarity of both mean and standar
Pierre Goovaerts writes:
> Hello,
>
> In fact, once the "pseudo-sill" A cancels out from
> the system of linear equations, the system is
> expressed in terms of semivariograms
> I use to think in terms of covariances since
> it's more intuitive, and the simple kriging
> system can on
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