Well, as the author of the "green bible" I guess I should help out a little bit
here...
The key idea is that there exists an analytical expression that allows you to
compute a priori, for any threshold of a multigaussian random function,
the indicator semivariogram models. You only need to know the threshold
and the normal score semivariogram of the variable. Then, you just compare
the "expected" or "theoretically-derived" indicator semivariogram models
to the "empirical" or "derived from the data" ones.
Note that you don't even need to go through the burden of computing the
"theoretically-derived" indicator semivariogram models to know that the
underlying assumptions of the multigaussian model are not fulfilled.
In many situations, you will notice that your experimental indicator
semivariograms
are not symmetric with respect to the median; for example the 0.1 decile
semivariogram might have a longer range than the 0.9 decile semivariogram.
This happens frequently since the low background values tend to be better
connected in space than the high values...
The next question is "what do we do with that?"... or in other words "How do we
know that the differences between expected and empirical indicator
semivariograms
are significant". You could test it, but I don't think it's worth it in
practice...
Well, cross-validation has taught me that even if the indicator semivariograms
don't
look like expected under the multigaussian model, multigaussian kriging might
still give you better results than indicator kriging.. so it's hard to come up
with
"cast-in-stone" rules regarding the relative merits of parametric and
non-parametric
approaches.. but I am sure that everyone who has some experience with
geostatistics
has already realized that.. As I often say during my short-course,
geostatistics provides
you with a toolbox, and cross-validation and experience will teach you wich
tools
to use in any particular situation...
Cheers,
Pierre
-----Original Message-----
From: Perry Collier [mailto:[EMAIL PROTECTED]
Sent: Tue 3/22/2005 7:55 PM
To: ai-geostats@unil.ch
Cc:
Subject: [ai-geostats] bi-Gaussian assumption for non-mathematicians
Hi all from Oz (Australia)
First post on this list. I am a mine geo currently doing some
post-grad geostats study (Edith Cowan Uni in WA, hi Dr Ute, Prof. Lyn!).
Expanding on some very useful feedback from my Uni course director, I
would be interested in your learned "from the horse's mouth" comments (what,
why, how, when) regarding the bi-Gaussian assumption for Gaussian simulation
and the various means of checking it. I am slightly "mathematically
challenged", so if anyone could explain the whole thing without too much scary
maths, it would be much appreciated. I have Goovaerts' green geostats bible,
which is good stuff, but I'm trying to convert some of the maths to English.
Any comments from mining practitioners would be interesting...
Cheers
Perry Collier
Senior Mine Geologist
Ernest Henry Mine
Xstrata Copper Australia
Ph:(07) 4769 4527
Fax: (07) 4769 4555
E-mail: [EMAIL PROTECTED]
Web: http://www.xstrata.com
PO Box 527
Cloncurry QLD 4824
Australia
"I like rich people. I like the way they live. I like the way I live
when I'm with them..."
From Roger & Hammerstein's Sound of Music
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