2126RE: [ai-geostats] Irf-k theory...
- Jun 30, 2005Ciao Simone,
I'm not sure I fully understand your question. Are you referring to the
monomials used to define the stationary trend model of the IRF-k?
What are these coefficients of 1 and -1 ?? I must admit I am very lazy
The fundamental differences between IRF-k and UK can be found in part 6
of a paper by Arianna Orasia
Some readings are:
1) the paper of D. Marcotte, M. David (1988)
Trend surface analysis as a special case of IRF-k kriging,
Mathematical Geology, 20(7): 821 - 824
2) the papers of van den Boogaart (hard reading !!!), e.g.
Why Universal Kriging is better than IRFk-Kriging: Estimation of
Variograms in the Presence of Trend
Karl Gerald van den Boogaart& Alexander Brenning
June 30, 2001
3) If you survived the reading above, then you can always go for a paper
from Alexander Brenning:
4) This one should saves your day (in Italian and easy reading) but it
is probably impossible to find today:
Bruno R. & Raspa G. (1994). La pratica della geostatistica lineare. Il
dei dati spaziali. Guerini Studio. 170 p
The book is all about FAIK (Italian equivalent to IRFk) and has (had?)
an MS DOS software for FAIK, FAIPACK, that was not bad at all.
There was also a wonderful text from Chauvet that I could try to find
again if needed but it is in French (try to contact Fontainebleau).
Chiles and Delfiner probably discuss this as well in their book but I
don't have it here.
For what concerns the previous mail regarding trend modelling, IRF-k can
be nicely automated for detecting the optimal "k" but I would make the
same critic as for the Neural Nets, you do not extract any useful
information from the drift (although you could map the local values of
k.. allowing you so to describe somehow the complexity of the underlying
trend.. Don't know if this has been done or any useful).
PS: there are some nice papers from Chauvet (Fontainebleau) that I could
try to find again if needed but there are in French.
Chiles and Delfiner probably discuss this in their book but I don't have
From: Simone Sammartino [mailto:marenostrum@...]
Sent: 29 June 2005 17:49
To: Geostat newsgroup
Subject: [ai-geostats] Irf-k theory...
I can't understand the link between the two approach explaining the
Irf-k theory... 1st approach: The Irf-k approach is based on a
generalization of the intrinsic model, in which the coefficients of the
combination of the variables in the X points are such that the
combination of monomials of the variables (computed with such
coefficients) is zero mean and second order stationary. In the intrinsic
model the coefficients are 1 and -1 and the linear combination of the
variables with such coefficients (the increment) is second order
stationary. Now...how to insert the generalized covariance in this
speech?... 2nd approach:
The non stationary random function is seen as the sum of a trend and a
residual (as in UK?!)...such trend is the linear combination of
monomials of the variables (as in UK?!) with the said coefficients. And
the generalized covariance? Reassuming...I did not understand the Irf-K
Someone can explain it to me? or suggest some clear refer? Thank you
Dr. Simone Sammartino
- Geostatistical analyst
- G.I.S. mapping
I.A.M.C. - C.N.R.
Port of Naples - Naples
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