Hello Peter
For that regard the definition of nugget effect you can read a good
explanation in statistics for spatial data(author Noel A. C. Cressie).
I think that you can find there all you need.
Sebastiano Trevisani
Ph.D. Padova University
I
At 01:48 PM 8/19/2003 +0200, Peter Pinn wrote
with almost
exhaustive data set but not when you have few data and the problem is
complex (often in geostatistics).
What do you think?
Thank you in advance
Sebastiano Trevisani
Ph.D Padova University Italy
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* As a general
weights in the kriging matrix (as I
can see looking at the debug file). Now I'm wondering how (if it is
possible) I can use this estimation variance.
Or better: in which way I can interpret this estimation variance?
Thank you in advance for your help...
Sebastiano Trevisani
Ph. D. student --Geology
Dear Isobel Clark
Thank you very for your help
I have used the software on your web site and I have understood more
precisely in which way kriging estimation variance works
Sincerely
Sebastiano Trevisani
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* As a general
geometrically discretize the
boreholes in data to use in simulation or estimation.
For example I divided a borehole of 10m in punctual values spaced every
25cmbut probably it is not the best approach.
Sincerely
Sebastiano Trevisani
for Natural Resources Evaluation chapter 6 could give you an
hand. Gstat code permit you easily to perform kriging with extrernal
drift.
Sincerely
Sebastiano Trevisani
At 04:35 PM 11/27/2003 +0100, Sigrun kvarno wrote:
Dear AI-GEOSTATS
members,
I tried co-kriging for the first time yesterday, and now I
) that you can not
one time calculate trend globally and the other time locally: it could happen
that globally you need a quadratic trend while locally a linear trend model is
enough. What about that?
Sincerely,
Sebastiano Trevisani
At 03.48 07/07/2005, [EMAIL PROTECTED] wrote:
Hi all
I may
that kriging is
wrong, geostatistics (or if you want spatial statistics...) will be
still alive...
and really useful.
Sincerely
Sebastiano Trevisani
At 19.13 01/06/2006, JW wrote:
Hello Tom,
Thanks for making my case against geostatistics even more
compelling. Just assume, krige, model, smooth
Dear list
I would like also to suggest another point:
really often there is a relationship between
topographic elevation and hydraulic head
data values; in this case an external drift approach could be useful.
Bye
Sebastiano
At 20.16 20/08/2006, Edzer J. Pebesma wrote:
Hi all,
I agree
Hi Bob
Thank you for the useful references
I forgot to mention the fact that sometime when you detrend hydraulic
head data using topography it could happen that the remaining
residuals show a pure nugget effect (i.e. all the spatial continuity
is described by the trend).
Then, (do you
Dear list member
A procedural question for you...
I'm thinking to transform my data in a standardized anomaly [i.e.
(raw datum- sample average)/sample standard deviation)] and then I`ll
perfom the geostatistical analysis on these transformed data. At
first glance, I don't see problem in
that your data follow a
fairly symmetrical histogram.
Your semi-variogram will look exaclty the same as your 'raw' data
semi-variogram but should have a sill around 1.
Isobel
http://www.kriging.com
Sebastiano Trevisani [EMAIL PROTECTED]
wrote:
Dear list member
A procedural question for you
Hi Isobel
Yes, the standardization is made for each layer separately (and so the
back transformation).
Actually I'm going to calculate the anomalies...and then let's see!
In this case I'm lucky because of the sampling along Z is regular.
Sincerely
Sebastiano
At 12.46 28/08/2006, Isobel Clark
be repeated until all your layers have
vertical variograms with no drift and therefore you have split your data
correctly.
Hope this helps
Regards
Bill Northrop
-Original Message-
From: [EMAIL PROTECTED]
[
mailto:[EMAIL PROTECTED]]On Behalf Of sebastiano
trevisani
Sent: Monday, August 28
.
Hope this helps
Regards
Bill Northrop
-Original Message-
From: [EMAIL PROTECTED] [
mailto:[EMAIL PROTECTED] Behalf Of sebastiano trevisani
Sent: Monday, August 28, 2006 9:57 AM
To: Isobel Clark
Cc: ai-geostats@jrc.it
Subject: Re: AI-GEOSTATS: Re: standardized anomaly
Hi Isobel
I
Dear list member
I' m wondering if in the case of negatively skewed data the rank
transformation is the best way to handle the problem.
As obvious my doubts are related to the back-transformation process.
Sebastiano
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+ To post a message to the list, send it to ai-geostats@jrc.it
+ To
Hi
Maybe you can give a look to the book :
Deutsch C V., Geostatistical Reservoir
Modeling: Oxford University Press, New York, 2002, 376 pp.
Sebastiano
At 15.55 16/01/2007, Rühaak, Wolfram wrote:
Dear all,
I would like to ask for some advice regarding a
simulation I am planning to compute.
Hi
From what I remember there are many works of Journel.
As a personal consideration, I Have the fealing that more the
statistical spatial index is complex, higher is the probability
that we met non stationarity conditionsand so the inference from
data become quite difficult!
Sebastiano
Hi
I'm not sure to understand what you mean when you say kriging on non
planar surface.
If you are saying that you need to interpolate on to an irregular
grid, both GSLIB and GSTAT (and so R) permit
you to interpolate whatever set of points.
Bye
Sebastiano T.
At 03.14 03/04/2007, Olumide
account for topography between the points). He
is currently on hoildays, but he may have ideas about that.
ciao, Peter
sebastiano trevisani [EMAIL PROTECTED] writes:
Hi
I'm not sure to understand what you mean when you say kriging on non
planar surface.
If you are saying that you need to interpolate
Hi
If you have many data (as I guess you have, given the way in which
you collected the data)
you can code the data by means of an indicator approach (as reported by Bob)
and apply a moving windows approach to calculate inside each window
the proportion
(well, it is like a probability of
Dear list member
I was working with gslib library and I had some troubles in trying to
interpolate with a linear variogram
(well, a power model with exponent = 1) and a nugget effect. In
particular both vmodel.exe as well as kt3d.exe don't
care about the value of the nugget effect imposed.
So
-com:office:smarttags /
UK you have to get the variogram of residuals!
It can be suspicious in some cases, then it is
preferable to use IRF-K approach.
Dr. Adrian Martínez Vargas
Departamento de Geología
ISMM, Las Coloradas s/n
Moa, Holguín
Cuba
CP 83329
-Original Message-
From: sebastiano trevisani
Dear Syed, I opted for this solution for several reasons:
1) the slope of the model follows an experimental variogram.
2) I can filter the nugget effect.
3) Also with a linear variogram Kriging gives weights to
data taking into account distance as well as clustering (from this
perspective is
or any information about the session. The details of the session are
attached below or follow the link:
http://meetingorganizer.copernicus.org/EGU2016/session/20486
Sincerely,
Sebastiano Trevisani
Session: SSS12.11/GM2.4
*Learning from spatial data: representation, inference and modelling in
earth an
ttp://meetingorganizer.copernicus.org/EGU2016/session/20486>
Thank for your patience and kind regards!
Sebastiano Trevisani
* Sebastiano Trevisani, Ph.D.*
*Assistant Professor*
*Applied and Environmental Geology*
*IUAV University of Venice: www.iuav.it <http://ww
,
The Conveners
* Sebastiano Trevisani, Ph.D.*
*Assistant Professor*
*Applied and Environmental Geology*
*IUAV University of Venice: www.iuav.it <http://www.iuav.it/>*
*Address: Dorsoduro 2206, Venice 30123, Italy Tel:+39. 041. 257
1299Mail:strevis...@iuav.it &l
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