Hi there,
Hoping you all doing very well.
Close to new year happy days I'm sincerely presenting my friends in
geostatistics community some software and tutorial gifts.
Hope you like them.
I have recently added some codes and tutorial of using Python and Matlab for
scientific analysis and
Hello Peter,
you can find Matlab code for empirical variogram estimation and
WLS-fitting in my spatial sampling design toolbox
http://www.uni-klu.ac.at/~guspoeck/spatDesign_V.1.0.0.zip
Moreover you will find there also functions for Box-Cox transformation
and variogram fitting and for Bayesian
I was wondering if you know of any code (ideally in MATLAB) able to do
fit a parametric variogram model by GLS.
Peter
you can find Matlab code for empirical variogram estimation and
WLS-fitting in my spatial sampling design toolbox
Gunter Spöck
Gunter,
thank you for the reply.
What I need
Hello,
I have a times series of precipitation and temperature data. I want to sum the
variograms to create one that is an average of the rest in terms of variance,
distance and points. I do not want to sum the last column of variogram because
it will give me NA such as this:
np dist
variable that is distributed NOT on a transect,
but rather at discontinuous (i.e. random) locations in a 2D study area?
Thank you,
Gustavo Vasques
--- Em qua, 11/3/09, seba sebastiano.trevis...@libero.it escreveu:
De: seba sebastiano.trevis...@libero.it
Assunto: AI-GEOSTATS: variogram
Hi Seba
It's truly hard to generalise, since the techniques to infer these
Hurst exponents (or fractal D) themselves are very sensitive to the
log-log slope. But you are correct some logs can, and do, exhibit
nested structures, which contradict the scale invariance that we are
assuming with an
Ciao Sebastiano,
I did work a bit on the issue with Peter (I guess he will get back to
you with even more references) and found the following papers useful:
Bruno, R., Raspa, G., 1989. Geostatistical characterization of
fractal models of surfaces. In: Armstrong, M. (Ed.),
Geostatistics. Kluwer
at
discontinuous (i.e. random) locations in a 2D study area?
Thank you,
Gustavo Vasques
--- Em qua, 11/3/09, seba sebastiano.trevis...@libero.it escreveu:
De: seba sebastiano.trevis...@libero.it
Assunto: AI-GEOSTATS: variogram and fractals
Para: ai-geostats@jrc.it
Data: Quarta-feira, 11
Jamina
Different software packages have different requirements for defining
anisotropy. Some will allow you to define completely a model for each major
axis of the anisotropy ellipse. The simplest (geometric anisotropy) just accept
anisotropy 'factors' for the range of influence.
In
Dear Mrs. Clark,
As you have mentioned,anisotropy is an important aspect in modeling.
For me one question always,is: although,we have usually rather strong
structures in vertical direction, the horizontal direction is very much vague
and with changing every graphical parameters or search
The general method is to try to apply the same sort of shape in each direction,
changing the range of influence for the different directions.
Have you tried looking at semi-variogram maps. These can often help with
anisotropy when individual directional semi-variograms are vague.
Hello,
I was wondering about some variogram models not having a
sill.
Does this mean that semi-variogram values can be greater
than
the population variance, in which case you would have
negative
covariances, or is the population variance the maximum these
models, e.g. linear model, may reach.
+
Charles
Image resolution has a major impact on range calculation. For an image
with fine resolution, spatial modeling implies resolving details at that
spatial scale. Take topograpy as an example, DEM resolution at the scale
of grain might resolve variability at that scale. DEM resolution at
Dear list
members,
Does anyone can help
me understanding variogram range interpretation or send me some specific
references.
I am currently
analyzing variograms computed over the same site of a remote sensing image. The
first variogram was derived from the original image and the second
First, the range of a variogram (if it actually has one) is the distance at
which the variogram value becomes constant with respect to lag distance (note
that a variogram with a geometric anisotropy will have a different range for
different directions. The constant value is the sill
Second,
Hi All,
I would like to use the Ramstein and Raffy (1989) algorithm
below for automatic extraction of variogram parameters (sill (C) and
range (a)).
My question is, if I have an image on whichI use a moving windows of 11 x 11 for instance,
is this algorithm mean that for each position of
Dear List members,
I have a querry for the variogram calculations and modelling.
Generally for the hydrological data sets specially hydraulic head,
transmissivity, etc we fit a spherical model. Why, we prefer spherical
model and not other models?
Thanks in advance to all of you
Regards
Rajni
* By
Linear model is also frequent in geostatistics
Adrian Martínez Vargas1
ISMM, las coloradas s/n
Moa, Holguín, Cuba.
-Original Message-
From: Rajni Gaur [EMAIL PROTECTED]
To: AI Geostats mailing list ai-geostats@unil.ch
Date: Mon, 19 Dec 2005 16:58:42 +0100
Subject: [ai-geostats] Variogram
Dear list,
I have a querry regarding the variogram calculations Which will help me in my research work.
while calculating the variogram we usually keep control over the function through lag and tolerance and we get the different number of pairs. Can we keep control over the number of PAIRS?
Hello:
Absolutely... It's not hard to develop an "Equal-N" algorithm so that each
lag contains the same number of pairs, instead of fitting all pairs within a
fixed range of a specific lag. The Equal-N lag distance will no longer be
constant, but the ability to visually interpret the
Dear all
Being a novice in the geostatistics field, I would like to know if there is
any software (I would prefer for free), that I could use in order to
calculate variogram values for every pixel of a raster image, within a
predefined kernel.
Thank you in advance
Giorgos Mallinis
Rajive
I haven't read the other responses yet, so this may be
redundant.
Two possibilities:
(1) anisotropy: if this is shallow marine data there
should be a difference between longshore drift and
off-shore deepening of sea-bed. You have an
omni-directional semi-variogram. It is possible that
Hole effect model, usually means your deposit has
alternating high and low grade zones. Sorry I'm not
familiar with the geology of this deposit but examples
of this could be pods of high grade spaced apart from
each other with waste or low grade halos between
them. If only the high grade zones
, December 07, 2004 4:50 PM
To: [EMAIL PROTECTED]
Subject: [ai-geostats] variogram analysis
My question is general. What do you conclude if your variogram is
wavy? Cyclic patterns? I have what appears to be high nugget,
followed by a wavy pattern.
If you wish, here is more info: an offshore placer
Scientist - Geochemistry Geoenvironmental DepartmentMACTEC Engineering and Consulting, Inc.Kennesaw, Georgia, USAOffice 770-421-3310Fax 770-421-3486Email [EMAIL PROTECTED] Web www.mactec.com-Original Message-From: Rajive Ganguli [mailto:[EMAIL PROTECTED] Sent: Tuesday, December 07, 2004 4:50 PMTo:
Dear Colleagues
I have a Digital Elevation Model with around 600,000 data points
(resolution=500 x 500 m). The x and y coordinates are in UTM and the z
coordinate are elevation from mean sea level.
Using GsLib to model the associated variogram, I am experiencing problem
in fixing the parameters
...
- Original Message -
From: M.J. Abedini [EMAIL PROTECTED]
To: Dr. M.J. Abedini from Civil Eng. [EMAIL PROTECTED]
Sent: Friday, February 20, 2004 2:35 PM
Subject: AI-GEOSTATS: Variogram modeling of DEM
Dear Colleagues
I have a Digital Elevation Model with around 600,000 data points
Geología
Moa, Holguín, Cuba
- Original Message -
From: [EMAIL PROTECTED]
To: [EMAIL PROTECTED]
Sent: Thursday, July 10, 2003 4:39 PM
Subject: AI-GEOSTATS: Variogram of large multivariate dataset
Hi all,
I have a multivariate (44 variables) dataset of 21,000 points. I would
like
: Friday, June 06, 2003 12:06 PM
Subject: AI-GEOSTATS: Variogram range physical meaning !
Hi All,
I got some results from a variogram analysis on two remote sensing images
with pixel size of 8m and 30m each. I would like to know the physical
meaning of the variogram range over a forest target
Hello All,
I'm looking some C codes to compute and fit (with spherical and exponential
models) experimental variograms.
I know that many software can do that, but I have more than 100 samples data
over which I have to generate the variograms and modeled them to find out
the range and sill values.
Dear List:
Thanks for all your answers, that are
summarized below.
Variowin 2.2 software computes an
indicates the goodness of the variogram fit by a weighted mean square
standardized (a dimensional) procedure; standardized by the variance of the
data and weighted by the number of pairs
Dear List:
Does anyone know which method of variogram fitting (goodness of the fit)
the software Variowin use when adjusting a variogram model? Is it minimum
weights mean squares errors technique? I don't have the guide of this
software so I don't know this specification.
Variowin 2.2. uses
Hi Carlo,
Note that a sill strongly different from 1 would
mean that the assumption of 2nd order stationarity
underlying SGS is not met.
I wouldn't force the sill to be one when
fitting the model and you have to use a unit sill
model in the simulation program for consistency.
If the deviation
Dear list members,
Following Ruben Roa's email, I would like
to add that I did not find any clear criterion to what is the minimum number of
data pairs that should be used in each lag and this would certainly affect the
variogram uncertainty. I know that a minimum of 30 data pairs is used
Hi list members
When there is a nugget effect say c0=45, there is
a discontinuity in variogram model, so that
gamma(0) = 0 , but
gamma(0.0001) = c0 (= 45),
I wrote a function for O. Kriging, but did not now
wheter i must define g(0)=0, or g(0)=c0 in the
kriging program??
For example
Jack
There is a schism in the geostatistical community
between those who do and those who don't make gamma(0)
equal to 0.
Some people argue that the nugget effect is 'sampling
error' and that gamma(0) should equal c0 so that
kriging does not honour the data values. It also makes
your kriging
)
Sent: Friday, March 16, 2001 8:29
AM
Subject: AI-GEOSTATS: Variogram
behaviour
I would (dangerously) suggest that in this early
lag period, choice of a variogram such as power
law, spherical, exponential, or Gaussian would be
nit picking. Unless the variogram clearly shows
Hello list,
I am dealing with heavy metals in upper soil layers of forests. Some
of my (indicator) variograms behave like a spherical model at the
beginning but in stead of reaching the sill they behave like a
power-model for greater distances. I tried
several power models but they didn't
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