Re: AI-GEOSTATS: Interpretation of nugget effect

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

Re: AI-GEOSTATS: robust kriging and indentifying outliers in soil data

You can also try to work with some transform of your data (logarithmic for example). And if you have many data you can try to use an indicator approach. The code SADA could be useful for study your data set. Sebastiano Trevisani -- * To post a message to the list, send it to [EMAIL

AI-GEOSTATS:

is method works well 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 -- * To post a message to the list, send it to [EMAIL PROTE

Re: AI-GEOSTATS: negative weight

Hi! There is something about negative weights in "geostatistics for natural Resources Evaluation" of Pierre Goovaerts. Also in Srivastava's "Applied geostatistics" you can find something of useful. Sebastiano At 05:29 PM 9/4/2003 +0200, [EMAIL PROTECTED] wrote: Hi Mailinglist!!! I have go

AI-GEOSTATS: Kt estimation variance

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 --Ge

Re: AI-GEOSTATS: Kt estimation variance

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 -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general

AI-GEOSTATS: source gslib f90

HI list members! Some one of you know if there is some way to get Gslib fortran 90 source code (not fortran 77)? Thank you in advance Bye Sebastiano Trevisani Padova University -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please

AI-GEOSTATS:

where to look thank you for your help! Sebastiano Trevisani -- * To post a message to the list, send it to [EMAIL PROTECTED] * As a general service to the users, please remember to post a summary of any useful responses to your questions. * To unsubscribe, send an email to [EMAIL PROTECTED]

AI-GEOSTATS: discretization of boreholes

reholes, I can 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 Sebas

Re: AI-GEOSTATS: co-kriging on data with spatial trends

c 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, an

Re: [ai-geostats] TIN, monitoring netwoks and density

Maybe, in order to have a quick and "safe" interpolation, the natural neighbor method of Dave Watson could perfom better than TIN. Sebastiano Trevisani Quoting Gregoire Dubois <[EMAIL PROTECTED]>: > Dear list, > > I would appreciate feedback on an issue that is clo

Re: [ai-geostats] Irf-k theory...

Well, Simone in my Ph.D thesis (available online, see geostatistics in other language on ai-geostats web site)I tried to explain it in a simple way... Bye S. Trevisani Scrive Simone Sammartino <[EMAIL PROTECTED]>: > Dear all > about Irf-k... > I can't understand the link between the two approach

RE: [ai-geostats] modelling trend and kriging type

) 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

Re: AI-GEOSTATS: Geostatistics: From Human Error to Scientific Fraud

case 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, mod

AI-GEOSTATS: objective analysis

Dear List Members I'm trying to find some paper explaining relationship and differences between classical geostatistical methodologies and objective analysis. I'm working with oceanographic data set (really complex: the data are 4D) it seem that in the oceanography world objective analysis

Re: AI-GEOSTATS: Detrending the data

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 with

Re: AI-GEOSTATS: Detrending the data

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 agree

AI-GEOSTATS: standardized anomaly

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 t

Re: AI-GEOSTATS: Re: standardized anomaly

ation also assumes 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 me

AI-GEOSTATS: Re: standardized anomaly

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 wrot

RE: AI-GEOSTATS: Re: standardized anomaly

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

RE: AI-GEOSTATS: Re: standardized anomaly

-Original Message- From: sebastiano trevisani [ mailto:[EMAIL PROTECTED]] Sent: Monday, August 28, 2006 2:06 PM To: Bill Northrop Cc: ai-geostats@jrc.it Subject: RE: AI-GEOSTATS: Re: standardized anomaly Hi Bill Thank you for your mail. In my case of study there are not sharp bounda

Re: AI-GEOSTATS: Re: standardized anomaly

e to be a party pooper... :-( So, after trying to spoil your joy, let me ask what about applying a logarithm, if the data are positive? and we may follow the discussion prompted by Gregoire ;-) Raimon En/na sebastiano trevisani ha escrit: > Hi Bill > Yes, my idea is to conduct interpola

AI-GEOSTATS: negatively-skewed data and rank transformation

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 + + To post a message to the list, send it to ai-geostats@jrc.it + To unsubsc

AI-GEOSTATS:

Dear list members A technical question. I got a problem using gstat inside R. When trying variogram calculation I got the following message: "dimensions do not match: locations 15882 and data 4749" At first glance this error seems related to the presence of missing values in my data (that

Re: AI-GEOSTATS: Simulation with hard and soft data

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

Re: AI-GEOSTATS: multi-point geostatistics

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 At

Re: AI-GEOSTATS: Kriging a non-planar surface lacking natural parametization e.g. meteorite

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 wr

Re: AI-GEOSTATS: Kriging a non-planar surface lacking natural parametization

which 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

Re: AI-GEOSTATS: bottom types interpolation

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 occur

AI-GEOSTATS: nugget effect plus linear model Gslib

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

Re: AI-GEOSTATS: nugget effect plus linear model Gslib

; /> 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 trev

Re: AI-GEOSTATS: nugget effect plus linear model Gslib

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 rea

AI-GEOSTATS: Call for abstract: EGU session: learning from spatial data

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

AI-GEOSTATS: Call for abstract: EGU session: learning from spatial data

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

AI-GEOSTATS: Reminder: EGU 2016, session: Learning from spatial data

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

AI-GEOSTATS: last reminder EGU session: Learning from spatial data: representation, inference and modelling in earth and soil sciences

follow the link: *http://meetingorganizer.copernicus.org/EGU2016/session/20486* <http://meetingorganizer.copernicus.org/EGU2016/session/20486> Thank for your patience and happy new year! * Sebastiano Trevisani, Ph.D.* *Assistant Professor* *Applied and Environmental G

AI-GEOSTATS: egu session: learning from spatial data

, 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