Yes, my idea is to conduct interpolations along layers (well, performing a "tricky" 3D interpolation only to speed up the process).
Well, I'll already know that the shape and the range of the horizontal variograms along Z doesn't change too much. I have some
doubt about anisotropy ...but I think that there are too few samples on the horizontal plane to take seriously care about that....
Then if the possible anisotropy of horizontal variogram changes with depth we are in troubles......in the sense that I should calculate manually (or with some automatic algorithms) for each layers a variogram ...... and I can no more use the "tricky" 3D interpolation idea. So, your point about anisotropy is really important.
Bye
Sebastiano
At 14.33 28/08/2006, Bill Northrop wrote:
Hullo Sebastiano,
It sounds as if the Isobel's suggestion of a limited 3D search is the best solution.
The resultant models per layer should tell you if your approach has been correct, especially if you do a trial run with an anisotropic model and search first to see what spatial pattern you obtain.
Will be interested to know what you get.
Regards
Bill Northrop
- -----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 boundaries (or at least it seems so!) but there is a gradual and fast decrease in horizontal spatial variability going in depth.
- Sincerely
- Sebastiano
- At 12.48 28/08/2006, you wrote:
- Good morning Sebastiano,
- I found your problem interesting and I thought I would respond in this fashion. I have done quite a bit of research on similar layered databases on fluvial mineral deposits and found that if one did vertical (at right angles to the contacts of the layers) variograms on the raw data and obtained a variogram with no drift. then one could be sure that all these layers you have split your data have similar spatial characteristics. It would then not be necessary to examine the horizontal spatial characteristics of each individual layer, but rather have one standardized variogram for all of them. If the reverse is true ie drift in the vertical variogram, then one must look critically at the data for some phenominum on which one can subdivide. For instance in fluvial (river) deposits different material types, drastically different particle size etc according to what you are studying. I found generally that the lag distance at which the drift commenced was the width of the thinnest horizon in the case of two different populations, but it does not tell you whether it is the top or bottom layer. This must then be done by scrutinization of your data in the vetical plane. Once your data is split you can then do variography on each one of the two layers in the horizontal plane modelling the anistropy of the variance separately, This should only be done once you have again checked these two layers with vertical variograms for drift. If there are more than two populations present then the process can 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] 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 would like to use this transformation to deal with a 3D data set characterized by a peculiarity (well, this is quite common!) in the horizontal spatial variability.
- In particular if I divide the dataset in horizontal layers I see that horizontal variograms show a similar shape but with a re-scaled variance.
- So, my idea, in order to speed up the process of interpolation, consists to calculate the standardized anomaly for each layer and use the same calculated variogram (well, now it is a kind of standardized variogram calculated using all layers)) during interpolation with a 3D routine. Yes, in reality this is only a trick ...because I`m simply performing a series of 2D interpolations along layers. This because of, once the data have been transformed, it is not reasonable to use during interpolation samples coming from different horizontal layers.........
- Sincerely
- Sebastiano
- At 14.06 25/08/2006, Isobel Clark wrote:
- Sebastiano
- You will be fine so long as you actually have a "stationary" phenomenon. That is, there is a constant mean and standard deviation over your study area -- no trends, no discontinuities, no changes of behaviour. Such a transformation 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 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 the back-transformation of
- interpolated data and in the correct evaluation of estimation
- variance. Am I wrong?
- Sincerely
- Sebastiano
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