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-----Hi Isobel
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]On Behalf Of sebastiano trevisani
Sent: Monday, August 28, 2006 9:57 AM
To: Isobel Clark
Cc: [email protected]
Subject: Re: AI-GEOSTATS: Re: standardized anomaly
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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