Hi Sven

If I understood well, practically you used ordinary kriging within strata in which a strata is a layer at a given depth. For example I used that techniques for 3D interpolation of pollution data (I made a paper in mathematical geology) as
well as for oceanographic data. Whenever you have layering in your phenomena
splitting a 3d problem in a series of 2d (or if you have a cross section from 2d to 1d) problem makes things easier to handle
and you have not to take care of trend along z.
Bye
Sebas

At 10.53 14/01/2010, Altfelder, Sven wrote:
Dear list,

I have point measurements of soil water tensions on an area of approx. 2m (width) x 0.8 m (depth) measured on a regular grid of 10 by 10 cm. In each of the 17 rows of this grid, 20 measurements were made. Every second row is shifted by 5 cm with regard to the previous row giving a chequerboard type pattern. My goal is to interpolate this data to a 5 cm grid, which pretty much means that I try to fill in the gaps.

The data has strong trend which is limited to the depth direction (driving forces are water movement under
gravity and plant uptake of water).

I had to take logarithms of the data because the original data variance is strongly dependent on the local mean.
After taking logs it looks fine (to me).
After doing this I calculated various variogramms which I fitted using gstat in the R environment. In log space I interpolated data using Universal Kriging (using a variogramm calculated perpendicular to the trend) with various polynomial trend functions in depth direction, Ordinary kriging, Ordinary kriging of residuals after trend removal and an addition of the trend component after kriging, Inverse distance weighting and finally Ordinary kriging using the variogramm calculated perpendicular to the trend and ignoring spatial correlation in depth direction by assuming an appropriate anisotropy.
(basically a 1D Kriging in X-direction).

I cross validated the various procedures on data sets that were actually measured on a 5 x 5 cm grid and were simply reduced to a 10 x 10 cm grid by leaving out every second data point.

I achieved by the best results with last method mentioned (Ordinary kriging
using the variogramm calculated perpendicular to the trend and ignoring spatial dependence in depth direction).
From a practitioners point of view I'm satisfied with the result.

However, after scanning the literature I've not found anybody who has done a 2-D interpolation this awkward way. This gives me the uneasy feeling that in a subsequent publication my approach will not stand up against the critical view of a real geostatistician. Is my approach suitable or should I further explore other methods?

Any advice on this issue would be appreciated.


Thanks,


Sven
+
+ To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu
+ To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and "unsubscribe ai-geostats" in the message body. DO NOT SEND Subscribe/Unsubscribe requests to the list + As a general service to list users, please remember to post a summary of any useful responses to your questions.
+ Support to the forum can be found at http://www.ai-geostats.org/


+
+ To post a message to the list, send it to ai-geost...@jrc.ec.europa.eu
+ To unsubscribe, send email to majordomo@ jrc.ec.europa.eu with no subject and 
"unsubscribe ai-geostats" in the message body. DO NOT SEND 
Subscribe/Unsubscribe requests to the list
+ As a general service to list users, please remember to post a summary of any 
useful responses to your questions.
+ Support to the forum can be found at http://www.ai-geostats.org/

Reply via email to