Greg
The answers to your questions depend heavily on what sort of data you have and
what software you are using.
If you are using borehole or other drilling data, sections of core down a hole
will tend to get very similar weights. Most mining packages recommend
compositing up into lengths of core equivalent to your block (bench) height. In
this way you can effectively use more sampling but still have a reasonably
small number of equations to solve.
If you are working with other 3D sampling, for example fisheries or
meteorological data, which is irregular in 3D then the number of samples is
more sensitive.
There are many varied attitudes to negative weights, but they are usually the
computer's way of telling you to narrow your search ;-)
Most software packages have a limitation on the number of equations they can
solve and this will reflect the confidence of the programmer in the computer's
precision. It really has nothing to do with the kriging as such. We use a
maximum of 80, for example.
Personally, I do not use samples outside the range of influence unless I am
doing Universal Kriging or Kriging with external drift, where they are useful
in characterising the trend component.
If you have very sparse data, this can lead to strange artifacts as the search
sphere moves and single samples drop out and come in. This is not a fault of
the kriging, but of the paucity of your data -- a sign you need more samples,
in plainer talk! Smoothing these out by increasing your search radius can be
misleading since the map looks acceptable when it is actually very unreliable.
If you have very dense data, reduce your search radius down from the range of
influence. Otherwise you will use a lot of computer time just tracking down the
closest samples.
Hope this helps and look forward to other viewpoints. Happy New Year!
Isobel
http://www.kriging.com
--- On Wed, 7/1/09, Greg White gregwh...@inbox.com wrote:
From: Greg White gregwh...@inbox.com
Subject: AI-GEOSTATS: 3D Kriging neighborhood size
To: ai-geostats@jrc.it
Date: Wednesday, 7 January, 2009, 4:28 PM
All,
First of all a happy 2009 to everyone!
I have a few (beginner?) questions about the neighborhood
size (number of points) for Kriging, in particular in 3D:
1) Firstly, I would just like to hear some user experiences
- what number have you used in the past? Was that 3D? What
range of numbers would you normally test?
2) If I understand correctly, Kriging weights can become
negative, but I get the impression that normally the large
majority of the weights are positive. Could I therefore
assume that if I use 100 points, then the smallest weights
are likely to be (much) smaller than 0.01?
3) I understand that (except for simple Kriging), it can be
usefull to use a larger search neighborhood than the
variogram range. What about the opposite, if you have
relatively dense sampling, and there are many points within,
say, one tenth of the range?
Many thanks,
Greg
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