AI-GEOSTATS: Re: 3D Kriging neighborhood size
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!
--- On Wed, 7/1/09, Greg White <gregwhite@...> wrote:
> From: Greg White <gregwhite@...>
> Subject: AI-GEOSTATS: 3D Kriging neighborhood size
> To: ai-geostats@...
> Date: Wednesday, 7 January, 2009, 4:28 PM
> 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,
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