434Re: AI-GEOSTATS: Declustering
- Nov 15, 2001
> I would like to know what is the declustering, whyIf your data is clustered spatially (in location) this
> we used this
> method and how we proceed to decluster a set of
may bias any histograms or probability plots which you
draw and, therefore, any conclusions you make about
what kind of distribution the values come from.
For example, in mining projects geologists tend to
drill a lot more holes in the good bits than the bad
bits. This means that a histogram contains a lot more
samples than it should in the higher end.
If you try to fit a model to such data, or use a
transform or 'anamorphosis' it will not really reflect
the values in the whole of the area. Backtransforms
will be biassed like the original samples.
Declustering is one way to get rid of the bias. There
are various ways to decluster but the most common ones
revolve around laying a grid of squares over your map
area and either (a) selecting one sample per square or
(b) averaging all the samples in each square. (b) is
not very sensible given what we are trying to do with
the data, but is very common (again) in mining. If you
use (a) it is a good idea to choose which sample to
'keep' in the histogram at random.
You may still use all of the clustered sampling for
geostatistical analysis, of course. The semi-variogram
and kriging techniques are not affected by clustering.
In fact, one of the main reasons for inventing kriging
was to make full use of every one of clustered and/or
preferentially sited sampling.
The term 'declustering' became popular around 1982/83
and is first used widely in the proceedings of the
geostatistical congress held at Lake Tahoe in
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