Els,
Which GSLIB program are you using? When you refer to the practical range
a and 3a this is normally associated with the exponential model, the GSLIB
programs appear to be setup to use spherical model parameters, so you
should fit spherical models to your data, in the case of fitting a spherical model
to your variogram (you can fit nested models if you are not happy with a single
structure spherical model) the input to the programs, e.g. in the case of kt3d,
spherical models only have one range i.e. a.
For the search radius most will tell you the range, but it really depends on how
much confidence you are prepared to have for your estimates, you can extend
your search radius further than your range, it's just that those points estimated
which use values greater than the range distance will have a lower confidence.
You can even have points estimated which only use data at distances greater
than the range, in which case these estimates will have a low confidence. It
depends on how desperate you are to get estimates into data points on how
far you extend the search radius beyond the range. In mining we classify all
estimates with a confidence, either by associating it with the search radius that
was allowed for a data point e.g. a geologist from visual assesment of the
continuity of the geology of an ore zone may draw a polygon extending 10m
either side of the drillhole, and say in that case that everything in that polygon
may fall into the Joint Ore Reserves Committes code as the classification as
"Measured which means the entire polygon has the go ahead for mining
depending on its economics, in which case the search radius would be extended
beyond the range, if necessary, so that all blocks within that polygon get filled
with grade. Note also that the confidence of the estimate at each point is
provided by the kriging variance, so if you do extend the search radius
beyond the range, you will have the kriging variance as another method of
classifying the resource.
i.e. You don't have to limit your search radius to the range, it's just that
estimates based on samples using some data greater than the range will have
a lower confidence, indicated by the kriging variance, which in some cases
may be better than having no estimate.
Regards Digby
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