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RE: [ai-geostats] practical range vs range

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  • Colin Badenhorst
    Hi Els, To add to Digby s comments. For a given block you can run several estimates, each with a different search ellipsoid dimension. In my case, I tend to
    Message 1 of 5 , Mar 23, 2005
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      Hi Els,

      To add to Digby's comments.

      For a given block you can run several estimates, each with a different
      search ellipsoid dimension. In my case, I tend to run 3 estimates (passes)
      for each block being estimated:

      1st pass = 0.5 x range in the XYZ dimension
      2nd pass = 1.0 x range in the XYZ dimension
      3rd pass = 2.0 x range in the XYZ dimension

      I have higher confidence in the 1st pass, moderate confidence in the 2nd
      pass, and lower confidence in the 3rd pass.

      I am sure that many people have several variants of this - there are no hard
      rules here, simply what suites your data and its setting. I would however
      caution you not to make your sample search ellipsoid dimensions too large. I
      have seen a few instances where because of this, samples located near the
      very edge of the ellipsoid are assigned negative weights, and the result is
      a very small negative block estimate.

      Regards,
      Colin

      -----Original Message-----
      From: Digby Millikan [mailto:digbym@...]
      Sent: 23 March 2005 11:46
      To: ai-geostats; Els Verfaillie
      Subject: Re: [ai-geostats] practical range vs range

      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
    • Pierre Goovaerts
      Hi Els, The key question here is the sampling density and how many data will be included in this search window. If there are many, the screening effect will
      Message 2 of 5 , Mar 23, 2005
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        Hi Els,

        The key question here is the sampling density and how many data will
        be included in this search window. If there are many, the screening effect
        will greatly attenuate the impact of the data further away, hence using a or
        3a won't make a big difference. If data are sparser, then usually I set up my
        search strategy in terms of maximum number of data, not maximum search
        radius, at least in 2D (in 3D setting the search ellipsoid right is very important).
        Although simple kriging weights become zero beyond the range, it is not
        the case for ordinary kriging, which is a reason why you shouldn't systematically
        discard the observations outside the range of autocorrelation, in particular
        if the sampling density is low..

        Regards,

        Pierre

        -----Original Message-----
        From: Els Verfaillie [mailto:els.verfaillie@...]
        Sent: Wed 3/23/2005 5:08 AM
        To: ai-geostats@...
        Cc:
        Subject: [ai-geostats] practical range vs range



        Hi list,

        I want to do ordinary kriging with an anisotropic variogram with GSLIB. My
        variogram is an exponential model with a practical range of 1800 m in
        direction 50 and 880 m in direction 320. I'm not sure whether I have to use
        the practical range (which is 3a) or the value a, which is respectively 733
        m and 293 m. Furthermore I wonder which maximum search radius I have to
        choose: the 3a or the a value?

        Any suggestions?

        Cheers,
        Els

        ___________________________________________________

        Els Verfaillie, PhD student
        Renard Centre of Marine Geology - Ghent University
        Krijgslaan 281-S8
        B-9000 Gent - Belgium
        tel: +32-9-2644573 fax: +32-9-2644967
        e-mail: Els.Verfaillie@...
        url: http://www.rcmg.ugent.be/
        ___________________________________________________

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