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Re: [ai-geostats] practical range vs range

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  • Digby Millikan
    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
    Message 1 of 5 , Mar 23, 2005
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      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
    • 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 2 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
      • "Julián Ortiz C."
        Dear Els, GSLIB uses the practical range for the exponential model.The best way to check this is to calculate your experimental variograms with gamv, run the
        Message 3 of 5 , Mar 23, 2005
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          Dear Els,

          GSLIB uses the practical range for the exponential model.The best way to
          check this is to calculate your experimental variograms with gamv, run
          the model with vmodel and then plot them together using vargplt. If the
          model fits correctly your data, then you can copy the parameters from
          vmodel to whatever program you are using next (kt3d, sgsim, or any other).
          In any case, you can check the source code to see which parameter each
          model requires (the fortran 77 code is available for free at
          www.gslib.com; you can check the cova3 subroutine that calculates the
          variogram values).

          Hope this helps! Regards,

          Julián.

          --------------------------------------
          Julián Ortiz C., Ph. D.
          Assistant Professor
          Department of Mining Engineering
          University of Chile

          www.ualberta.ca/~jmo1
          www.minas.cec.uchile.cl



          Els Verfaillie wrote:

          >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/
          >___________________________________________________
          >
          >
          >
        • 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 4 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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