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561RE: AI-GEOSTATS: Ore Reserves Classification

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  • Stelpstra, David
    Apr 3, 2002
    • 0 Attachment
      Dear list members,

      I wish to make a remark on the discussion started on the kriging variance.
      In my opinion the SD of the solution obtained by kriging is determined by
      two effects. One is the (geometrical) distribution of the data, the other
      one is the (a priori) standard deviation of the data. Richards remark is
      right if the data has equal weights or standard deviations (SD). However if
      the data is all weighted equally (by say 1.0) we will get a scaled 'SD'. If
      you like studentized statistics, you can get a estimate for the SD of the
      solution by multiplying with the a posteriori variance factor.

      In our problems (cross validation of bathymetry data) we have a estimate for
      the SD, which we use in the covariance function for the kriging problem. The
      resulting SD of the solution will depend on the chosen a priori SD for the

      Just my 2 cents,

      Quality Positioning Services bv, Huis ter Heideweg 16, 3705 LZ
      Zeist, the Netherlands
      Tel +31 (0)30 6925825, Fax +31 (0)30 6923663, Web http://www.qps.nl

      -----Original Message-----
      From: Richard Hague [mailto:richardh@...]
      Sent: woensdag 3 april 2002 6:48
      To: ai-geostats@...
      Subject: Re: AI-GEOSTATS: Ore Reserves Classification

      List Members,

      The use of the kriging variance to categorise/classify Mineral (Ore)
      Resources and/or Ore Reserves is an old chestnut that periodically raises
      it's ugly head. The kriging variance is related, pure and simply, to the
      data configuration and has nothing to do with the sample grades/variables
      being used for interpolation. As an example say a grade was being
      interpolated into a block which has been sampled on each corner, regardless
      of what the individual sample grades are, the kriging variance for that
      block is going to be the same. Example: if all four samples have the same
      grade of (say) 2.35g/t Au you will get the same kriging variance as the case
      where the four samples grades are (say) 0.01, 102.9, 0.88 and 3.60 g/t Au.
      Naturally I would have more confidence in the interpolated grade in the
      former scenario than the latter; thus the use of the kriging variance to
      determine confidence (or classification) of an estimate is misleading.

      One method of obtaining some feel for the possible error range would be to
      run a large number of grade simulations into the block, the variance of all
      simulated grades would give an indication of error - again in the example
      given above, the variance of the simulated grades using the former case
      would be much smaller than in the latter case.

      Of course classification of Mineral (Ore) Resources and/or Ore Reserves
      needs to take into account a lot more items (as expounded by the JORC
      (1999) code) - than just some objective measure of estimation error, it
      needs to take into consideration, amongst other things, data quality - if
      you have poor quality data (eg biased/inaccurate), regardless of how good
      any statistical measure of the estimation error is, you will always have
      poor estimate.

      JORC; 1999: Australasian code for reporting of mineral resources and ore
      reserves (the JORC Code). Prepared by the Joint Ore Reserves Committee of
      the Australasian Institute of Mining and Metallurgy, Australian Institute of
      Geoscientists and Minerals Council of Australia (JORC).

      Richard Hague
      Hellman & Schofield Pty Ltd
      Brisbane Office
      p&f: +61 (0)7 3217 7355
      e: richardh@... <mailto:richardh@...>
      w: http://www.hellscho.com.au <http://www.hellscho.com.au>

      ----- Original Message -----
      From: José <mailto:cuador@...> Quintín Cuador Gil
      To: ai-geostats@... <mailto:ai-geostats@...>
      Sent: Wednesday, March 27, 2002 4:27 AM
      Subject: AI-GEOSTATS: Ore Reserves Classification

      Dear list members

      The Kriging variance has some uses. In mining, it can be used in the Ore
      Reserves Classification.
      What is the opinion about this in the Geostatistical community?
      It is possible to use the Kriging variance for ores reserves
      classification?, (Yes or No).
      Thanks in advances for any opinion.

      José Quintín Cuador Gil
      Department of Informática
      University of Pinar del Río
      < cuador@... <mailto:cuador@...> >

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