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AI-GEOSTATS: entering the fray again

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  • Ian Hunt [KLP BUSINESS DEVELOPMENT]
    This is one awesome discussion - sounds better than the symposiums !! Maybe we must clarify a few points by Steven! 1) What manager in the mining or petroleum
    Message 1 of 1 , May 23, 2001
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      This is one awesome discussion - sounds better than the symposiums !!

      Maybe we must clarify a few points by Steven!

      1) What manager in the mining or petroleum industry who has
      graduated
      from college hasn't taken a serious statistics course, including
      covariances
      and correlations?

      >> I am a geologist with ten years mining experience in South Africa.
      It is part of my job to try and implement geostatistical techniques in
      grade estimations on the mines. The mining/geological managers knows
      how to take ore out of the ground and sell it. They would not be
      bothered by the covariance or the difference approach - the only thing
      is that there is BIG problems if the actual plant feed grade is lower
      than predicted grade. Then the true colors of the managers come out and
      the in-house geostatician has lots of problems. If the in-house
      geostatician can blame it on the outside consultant/instructor then he
      might survive.

      2) Surely when starting from scratch, educating someone about
      geostatistics is more intuitive using covariances? (Just my opinion so
      far,
      speaking as a mathematician who remembers teaching basic college level
      statistics to nursing majors, education majors, sociology majors, etc.
      And
      even succeeding occasionally.)

      >> From my personal experience in trying to explain to managers why they
      should not use polygonal or inverse distance estimates on their mine, it
      was much easier to explain it with the difference method. The
      discussion would normally be around samples from drillholes (which the
      managers always refers to as being expensive) which are more different
      at larger distances and hopefully less different at closer distances.
      Which is why the geologist likes to drill at closer distances - it also
      tends to make the plant grade feed predictions more accurate.

      3) I have taken two multi-day courses in geostatistics from well
      known
      industry experts. In each class they included significant material and
      time
      on the first day explaining/justifying variograms by showing their
      mathematical relationship to spatial covariance functions. It seems
      that
      those instructors did not trust the variogram to be more intuitive than
      spatial covariance functions.

      >> I have been in a few practical geostats courses where the students
      are a mix of surveyors, miners, samplers, grade control officers and
      geologists. Somehow it always seems to be easier for the instructor to
      use the difference method to explain variography.

      4) The two basic level introductions to geostatistics I have on my
      bookshelf replicate the experience at those two classes....

      >> As a practicing geostatician on a producing mine the reality is that
      we can have huge discussions on the mathematical delicacy's of pivoting
      the Kriging equations etc. but the feed grade to the plant or ore beds
      must be in close proximity of what was predicted. The big mining
      companies and banks are spending huge amounts of capital on mining
      ventures and the grade/tonnage curves (or block model as some people
      call it) plays a significant part in the decision. And any industry
      geostats course must be practical enough so that the students can go
      back and make accurate grade/tonnage curves. That's what pays the
      bills.

      I don't know if I am getting the message through - the big mining
      companies needs the practical experience because the "proof of the
      pudding lies in the plant feed"


      Ian Hunt


      -----Original Message-----
      From: Yetta Jager [SMTP:zij@...] <mailto:[SMTP:zij@...]>

      I think part of the difficulty in the semivariogram vs. covariance war
      is
      that modeling is subjective, and the notion of covariance has become
      more
      intuitive for statisticians, while the notion of semivariance has
      become
      more intuitive for geologists.

      From: Isobel Clark [drisobelclark@...]

      I agree that the semi-variogram approach is easier for the
      non-statistician
      to grasp. Difference in value is a simpler concept to grasp than
      cross-product, especially when your boss wants to know the likely
      difference
      between what you tell him and what really happens!



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