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RE: [ai-geostats] Very narrow Archean Gold Reef distribution

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  • Dan Bebber
    By do anything with the data , do you mean geostatistics or other statistics? If geo, how about transforming to a binary indicator, i.e. gold worth bothering
    Message 1 of 4 , Nov 9, 2004
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      By "do anything with the data", do you mean geostatistics or other statistics? If geo, how about transforming to a binary indicator, i.e. gold worth bothering with (1), gold not worth bothering with (0)? It sounds like you've got a lot of places with no gold at all. It also sounds like you've got extreme anisotropy.
       
      Dan
       
      Department of Plant Sciences
      University of Oxford
      South Parks Road
      Oxford OX1 3RB
      UK

       

      -----Original Message-----
      From: Gayle Hanssen [mailto:dms@...]
      Sent: 09 November 2004 17:11
      To: ai-geostats@...
      Subject: [ai-geostats] Very narrow Archean Gold Reef distribution

      Dear All
       
      I am involved in trying to find a solution for a gold mine in Zimbabwe.  The veins are very thin (from 5cm to 4m) with grades from trace to 2.2% Au. 
       
      We are able to physically model the veins (a series of them).  However, I am informed that the grade distribution is a "J-type" distribution and needs to be "normalised" before we can do anything with the data.  A log-normal distribution does not work and it is still hugely skewed.
       
      Any suggestions on solutions or software applications that can solve this problem?
       
      Regards
      Gayle
       
       
      Gayle Hanssen
      Digital Mining Services
      P O Box HG528, Highlands
      Harare, Zimbabwe
       
      Ph +263 4 730 534
      Cell +263 11 601 973
      e-mail dms@...
    • bob sandefur
      Hi- Without knowledge of the geometry of the veins the suggestion may be pretty silly, but if the veins occur in swarms which are combined for mining I would
      Message 2 of 4 , Nov 9, 2004
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        Hi-

        Without knowledge of the geometry of the veins the suggestion may be pretty
        silly, but if the veins occur in swarms which are combined for mining I
        would suggest diluting to full mineable thickness (if the veins are
        intersected at odd angles this may be fairly tricky) and then looking at
        mineable grade thickness and mineable thickness.

        Regards

        Robert (Bob) L. Sandefur PE
        Senior Geostatistician / Reserve Analyst
        CAM
        200 Union Suite G-13
        Lakewood, Co
        80228

        rsandefur@...

        303 472-3240 (cell) <-best choice

        303 716-1617 ext 14



        ________________________________

        From: Gayle Hanssen [mailto:dms@...]
        Sent: Tuesday, November 09, 2004 10:11
        To: ai-geostats@...
        Subject: [ai-geostats] Very narrow Archean Gold Reef distribution


        Dear All

        I am involved in trying to find a solution for a gold mine in Zimbabwe. The
        veins are very thin (from 5cm to 4m) with grades from trace to 2.2% Au.

        We are able to physically model the veins (a series of them). However, I am
        informed that the grade distribution is a "J-type" distribution and needs to
        be "normalised" before we can do anything with the data. A log-normal
        distribution does not work and it is still hugely skewed.

        Any suggestions on solutions or software applications that can solve this
        problem?

        Regards
        Gayle


        Gayle Hanssen
        Digital Mining Services
        P O Box HG528, Highlands
        Harare, Zimbabwe

        Ph +263 4 730 534
        Cell +263 11 601 973
        e-mail dms@...
      • Digby Millikan
        MessageGayle, I can t give you any solution but can inform you of my past experience; - Gold deposits normally display skewed distributions, so that if the
        Message 3 of 4 , Nov 9, 2004
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          Gayle,
           
           I can't give you any solution but can inform you of my past experience;
           
              - Gold deposits normally display skewed distributions, so that if the
                assays are used for modelling (usually block modelling) the result
                will be that the grade will be overestimated.
              - Lognormal kriging was developed to overcome this. You krige the
                natural logarithm (not base 10) of the grades then back transform
                the results making an adjustment for the variance.
              - The problem with lognormal kriging is that it is highly sensitive to
                 your interpreted variograms, and infact the error in your grade
                 estimate is directly proportional to the error in your variogram
                 e.g. sill estimation.
                 Hence lognormal kriging is best reserved for deposits which show
                 very well formed lognormal variograms.
              - Gold data of course is not always strictly lognormal and often 
                 shows mixed distribution characteristics (you may use 
                 disjunctive kriging for mixed distributions which you may like for a 
                 comparison with other estimation methods) which worsens the
                 prospects of accurate modelling.
              - In such cases as a non strictly lognormal population and poor
                 variograms which may be the case the old hand method is to cut
                 the grade population prior to modelling then just use inverse
                distance squared or cubed (experience that cubed is better for
                skewed gold distributions) or ordinary kriging modelling.
              - Determing the cut value is the problem. Some use a cut value
                 based on experience or reconciliation of deposits in the region.
                 I have used a method whereby I calculate the sichel mean of  
                 the dataset (an estimate of the true mean of a lognormally
                 distributed population from a sample dataset) then cut the dataset
                 until the arithmetic mean of the cut dataset equals the previously
                 calculated sichel mean.
                 I am currently working on an improved top cut method 
                 calculation in association with Frans Manns.
           
             Software used is any generalised mining package for the modelling
             and for calculation of the topcut, if you don't have a macro based
             software or are intimately familiar with a database package,
             spreadsheet software will suffice.
           
             I have not checked or do not know if there is any software in the
            Stanford University GSLIB software library for treatment or 
            processing of mixed populations.
           
            If you would like further details on any of the above processes please
           feel free to contact me.  
           
           
          Regards Digby J. Millikan BEng.
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