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GEOSTATS: summary of responses

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  • Jon Jones
    Hello everyone, Here is a summary of the responses I received w.r.t. my question on indicator kriging. Hi Jon, Indicator Kriging can work with as many classes
    Message 1 of 1 , Jul 28, 2000
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      Hello everyone,
      Here is a summary of the responses I received w.r.t. my question on
      indicator kriging.

      Hi Jon,

      Indicator Kriging can work with as many classes as you want. At each grid
      node, it

      will give the probability for each class. You would then need to extract the
      class for each node, if you are after a categorical model. For the same
      amount of
      efforts, Sequential Indicator Simulation can give you the categorical model
      Monte-Carlo sampling from those local probabilities.

      Both algorithms require a variogram model for each class and both are
      available in


      Hello Jon,

      if you are not familiar with geometrical a n d geostatistical 3D-modelling
      then I would emphatically recommend to look for an alternative approach to
      your problem, i.e. corresponding to the scientific problem which you would
      analyze, e.g.

      1. is it possible to reduce to 2D
      2. can you transform classes of materials into hydro-geological relevant
      parameters (porosity, permeability or K-values) so that you can
      work with a single parameter in 3D space (which is difficult enough)
      3.? etc

      If you want to get a picture of the subsurface only then the relation
      of scientific result/expenditure tends to zero.


      Heinz Burger


      The GSLIB IK routines are all there is going to be (and all you need). I'm
      not sure what you mean by "2-parameter." An indicator CAN only be one of
      two values -- zero or one. Either, you have "sand" at location x or you do
      not. What you will get out of IK is the probability of having "sand" at an
      unsampled location. You can't krige the values 1 through 19 because that
      implies a numerical "sequence" to the values, whereas they really are just
      arbitrary class designations: "Fred" and "Mary" would do just as well.

      Obviously you can run the IK exercise 19 time, once for each class vs. "all
      others", but then you're going to need to confront the issue of how you
      convert all the probabilities to actual facies maps (Pr > 1/19?) and would
      it be consistent. The power of IK is that your spatial model (variogram) is
      customized to the variability of the indicator in question. You need a
      variogram for each class. The downside is that the indicator technique is
      an either-or proposition. The other plus is that IK forces you to confront
      the issue of uncertainty.

      Did you check out the gslib 1.2 program sisimpdf? This is intended for
      categorical variable (class) simulation. It will generate simulated maps of
      your 19 categories, but again, you've got to deal with the uncertainty
      issue. A simulation is only one of N possible alternative models, all
      equally likely. Note: sisimpdf is now incorporated within sisim in ver.

      You may need to do some thinking on what you're going to use the result for
      -- if a flow model, then you may be interested only in knowing where "sand"
      and "gravel" are, because that's where the water is going to flow.

      There is some literature around on modeling facies distributions using
      truncated gaussian simulation. I've never done it, but the technique has
      the advantage of respecting facies relationships: shale can only occur next
      to limestone and never next to sandstone -- that sort of thing. I'd
      recommend cruising the SCRF web site http://ekofisk.stanford.edu/SCRF.html)
      at Stanford and looking through their assortment of papers and look for
      titles including "facies" or "truncated gaussian simulation". You can also
      download gslib ver. 2 from there.

      Note that I'm assuming that you're modeling these lithologies as facies
      rather than as discrete layers (formations).


      Hi Jon,

      as I see it, your problem is primarily a problem of geometrically modeling,
      not of estimation/prediction. Therefore, I would skip GeoStats and start
      with some
      interactive tool for 3d geometrical design like gOcad from ENSG Nancy,

      Good luck,
      Helmut Schaeben


      What do you want exactly to interpolate?
      If it's just the probability of occurrence of each
      of the 19 classes, IK is the way to go and in the
      new Gslib version there is possibility to
      perform indicator kriging of categorical variables.
      Let me know if it's what you are looking
      for and I could send you the source code.


      Jon Paul Jones
      University of Waterloo
      Department of Earth Sciences

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