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Re: [ai-geostats] sisim and SK vs. OK

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  • Pierre Goovaerts
    Hi Eric, I am guessing that the sampling density in 3D is small, hence at the beginning of the simulation procedure when the grid is essentially empty the
    Message 1 of 2 , Jan 3, 2005
      Hi Eric,

      I am guessing that the sampling density in 3D is small, hence
      at the beginning of the simulation procedure when the grid is
      essentially empty the estimate depends mainly on the type of trend
      model you adopt, i.e. a global mean for SK or a locally re-estimated
      local mean for OK. It looks like your estimate of the local mean
      is not very good and this might biase your simulation right from
      the beginning. Are you using a multiple grid strategy and did you
      notice a higher proportion of order relation deviations when using OK
      versus SK? Note that in the recent Stanford software GEMS, sequential
      indicator simulation is available only with the SK option, which
      might indicate some problems with the OK option...

      Hope it helps,

      Pierre
      <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

      Dr. Pierre Goovaerts
      President of PGeostat, LLC
      Chief Scientist with Biomedware Inc.
      710 Ridgemont Lane
      Ann Arbor, Michigan, 48103-1535, U.S.A.

      E-mail: goovaerts@...
      Phone: (734) 668-9900
      Fax: (734) 668-7788
      http://home.comcast.net/~goovaerts/

      <><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>

      On Mon, 3 Jan 2005 ewbhark@... wrote:

      > Hello all. I'm performing unconditional sequential indicator simulation over a 3D domain. As the method requires, I have specified the data cdf at (10) thresholds, and have also defined parameters for an (exponential) variogram at each threshold. When I run the simulation algorithm using simple kriging to estimate the cdf at each threshold for each node, the method works great, i.e., I am able to approximatley reproduce the domain cdf and variograms. However, when I use ordinary kriging, the method falls apart. It seems that reproduction of the domain cdf becomes 'blocky' and looses its smoothness. I have made the search ellipsoid very large and allowed the number of data points that can be used for the OK to be very large, but the results are always poor. Does anyone have any suggestions about what is happening with the OK? I am using GSLib software. Thanks to all!
      >
      > Eric Bhark
      >
      >
      >
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