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Re: AI-GEOSTATS: Back transforms and simulations

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  • Chris Lloyd
    Hello, Many thanks to Isobel Clark and Pierre Goovaerts for sending replies to my email about simulation and back transforms. Both pointed out that in the case
    Message 1 of 3 , Oct 20, 2003
      Hello,

      Many thanks to Isobel Clark and Pierre Goovaerts for sending replies to
      my email about simulation and back transforms. Both pointed out that in
      the case of simulation the back transform is straightforward. Pierre
      also noted that GSLIB allows normal scores transforms and back
      transforms, which are more flexible than log transforms.

      Both replies are copied below.

      Chris


      Isobel:

      Since your simulated values should have the same distribution as the
      original data, you simply need to anti-log.

      I prefer to use 'natural' logarithms for transformation and then do
      e-to-the-x, but using logs to the base 10 and then 10-to-the-x should
      work just
      as well. The answer is rather more complicated if you krige with logs to
      the base 10.

      Pierre:

      The back transform of simulated values is very easy to perform. Just
      take the exponential of the simulated values since you are not trying to
      estimate the mean of the local probability distribution in the original
      space, but only a quantile of this distribution. Note that if you
      perform SGS using Gslib, there is a built-in normal score transform and
      back-transform in the program, which is more flexible than the lognormal
      transform.



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