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Re: [ai-geostats] Regression vs. Kriging vs. Simulation vs. IDW

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  • Digby Millikan
    Seumas, I was probably a bit misleading to say regression is not an estimation technique. The word regression meaning to revert back to the original, or find
    Message 1 of 16 , Jan 5, 2005
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      Seumas,

      I was probably a bit misleading to say regression
      is not an estimation technique. The word regression
      meaning to revert back to the original, or find the
      underlying real equation for a set of data. "Kriging"
      is a form of what is called "generalised linear regression"
      which is one of the most advanced forms of regression.
      The simpler forms of regression can be used to fit
      parametrics equations to data, such as linear regression
      to fit an equation of a line to a set of data points,
      or non-linear regression to fit a polynomial surface
      to a scattered set of say topography data points.
      Not really estimation, but equation fitting. I use non-linear
      regression to fit equations to drillhole survey points
      to plot their curves. In it's more advanced form when
      you wish to fit equations to say a set of two dimensional
      data points, or three dimensional orebody samples,
      this is called trend surface fitting. Unfortunately normally
      the equations developed from trend surface fitting
      become massively too complex to handle to be practical,
      and hence estimation is opted for.

      Digby
    • Digby Millikan
      For ore resource modelling I ve used IDW on a highly skewed lognormally distributed deposit, where no variograms could be produced. With lognormally
      Message 2 of 16 , Jan 5, 2005
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        For ore resource modelling I've used IDW on a highly skewed lognormally
        distributed deposit, where no variograms could be produced. With lognormally
        distributed data often found in ore resources, having a good variogram is
        important, to avoid large errors in kriging hence it may be preferential to
        use
        IDW and a topcut. However if your data is not so highly skewed even
        approximating
        a variogram can provide superior results. I used to model topography
        surfaces
        and Kriging with a 'guessed' variogram produced good results compared to
        IDW which produced highly spiked and erroneous results.

        Digby
        www.users.on.net/~digbym
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