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Re: AI-GEOSTATS: Optimal Kriging parameters

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  • Syed Abdul Rahman Shibli
    Variogram modeling is usually a pre-requisite for kriging and/or stochastic simulation. It s not usally something that you d want to automate in some sort of
    Message 1 of 3 , Jul 9, 2002
      Variogram modeling is usually a pre-requisite for
      kriging and/or stochastic simulation. It's not
      usally something that you'd want to "automate" in some
      sort of computer program. Selection of a model type/range/sill
      will usually be based on available sample points,
      or analagous samples of the same origin as the dataset
      one is looking at, complemented by a qualitative interpretation
      of the spatial model. I guess one can try to "program"
      this whole process from start to finish (exploratory
      data analysis/variogram modeling/kriging) but this is
      not at all recommended. Perhaps in some applications with
      abundant data, yes, but probably not in a geoscience
      setting.

      You haven't told us what your applications are? Will you
      be mapping some geological variable? Interpolating 6 million
      pixels in an image file? Trying to gauge the distribution of a
      certain species of rare tropical flower?

      Syed

      ---- Original message ----
      >Date: Mon, 8 Jul 2002 20:56:15 -0700
      >From: "Eva Pierce" <logicgrrl@...>
      >Subject: AI-GEOSTATS: Optimal Kriging parameters
      >To: <ai-geostats@...>
      >
      >Hi.
      >
      >I want to use Ordinary Kriging on an arbitrary dataset of X,Y, and Z
      >values to estimate the Z values on a grid of arbitrary size/density. But
      >I don't know what length and scale parameters to choose for the
      >semivariogram. So I need to answer the following questions. I'm looking
      >for guidance and resources, not necessarily definitive answers. When
      >answering, please keep in mind that I'm a computer programmer, not a
      >statistician, by education and experience. :-)
      >
      >1. How does one measure the "goodness" or "badness" of a Kriging
      >estimate? E.g. when the bounds of the grid are fairly close to the
      >bounds of the dataset, I might expect the estimated surface of Z values
      >to have roughly the same number of "bumps" and "valleys" as the original
      >dataset (if discernible), and not too many flat regions. How do I
      >quantify such characteristics, and are there others I should be looking
      >for?
      >2. How does one arrive at the "optimal" length and scale parameters
      >for the semivariogram when doing ordinary Kriging, given these measures
      >of "goodness" and "badness"? (here's where my comp. sci education would
      >come in handy, if I knew the answer to #1)
      >
      >I'll send out a summary of answers that I receive. Thanks!
      >Eva
      >
      >
      >
      >

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    • Isobel Clark
      Hi Eva You have your questions the wrong way round. Once you find the semi-variogram model for your particular application, the kriging system should povide
      Message 2 of 3 , Jul 9, 2002
        Hi Eva

        You have your questions the wrong way round. Once you
        find the semi-variogram model for your particular
        application, the kriging system should povide you with
        a measure of 'goodness' of the estimator. It is
        usually called the 'kriging standard error' or
        sometimes software provides the kriging variance.

        Please feel free to download a free copy of my (old)
        book at
        http://uk.geocities.com/drisobelclark/practica.html

        It is only 125 A5 pages long and you can skip a lot of
        that to get what you need. Alternatively download my
        RSMA article which says much the same thing in 500
        words. Find this on
        http://uk.geocities.com/drisobelclark/resume/Publications.html

        Mind the capital P on Publications ;-)

        Hope this helps
        Isobel Clark

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