Loading ...
Sorry, an error occurred while loading the content.

Re: AI-GEOSTATS: Modelling anisotropy with GSTAT

Expand Messages
  • Digby Millikan
    Pei-Chun, Question 1, You may be interested in some of these points on lognormal kriging, as I have been involved with lognormal kriging of datasets, but not
    Message 1 of 1 , Dec 2, 2002
    • 0 Attachment
      Pei-Chun,

      Question 1,
      You may be interested in some of these points on lognormal
      kriging, as I have been involved with lognormal kriging of datasets,
      but not in use for unconditional simulation.

      - Lognormal kriging can be very sensitive. If you plot experimental
      variograms of your lognormal dataset, note that if your variograms
      are well formed you are in luck, however if your variograms are very
      erratic, note that your grades will be in error in proportion of the error
      in your estimation of the sill.
      - Yes prior to lognormal kriging you log transform the data so it has
      zero skewness and is normally distributed. Note that if your data
      belongs to a "three" parameter lognormal population you must also
      make an estimte of the third parameter alpha, and add this to your
      values before taking their logarithms.
      If you plot a log probability plot of your data and it is a straight line
      then you have a two parameter lognormal distribution and you can
      take logarithms of the data to acheive zero skewness.
      If the line drops of towards the origin you may have a three parameter
      lognormal population with which you can estimate alpha from the
      graph or alternatively iterativley trial different values to your population
      until it has a skewness of zero i.e.
      z=ln(x+alpha)
      where z is the transformed distribution which has skewness=0;

      - Also note that the anti-logarithm of a number is not equal to the
      logarithm of a number, so after your modelling, you cannot back
      transform your data, by simply taking the antilogarithm of the values.
      You will have to check a geostatistical text to see the procedure for
      back transformation of data.

      Question 2,
      I have just read Margaret Armstrongs "Basic Linear Geostatistics" and
      in the chapter on Structural Analysis provides three case studies, and in
      all case studies uses the same nugget effect for all directions, even though
      in two of the case studies there is variation of the nugget effect in different
      directions as in your case.
      I would be inclined to use the omidirectional nugget effect in your case, the
      values are reasonably similar as in the case studies. It is possible the nugget
      effect varies in the different directions due to the different spacing of the data
      in the different directions, and the data is samples also, so may not perfectly
      follow the real underlying values of the actual continuous data.

      Regards Digby Millikan B.Eng

      Geolite Mining Systems
      U4/16 First Ave.,
      Payneham South SA 5070
      Australia.
      Ph: +61 8 84312974

      digbym@...
      http://www.users.on.net/digbym

      [Non-text portions of this message have been removed]
    Your message has been successfully submitted and would be delivered to recipients shortly.