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2138RE: [ai-geostats] modelling trend and kriging type

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  • Isobel Clark
    Jul 7, 2005
      Your basic semi-variogram graph has a parabola added to it. Shoots off upwards (usually) at some distance. If the distance is large (past the range of influence) you can ignore it. See some of our  mid-80s papers on the Wolfcamp data which lots of people use as a teaching set now. Or read my free tutorial at http://geoecosse.bizland.com/softwares (kriging with trend).

      PCollier@... wrote:

      Hi all

      I may know this already, but what are the symptoms of data with a trend?  What is the difference between a dataset with a trend and a non-stationary dataset?


      Perry Collier

      Senior Mine Geologist
      Ernest Henry Mine  
      Xstrata Copper Australia
      Ph (07) 4769 4527
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      E-mail PCollier@...
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      Cloncurry QLD 4824
      "Light travels faster than sound. That is why some people appear bright
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      -----Original Message-----
      From: Pierre Goovaerts [mailto:Goovaerts@...]
      Sent: Friday, 1 July 2005 12:54 AM
      To: Recep kantarci; ai-geostats@...
      Subject: RE: [ai-geostats] modelling trend and kriging type

      To add to the excellent comments by Edzer and Gregoire,
      1. Universal kriging = kriging with a trend. The second terminology has been proposed by Andre
      Journel who felt that the term "universal" was vague and misleadingly "ambitious".
      2. Kriging with an external drift (KED) is mathematically the same as universal kriging (UK). Secondary variables
      are simply replacing the spatial coordinates used in UK.
      3. Regression kriging denotes all the techniques where the trend is modeled outside the kriging algorithm.
      There are various methods that can be used to model that trend, ranging from linear regression
      to neural networks. Kriging is used to interpolate the residuals. In practice these techniques have more
      flexibility than universal kriging in term of modeling the trend: multiple variables either categorical or
      continuous can be incorporated  easily and many sofwtare are available for this trend modeling.
      The only limitation is that the trend is modeled globally (i.e. the regression coefficients are constant
      in space) while in KED the coefficients are reestimated within each search window.

      Pierre Goovaerts

      Chief Scientist at Biomedware

      516 North State Street

      Ann Arbor, MI 48104

      Voice: (734) 913-1098
      Fax: (734) 913-2201


              -----Original Message-----
              From: Recep kantarci [mailto:recep_kantarci_1978@...]
              Sent: Thu 6/30/2005 9:38 AM
              To: ai-geostats@...
              Subject: [ai-geostats] modelling trend and kriging type
              Dear ai-geostats members
              When the data used has a trend, it is needed to model trend and in this case there exists various types of kriging to apply (universal kriging, kriging with a trend, regression kriging etc).

              If this is the case, does one should use the same type of kriging or different depending on modeling the trend using coordinates of target variable or using other (namely, secondary or auxillary) variables such as elevation or topography ? That is , are there a dinstinction depending on the type of variables to model the trend while kriging?

              Best regards


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