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AI-GEOSTATS: determinsitic models - residual kriging

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  • Dobler, Lorenz
    Hello Yetta, hello all Yetta! i don t know whether you remeber; you sent me your tm-report in march and i wish to thank you very much! I tried to answer you
    Message 1 of 1 , May 31, 2001
      Hello Yetta, hello all

      Yetta! i don't know whether you remeber; you sent me your tm-report in march
      and i wish to thank you very much! I tried to answer you but may be your
      e-mail does not work?!

      As i am not an expert in geostatistics i would like to ask some questions
      and would be glad if someone could bring me on the right way!

      What i got and want to do:
      I have data from different campaigns between 1980 and 2000 in a
      morphological heterogenous (elevation) region. I want to assess large scale
      (1:500.000) spatial variability of heavy metals in upper soil layers due to
      atmogenous depostion. Before making spatial estimations i have to homogenize
      the data (=> pattern free data!). i don't want to assess a spatial
      (coordinate dependend) drift!

      What i did up to now:
      i built up one deterministic (pattern-)model for each element via multiple
      linear regression analysis (SPSS). The pattern-models should explain
      variations in element-concentrations due to different binding/retention
      capacities of different soil types (c-content, pH), morphology (elevation at
      sample site) and (linear?) decreasing deposition rates since 1980 (year of
      taking the sample). As the data are "lognormal" i used a ln-transformation.

      1. When calculating multiple linear regression (pattern-)model i did
      not include xy-coordinates of the sample sites as aditional predictor
      variables allthough there is considerable correlation between coordinates
      and heavy metal contents (but there is no trend in variograms!). is that ok?
      2. (backtransformed) semivariograms of the pattern-models still have a
      "good shape" suggesting a spatial/random variability. shouldn't they behave
      like "white noise"?
      3. how do i get backtransformed residuals from ln-transfortmed data ?
      when i backtransform ln-residuals of the regression/pattern-models negative
      ln-values are just interpreted as positive values below 1,0. is it possible
      to get residuals by substracting (backtransformed) predicted values
      (=exp(regression model)) from the original (= untransformed) values?
      4. simultanous estimation of drift and semivariogram with a single
      realization is rigorously not possible. Are there some (simple)
      aproximations for the semivariogram? What about the iterative solution for
      the simultaneaous inference of drift and semivariogram, how does it work in

      I know there are a lot of questions but i hope you can give me some answers



      Lorenz Dobler
      Bayerisches Geologisches Landesamt
      Heßstr. 128, D-80797 München
      Tel.: ++49-89-9214-2766
      e-mail: Lorenz.Dobler@...

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