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AI-GEOSTATS: Comparison of semivariograms

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  • Kalle Kronholm
    Dear list members in both hemispheres; For my PhD thesis, I am studying the spatial variability of penetration resistance (a proxy for strength) in snow layers
    Message 1 of 1 , Jul 28, 2003
      Dear list members in both hemispheres;

      For my PhD thesis, I am studying the spatial variability of penetration
      resistance (a proxy for strength) in snow layers in an Alpine snow
      cover. Of specific interest are weak layers that are responsible for
      snow avalanche release. Are such weak layers less spatially variable
      than layers that are not critical for snow stability? To answer this, I
      want to compare the range, sill and nugget from model semivariograms
      calculated for all layers investigated. I also calculate mean and CV for
      each layer.

      Measurements:
      At 113 locations on each small slope (20m x 20m), measurements of
      penetration resistance were made in a nested grid with a spacing of 0.5m
      to 2m. The penetration resistance for each layer within the grid was
      recorded at all locations. I have data from approximately 100 layers.
      Weak layers were identified with separate tests within each grid.

      Analyses:
      The penetration resistance for each layer was log10 transformed to
      approach normality. Grid-scale trends for each layer were investigated
      with a (robust) linear regression on the x-y coordinates. In most
      layers, this trend was statistically significant, but often in different
      directions even in adjacent layers. The trend was removed to do a
      geostatistical analysis on the normally distributed residuals. A robust
      experimental semivariogram was calculated for the residuals for each
      layer. Now I want to fit a spherical model semivariogram to the
      experimental semivariograms. The spherical model fits the data from most
      layers better than other models.

      Questions:
      - Is it possible to compare directly the range, the sill and the nugget
      of the spherical model semivariograms fitted to the residuals of the
      linearly detrended data for each layer?
      - Are there any pit-falls that I should be aware of? (Should I test
      different semivariogram models for each layer? Should I leave the linear
      trend in the data for the structural analyses? ...)

      All comments, suggestions and references are welcome and will be much
      appreciated. I will be happy to provide more info if needed.

      Best regards,

      Kalle

      PS: No one was hurt during the measurements ;-)




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