Using semi-variograms

Semi variogram Geostatistics

Binned values are shown as red dots, and are generated by grouping (binning) empirical semivariogram/covariance points together using square cells that are one lag wide. Average points are shown as blue crosses, and are generated by binning empirical semivariogram/covariance points that fall within angular sectors. Binned points show local variation in the semivariogram/covariance values, whereas average values show smooth semivariogram/covariance value variation. In many cases it is easier to fit a model to the averaged values, as they offer a less cluttered view of the spatial autocorrelation in the data and show smoother changes in the semivariogram values than the binned points.

The Show points control can be set to Binned and Averaged (as shown in the figure above), Binned, or Averaged (as shown in the figures below).

Additionally, lines can be added to the plot. The lines are local polynomials fitted to the binned empirical semivariogram/covariance values. If the Show search direction option is set to True, then only the local polynomial fitted to the empirical semivariogram/covariance surface in the Show search direction tool’s central axis transect is displayed, as shown in the following figure:

The semivariogram/covariance model you fit to the empirical data should:

  • Pass through the center of the cloud of binned values (red dots).
  • Pass as closely as possible to the averaged values (blue crosses).
  • Pass as closely as possible to the lines (green lines).

Keep in mind that your knowledge of the phenomenon may dictate the shape of the model as well as its nugget, range and partial sill and anisotropy values, even though the model does not fit the empirical data too well (recall that the empirical data is just a sample of the real phenomenon you want to model, and may not be fully representative of all of its spatial and statistical aspects).

Different types of semivariogram/covariance models

Geostatistical Analyst provides the following functions to model the empirical semivariogram:

  • Circular
  • Spherical
  • Tetraspherical
  • Pentaspherical
  • Exponential
  • Gaussian
  • Rational Quadratic
  • Hole Effect
  • K-Bessel
  • J-Bessel
  • Stable

The selected model influences the prediction of the unknown values, particularly when the shape of the curve near the origin differs significantly. The steeper the curve near the origin, the more influence the closest neighbors will have on the prediction.

As a result, the output surface will be less smooth. Each model is designed to fit different types of phenomena more accurately.

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