More Options - Empirical Covariance

Empirical covariance is the covariance (‘cross-correlation function that is not normalized’) computed from measurements.

Computing Empirical Covariance

  1. Select the gridding method you wish to work with by clicking on the arrow in the upper right-hand corner of the Kernel Gridding window under Gridding Method. Select one of the gridding methods from the drop-down menu.
  2. Click <More Options> in the lower right-hand corner of the window.
  3. In the window that opens, click the <Compute Empirical Variogram> button. A dialog box will open in which you can resample your input data to save computation time.
  4. Click <OK> to begin calculation.

The empirical covariance plot will look something like this:

image\EmpCov.gif

The empirical covariance plot is a plot of correlation (CO) versus distance (r). If the half-correlation point (CO/2) is well to the left of the center of the diagram, the data correlation over distance is low. This indicates that interpolation in zones with few input data points may be poor. Generally, the further the half-correlation point lies to the right, the higher the data correlation to distance is and the better your grid interpolation results will be.

The covariance can be used as a guideline in deciding how large a margin to use when incorporating data outside your gridding area in your gridding calculations. When you compute empirical covariance, the resulting value is automatically inserted for margin distance. In the Kriging gridding method, the half-correlation length is also inserted automatically when you compute empirical covariance.

Variogram characterizes the spatial continuity of a value.

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