contents   index   previous   next

Suggested Approach


Characterizing spatial correlation across the site through experimental variography can often be the most time consuming step in a geostatistial analysis. This is particularly true if the data are heterogeneous or limited in number. Without a rationale for identifying the major direction of anisotropy, the following steps might be useful in narrowing the focus of the exercise.


1) Begin with an omni-directional variogram with a bandwidth large enough to encompass all data points on the site.


2) Select the number of lags and lag distances sufficient to span a significant portion of the entire site, and choose the lag tolerance to be very close in value to the lag distance itself.


3) Press the Variography button. In most cases, data become less correlated as the distance between them increases. Under these circumstances, the semivariogram values should produce a monotonic increasing function which approaches a maximal value called the sill. In practice, this may not be the case with variogram values that may begin high or jump around as distance increases.


4) Adjust the number of lags and lag tolerances until, generally, a monotonic increasing trend is seen in the semivariogram values. If this cannot be achieved, it may be that a geostatistical approach is not viable or that more complicated trends are occuring than can be modeled within SADA. If a visual inspection of the data or knowledge about the dispersion of contamination indicates a direction of correlation, it may be more appropriate to first test this direction.


5) Assuming the omni-directional variogram is reasonable, add another direction to the plot with a smaller tolerance. You may have to adjust the bandwidth and angle tolerance to produce a reasonable semivariogram plot.


6) If the second direction rises slower to the sill or rises to a lower sill, then this is the major direction of anisotropy.


7) If neither direction produces significantly lower spatial correlation, it may be reasonable to assume an isotropic correlation structure.


8) Add a cone structure with direction equal to the major direction plus 90 degrees, and model the semivariogram results in this direction.


9) If the data are isotropic, choose the omnidirectional variogram as the major direction.

Now the spatial correlation models may be applied. (See Modeling Spatial Correlation.)