Empirical Bayesian kriging also differs from other kriging methods by accounting for the error introduced by estimating the underlying semivariogram. Other kriging methods calculate the semivariogram from known data locations and use this single semivariogram to make predictions at unknown locations; this process implicitly assumes that the estimated semivariogram is the true semivariogram for the interpolation region. By not taking the uncertainty of semivariogram estimation into account, other kriging methods underestimate the standard errors of prediction.
Advantages
- Requires minimal interactive modeling
- Standard errors of prediction are more accurate than other kriging methods
- Allows accurate predictions of moderately nonstationary data
- More accurate than other kriging methods for small datasets
Disadvantages
- Processing time rapidly increases as the number of input points, the subset size, or the overlap factor increase. Applying a transformation will also increase processing time. These parameters are described below.
- Processing is slower than other kriging methods, especially when outputting to raster.
- Cokriging and anisotropy are unavailable.
- A small number of parameters in the semivariogram model limits the ability to customize. Other kriging methods provide many choices for the semivariogram model.
- The Log Empirical transformation is particularly sensitive to outliers. If you use this transformation with data that contains outliers, you might receive predictions that are orders of magnitude larger or smaller than the values of your input points. This parameter is described in the “Transformations” section below.
Local fit, especially good for non-stationary data
- Max likelihood simulation to get the semivarigram
Normal score transformation:
- Log
- Log Empirical
The Log Empirical transformation requires all data values to be positive, and it will guarantee that all predictions will be positive
- None
Recommended Work Flow for Kriging
- EBK: all default values (baseline)
- Kernel smoothing (baseline)
- Traditional Kriging (baseline)
- EBK: transformation (& change different parameters)
Sometimes there are more than one model that works well, pick the one
Resources:
Appedix
2. A Practical Guide to Geostatistical Mapping