Those of you familiar with kriging interpolation know that it is not always the easiest technique to implement successfully. For a long time we’ve wanted to make a geoprocessing tool that can automate kriging, but the problem has always been in the complexity of calculating good default parameters. At 10.1, through a combination of subsetting and simulations, we have a solution to the problem with a method called empirical Bayesian kriging (EBK). The method is available in the Geostatistical Wizard and as a geoprocessing tool in the Geostatistical Analyst toolbox.
EBK works by building local models on subsets of the data, which are then combined together to create the final surface. Because the interpolation model is built automatically, the method requires very few parameters. There are also some optional parameters that give you some control over how locally the models will be built and how they will be combined together.
Why should I use EBK?
- Simplicity – To get accurate results, all you need to do is specify the field you want to interpolate. Other kriging methods require you to build the model step-by-step to be confident that the results are statistically accurate.
- Automation – Because EBK is available as a geoprocessing tool, you can use it in Model Builder and in Python scripts.
- Capture small-scale effects – Using local models allows EBK to capture small-scale effects that global kriging models may miss.
This post was contributed by Eric Krause, a product engineer on the analysis and geoprocessing team.