Geostatistics provides many different tools for specific problems in spatial prediction. These tools come from many fields of application and are documented in many forms; however, there are few places for the geomodeler to find specific advice on important decisions in geostatistics. Geostatistics Lessons is an open disclosure of some guidance in geostatistical modeling reviewed by an editorial board.
It is not possible to consider all geological situations, problem settings, project goals and data. Nevertheless, there is value in experienced geostatisticians publishing their belief of what constitutes best practice. Those beliefs will change as experience is gained; Geostatistics Lessons will be updated as new lessons are authored, revised and reviewed.
- Transforming Data to a Gaussian Distribution
- An Application of Bayes Theorem to Geostatistical Mapping (see source code on GitHub)
- Sphereing and Min/Max Autocorrelation Factors
- Principal Component Analysis
- Projection Pursuit Multivariate Transform
For more information on Geostatistics Lessons, see the About page.