Best practice in geostatistical methodology will often choose methods/techniques that are simple, robust and within the level of available expertise. The methods, techniques, and analysis should be appropriate to the goals of the study. We should be able to explain the background and critical assumptions behind every technique and provide documentation for critical input parameters. Geostatistical models are constructed more than once and we must take care and compare with historical methods and justify current choices.
Methodology lessons are intended to help both relative newcomers and experienced geomodelers scope out a geostatistical study, choose specific workflows and tools, make implementation decisions and support the numerous interdependent decisions that must be made during a geostatistical study.
- Bayesian Updating for Combining Conditional Distributions
- An Application of Bayes Theorem to Geostatistical Mapping (see source code on GitHub)
- Combination of Multivariate Gaussian Distributions through Error Ellipses
- The Nugget Effect
- A Simulation Approach to Calibrate Outlier Capping
- The Multivariate Spatial Bootstrap
- Change of Support and the Volume Variance Relation
- Collocated Cokriging (see source code on GitHub)
- Conditioning by Kriging
- Choosing the Discretization Level for Block Property Estimation
- Introduction to Choosing a Kriging Plan
- Multidimensional Scaling (see source code on GitHub)
- Permanence of Ratios
- Aggregating Variables into a Super Secondary Variable