Geostatistics Lessons is an open disclosure of some guidance in geostatistical modeling. These Python notebooks and data are prepared by Lesson authors and associated contributors to supplement the Lessons. As new Lessons are authored and notebooks created, this repository will be updated.
Lessons with notebooks and data available include:
- An Application of Bayes Theorem to Geostatistical Mapping (notebook and lesson), Jared Deutsch and Clayton Deutsch, 2018
- Multidimensional Scaling (notebook and lesson), Steven Mancell and Clayton Deutsch, 2019
- Collocated Cokriging (notebook and lesson), Matthew Samson and Clayton Deutsch, 2020
- The Nugget Effect (notebook and lesson), Eric Daniels and Diogo Silva, 2024
- The Pairwise Relative Variogram (notebook and lesson), Haoze Zhang and Ryan Barnett, 2024
- Introduction to Choosing a Kriging Plan (notebook and lesson), James Eke and Ryan Barnett, 2024
- Trend Modeling and Modeling with a Trend (notebook and lesson), Sebastian Sanchez, Ben Harding, and Ryan Barnett, 2024
Notebooks are implemented in Python using the scientific python stack (NumPy, Pandas, Matplotlib, ...). Refer to the individual notebooks for any particular dependencies.
Some notebooks leverage the Resource Modeling Solutions Platform (RMSP), a geostatistical modeling package that provides commercial and academic licenses. These notebooks augment the Lesson, but the interested reader is free to implement in any geostatistical modeling software they are interested in using.
Notebooks are licensed under the MIT license separately from the Lessons. Refer to Geostatistics Lessons for licensing information on the Lessons.