In this document, you will find the list of the different files which will be browsed during this course as well as a short description.
Global presentation
What is gstlearn?
- Discover the web site
- Documentation (Application Programming Interface, Tutorials)
- Road Map (Stable version vs. Version under development). Github site
- Internal organization: C++, R, Python (accessor, graphics, …)
- Some important features: . Object language . Processing through SWIG . Memory and garbage collector . Adapting documentation (built using Doxygen from C++)
- How to install it (Rstudio, Jupyter-notebook, Terminal)
Overview of Geostatistics
The principle is to browse the different steps of a standard geostatistical study.
Get familiar with the library
The contents of this paragraph is described in the Tutorial for Db.
- Download Data Sets (using URL): we will use the 2D Scotland files.
- Pay attention on the language and platform specifics
- Other importing format (CSV, Panda Frame, Neutral File)
- Various features for accessing data
- Unique name for variables
- Discussion on locators
- Particular file organization (Grid)
- Basic operations using a (numerical) Data Base (Db)
- Adding and suppressing one or several variable(s)
- Masking samples using selection (only keep inshore information)
- Basic statistics
- Summarize basic statistics as a table
- Show (C++) documentation (interpretation of Doxygen information)
Basic Geostatistics
- Variography
- The contents of this chapter is described in the Tutorial for Variography
- Variogram cloud
- Experimental variogram: isotropic and directional variogram
- Fitting a Model: automatic version, selecting basic structures, with constraints ,…
- Variogram maps
- Estimation using Kriging
- The contents of this chapter is described in the Tutorial for Estimation
- Simple Kriging
- Ordinary Kriging (adding Universality condition)
- Produce maps for estimation and standard deviation of the estimation errors
- Neighborhood definition (unique, moving, possibility to introduce faults)
- Cross-validation (K-Fold option)
- Simulations
- The contents of this chapter is described in the Tutorial for Simulations
- Using Turning Bands method
- Non-conditional or conditioning to data
- Multivariate case
- The contents of this chapter is described in the Tutorial for Multivariate
- Define several target variables simultaneously and re-run previous steps
- Simple and cross-variograms
- Coiging
Advanced usage
How to turn gstlearn for personal usage?
- Object principle (heritage)
- Example: enhance one capability of Moving Neighborhood search
Quick overview of SPDE
- Some theory (meshing, precision matrix, sparse matrix)
- Using API for performing estimation and somulations
- Highlighting the non-stationarity: variable anisotropy
- Working in different spaces: on the sphere