Introduction¶

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.

  1. 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)
  1. Basic operations using a (numerical) Data Base (Db)
  • Adding and suppressing one or several variable(s)
  • Masking samples using selection (only keep inshore information)
  1. Basic statistics
  • Summarize basic statistics as a table
  • Show (C++) documentation (interpretation of Doxygen information)

Basic Geostatistics¶

  1. 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
  1. 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)
  1. Simulations
  • The contents of this chapter is described in the Tutorial for Simulations
  • Using Turning Bands method
  • Non-conditional or conditioning to data
  1. 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