G2S: The GeoStatistical Server

A free and flexible multiple point (geo)statistics framework including state-of-the-art algorithms: QuickSampling and Narrow Distribution Selection

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Brief overview

The GeoStatistical Server (G2S) is a framework that allows you to use state-of-the-art Multiple Point Statistics (MPS) algorithms to run stochastic simulations.

G2S is designed to run simulations in a generic way, independently of the code used or language it is written in. For example, it enables you to run a C/C++ simulation code using Python, or Python using MATLAB (or any other combination).

Currently, the framework is provided with:

  • QuickSampling (QS) (aka. Quantile Sampling) is a general-purpose pixel-based MPS algorithm that is designed to be robust, efficient, and run in constant time. QS was designed to adapt to your problem; it can be used to do (un)conditional simulation, gap filling, or even downscaling, using continuous or categorical variables or a combination of both. The code was developed without restrictions regarding the dimensionality of the data (e.g. 1D, 2D, 3D, nD).
    • A journal article published in Geoscientific Model Development describes the method here.
  • Anchor Sampling (AS) is a location-anchored variant of QS for aligned training-image stacks. Neighborhood matching still drives the selection, but when simulating a pixel at a given coordinate, the sampled center value can only come from that exact same coordinate across the TI stack. AS supports continuous, categorical, multivariate, kernel-based, path-based, and masked-prior workflows, while exporting the selected TI id as the index image.
    • Pronunciation note: say AS like the French as (the ace of a deck) 🂡, or spell it A, S. Do not pronounce it like “ass”.
  • Narrow Distribution Selection (NDS) is an algorithm specifically targeted to simulate spectrally enhanced remote-sensed imagery. It requires an external variable (for example, a grayscale image) to control the simulation (of colors).

The framework can be easily extended to handle most codes that use gridded data. Currently, any compiled code or Python code can be handled.

For a hands-on introduction to MPS (PPT slides, Colab Notebook & recorded tutorial), follow this link.