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.
- 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).
- A paper describing it is available here: Analogue-based colorization of remote sensing images using textural information.
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.