In this post I will discuss an implementation of sequential Gaussian simulation (SGS) from the field of geostatistics. Geostatistics is simply a statistical consideration of spatially distributed data. Sequential Gaussian simulation is a technique used to “fill in” a grid representing the area of interest using a smattering of observations, and a model of the observed trend. The basic workflow incorporates three steps:
- Modeling the measured variation using a semivariogram
- Using the semivariogram to perform interpolation by kriging
- Running simulations to estimate the spatial distribution of the variable(s) of interest
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