Geostatistical Analysis
Definition
Geostatistical analysis applies statistics to spatially correlated data, modeling how similarity decays with distance and direction. Core tools include variography, kriging, stochastic simulation, and uncertainty mapping. By explicitly modeling spatial structure, geostatistics delivers better predictions and realistic confidence than naive smoothing.
Application
Mining estimates ore grades, environmental science maps contaminants, and meteorology infers surfaces from sparse sensors. The approach supports both interpolation and risk assessment from limited samples.
FAQ
What is a variogram and why care?
It quantifies how variance grows with separation, revealing range, sill, and nugget. A good variogram is the backbone of kriging performance.
How do anisotropies enter the model?
Directional variograms show different ranges by azimuth. You can rotate and scale coordinates so kriging respects elongated trends like valleys.
Can geostatistics handle non‑Gaussian data?
Yes—transformations (log, normal score) and indicator kriging handle skewed or categorical variables, with back‑transforms for interpretation.
What about uncertainty maps?
Kriging variance or simulation ensembles provide uncertainty surfaces that guide sampling and risk‑aware decision making.