Geostatistical analysis in mining is a powerful statistical methodology used to analyze spatially correlated data, such as mineral grades and thicknesses, to improve the accuracy of mineral resource estimation and reduce uncertainty.
It takes advantage of spatial autocorrelation the principle that data points closer in space are more related to model and predict the distribution of mineral resources in three dimensions.
Key concepts in geostatistical analysis include:
- Regionalized variables: Variables that vary spatially, like ore grade.
- Variogram: A graphical tool that quantifies spatial correlation, showing how similarity between data points changes with distance.
- Kriging: A widely used geostatistical interpolation method that predicts values at unsampled locations based on spatial correlation, providing optimal linear estimates.
- Conditional simulation: Generates multiple plausible realizations of spatial data to assess uncertainty and risk.
Applications of geostatistics in mining include resource estimation and classification, mine planning and optimization, grade control, and quality assurance. It helps distinguish resource categories (measured, indicated, inferred), optimize drilling patterns and mine design, and improve ore grade prediction and control(Lee, n.d.).
Reference:
Lee, S. (n.d.). Mastering Geostatistical Mining Techniques. Retrieved August 7, 2025, from https://www.numberanalytics.com/blog/ultimate-guide-mining-geostatistics
