: Comparing timed mean grade/recovery curves and performing regression analysis to establish relationships between variables.
Used to check for normality. Grade distributions are often log-normal rather than normal (Gaussian). Statistical Methods For Mineral Engineers
by Professor is widely considered the definitive practical guide for metallurgists and plant engineers. Core Focus and Utility : Comparing timed mean grade/recovery curves and performing
Geostatistics is arguably the most influential statistical discipline in mineral engineering, providing the mathematical framework to estimate the grade and tonnage of a mineral deposit from a limited set of drill core samples. Modern geostatistical methods, which account for spatial correlations, have become commonplace in quantitative resource assessment. by Professor is widely considered the definitive practical
: Tim Napier-Munn’s 50 years of industry experience, including co-authoring the famous Wills' Mineral Processing Technology , lends the book significant professional weight.
These techniques are used to simultaneously model multiple grade variables (e.g., lead, zinc, copper, iron) and their mineralogical associations. A proper method requires a log-ratio transformation to handle the compositional nature of the data (where components sum to 100%), before applying cokriging for estimation.