MIST is a computational algorithm designed to filter geochemical analyses and identify observations with stoichiometric elemental ratios that match real mineral compositions. The algorithm uses normalized oxide percentages and stoichiometric ratios between elements in a detailed decision-tree approach to identify mineral phases.
Mineral species are recognized in five steps, first as observations that fit in mineral classes, then mineral groups, sub-groups, sub-sub-groups and species. Mixtures of phases or chemical ratios that would not fit a real mineral structure are identified as mixtures. When an observation matches a single mineral we output the name of mineral according to IMA rules and a stoichiometric mineral formula.
The algorithm has been tested on well over 2000 EPMA mineral compositions and currently identifies 150 mineral species with >95% accuracy.
Mineral species are recognized in five steps, first as observations that fit in mineral classes, then mineral groups, sub-groups, sub-sub-groups and species. Mixtures of phases or chemical ratios that would not fit a real mineral structure are identified as mixtures. When an observation matches a single mineral we output the name of mineral according to IMA rules and a stoichiometric mineral formula.
The algorithm has been tested on well over 2000 EPMA mineral compositions and currently identifies 150 mineral species with >95% accuracy.