A data screening approach to confirming a target mineral is chlorite using EPMA and LA-ICPMS data

Applying machine learning techniques to large datasets of in situ analyses has been proven to be a powerful tool in Earth Sciences. However, problems may arise when dealing with minerals such as chlorite, that exist as a solid solution rather than a single, stoichiometric ideal. It can be difficult to determine whether the variations in major element concentrations are due to compositional difference in the mineral of interest or due to sampling of the surrounding mineral phases in addition to the mineral of interest during the analyses. If the latter, interpretations of the results would be complicated, misled or even spurious. Here we present a method to identify chlorite based on the major and minor element content, from both LA-ICPMS and EPMA data. Further we present a dataset of 3,317 analyses of chlorite and have shown that 7.4% of these analyses include significant quantities of non-chlorite material.