Garnet major-element composition as an indicator of host-rock type: a machine learning approach using the random forest classifier

The major-element chemical composition of garnet provides valuable petrogenetic information about its primary host lithologies, particularly in metamorphic rocks. When facing detrital garnet, information about the bulk composition and mineral paragenesis of the initial garnet-bearing host rock is absent. This prevents the application of chemical thermobarometric techniques and calls for quantitative empirical approaches. Here we present a new garnet host-rock discrimination scheme that is based on a random forest machine-learning algorithm trained on a large dataset of 13,615 garnet analyses that covers a wide variety of garnet-bearing lithologies. Considering the out-of-bag error, the scheme correctly predicts the original garnet host-rock in (i) >95 % concerning the setting, that is mantle versus metamorphic versus igneous versus metasomatic; (ii) >84 % concerning the metamorphic facies, that is blueschist/greenschist versus amphibolite versus granulite versus eclogite/ultrahigh-pressure; and (iii) >93 % concerning the host-rock composition, that is intermediate–felsic/metasedimentary versus mafic versus ultramafic versus alkaline versus calcsilicate. The wide coverage of potential host rocks, the detailed prediction classes, the high discrimination rates, and the successfully tested real-case applications demonstrate that the introduced scheme overcomes many issues related to previous schemes. This highlights the potential of transferring the applied discrimination strategy to the broad range of detrital minerals beyond garnet, as well as many other quantitative empirical challenges in Earth sciences. For easy and quick usage, a freely accessible web app is provided that guides the user in five steps from garnet composition to prediction results including data visualization.