Discriminating Tectonic Setting of Igneous Rocks Using Biotite Major Element chemistry−A Machine Learning Approach

Abstract

Abstract The composition of igneous biotite is a potential indicator of the geologic environment of its host rock. Here, we apply two machine learning models−eXtreemly Greedy tree Boosting (XGBoost) and light gradient boosting machine (LightGBM) to classify biotite in igneous rocks from five tectonic settings−oceanic intraplate, continental intraplate, continental arc, island arc, and rift, using their major element chemistry compiled from a global dataset. The two models are successfully able to discriminate among biotite from the five tectonic settings. The classifiers quantitatively search for unique geochemical signatures in the training dataset, mapping each biotite analysis to its corresponding tectonic setting. Both models yielded good classification accuracy (∼90% on average) on an unseen dataset, suggesting that biotite major element chemistry can successfully discriminate the tectonic setting of many igneous rocks. The Shapley Additive exPlanations algorithm, which measures the impact of every element, indicates that Na, Mn, Ba, Mg, Al, Cr, and O2− constitute important geochemical discriminators. According to both models, high Na, low Na, low Ba, high Mn, and low Mn of biotite have the strongest influence on the prediction of continental arc, continental intraplate, island arc, oceanic intraplate, and rift settings, respectively. The classifier models have been applied to investigate the Neoproterozoic geodynamics of the Aravalli‐Delhi Belt, northwestern India. The models show that igneous activity related to the Erinpura granites (∼835 Ma) and the Malani Igneous Suite (∼750 Ma) can be ascribed to continental intraplate setting related to extensional tectonics prior to the break‐up of the Rodinia supercontinent.

Keywords

• tectonic setting discrimination
• biotite
• machine learning method
• supervised classification
• principal component analysis
• feature importance