Solving petrological problems through machine learning: the study case of tectonic discrimination using geochemical and isotopic data

Machine-learning methods are evaluated to study the intriguing and debated topic of discrimination among different tectonic environments using geochemical and isotopic data. Volcanic rocks characterized by a whole geochemical signature of major elements (SiO2, TiO2, Al2O3, Fe2O3T, CaO, MgO, Na2O, K2O), selected trace elements (Sr, Ba, Rb, Zr, Nb, La, Ce, Nd, Hf, Sm, Gd, Y, Yb, Lu, Ta, Th) and isotopes (206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb, 87Sr/86Sr and 143Nd/144Nd) have been extracted from open-access and comprehensive petrological databases (i.e., PetDB and GEOROC). The obtained dataset has been analyzed using support vector machines, a set of supervised machine-learning methods, which are considered particularly powerful in classification problems. Results from the application of the machine-learning methods show that the combined use of major, trace elements and isotopes allows associating the geochemical composition of rocks to the relative tectonic setting with high classification scores (93 %, on average). The lowest scores are recorded from volcanic rocks deriving from back-arc basins (65 %). All the other tectonic settings display higher classification scores, with oceanic islands reaching values up to 99 %. Results of this study could have a significant impact in other petrological studies potentially opening new perspectives for petrologists and geochemists. Other examples of applications include the development of more robust geothermometers and geobarometers and the recognition of volcanic sources for tephra layers in tephro-chronological studies.