  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 allow 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 geo-thermometers and geo-barometers and the
recognition of volcanic sources for tephra layers in tephro-chronological
studies.
