The effect of chemical composition on the Raman spectra of a series of natural calcalkaline silicate glasses has been quantified by performing electron microprobe analyses and obtaining Raman spectra on glassy filaments (~450 µm) derived from a magma mingling experiment. The results provide a robust compositionally‐dependent database for the Raman spectra of natural silicate glasses along the calcalkaline series. An empirical model based on both the acquired Raman spectra and an ideal mixing equation between calcalkaline basaltic and rhyolitic end‐members is constructed enabling the estimation of the chemical composition and degree of polymerization of silicate glasses using Raman spectra. The model is relatively insensitive to acquisition conditions and has been validated using the MPI‐DING geochemical standard glasses1 as well as further samples. The methods and model developed here offer several advantages compared with other analytical and spectroscopic methods such as infrared spectroscopy, X‐ray fluorescence spectroscopy, electron and ion microprobe analyses, inasmuch as Raman spectroscopy can be performed with a high spatial resolution (1 µm2) without the need for any sample preparation as a nondestructive technique. This study represents an advance in efforts to provide the first database of Raman spectra for natural silicate glasses and yields a new approach for the treatment of Raman spectra, which allows us to extract approximate information about the chemical composition of natural silicate glasses using Raman spectroscopy. We anticipate its application in handheld in situ terrestrial field studies of silicate glasses under extreme conditions (e.g. extraterrestrial and submarine environments). © 2015 The Authors Journal of Raman Spectroscopy Published by John Wiley & Sons Ltd

26After 54 years since its last major eruption in 1961, Calbuco volcano (Ensenada, 27 Southern Chile) reawakened with few hours of warning on 22 April 2015 at 18:05 local

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Characterization, correlation and provenance determination of tephra samples in sedimentary sections (tephrochronological studies) are powerful tools for establishing ages of depositional events, volcanic eruptions, and tephra dispersion. Despite the large literature and the advancements in this research field, the univocal attribution of tephra deposits to specific volcanic sources remains too often elusive. In this contribution, we test the application of a machine learning technique named Support Vector Machine to attempt shedding new light upon tephra deposits related to one of the most complex and debated volcanic regions on Earth: the Pliocene-Pleistocene magmatism in Italy. The machine learning algorithm was trained using one of the most comprehensive global petrological databases (GEOROC); 17 chemical elements including major (SiO O 5 ) and selected trace (Sr, Ba, Rb, Zr, Nb, La, Ce) elements were chosen as input parameters. We first show the ability of support vector machines in discriminating among different Pliocene-Pleistocene volcanic provinces in Italy and then apply the same methodology to determine the volcanic source of tephra samples occurring in the Caio outcrop, an Early Pleistocene sedimentary section located in Central Italy. Our results show that: 1) support vector machines can successfully resolve high-dimensional tephrochronological problems overcoming the intrinsic limitation of two-and three-dimensional discrimination diagrams; 2) support vector machines can discriminate among different volcanic provinces in complex magmatic regions; 3) in the specific case study, support vector machines indicate that the most probable source for the investigated tephra samples is the so-called Roman Magmatic Province. These results have strong geochronological and geodynamical implications suggesting new age constraints (1.4 Ma instead of 0.8 Ma) for the starting of the volcanic activity in the Roman Magmatic Province.