Abstract
This work aimed to study geochemical data, composed of major and trace elements describing volcanic rocks collected from the Campanian active volcanoes of Vesuvius, Campi Flegrei and Ischia Island. The data were analyzed through the Self-Organizing Map (SOM) unsupervised neural net. SOM is able to group the input data into clusters according to their intrinsic similarities without using any information derived from previous geochemical-petrological considerations. The net was trained on a dataset of 276 geochemical patterns of which 96 belonged to Ischia, 94 to Vesuvius and 86 to Campi Flegrei volcanoes. Two investigations were carried out. The first one aimed to cluster geochemical data mainly characterizing the type of volcanic rocks of the three volcanic areas. The SOM clustering well grouped the oldest volcanic products of Ischia, Vesuvius and Campi Flegrei identifying a similar behaviour for the rocks emplaced in the oldest activity periods (>19 ka), and showing their different evolution over time. In the second test, devoted to inferring information on the magmatic source, the ratios of significant trace elements and K2O/Na2O have been used as input data. The SOM results highlighted a high degree of affinity between the geochemical element ratios of Vesuvius and Campi Flegrei that were separated from the products of Ischia. This result was also evidenced through isotope ratios by using traditional two-dimensional diagrams.
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Esposito, A.M., Alaia, G., Giudicepietro, F., Pappalardo, L., D’Antonio, M. (2021). Unsupervised Geochemical Analysis of the Eruptive Products of Ischia, Vesuvius and Campi Flegrei. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_17
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