In this paper we explore the efficiency of various machine learning techniques to determine the volcano source, the eruptive formation and the eruption period of volcanic rocks when their chemical contents are known. With this aim, we assembled a data set of 9800 volcanic rocks from the open-access literature. The rocks belong to eruptive formations from Somma-Vesuvius, Campi Flegrei, Ischia and Procida volcanoes, in the Neapolitan region of Italy. The data set includes content of major oxides and trace elements, as well as Sr and Nd isotope ratios, eruptive periods, eruption formations and volcano source. Some discrete numerical variables are missing in certain samples resulting in data exclusion and measurement inhomogeneity. Our results indicate that, despite such issues, some machine learning algorithms have a very high prediction ability, i.e., at >70%. The achieved results are interesting in order to facilitate the managing of new data for volcanological reconstruction and tephrostratigraphic studies.

在本文中，我们探索了各种机器学习技术来确定火山岩的化学成分的已知来源，火山岩的喷发形成和喷发期的效率。为此，我们从开放获取文献中收集了9800个火山岩的数据集。这些岩石属于意大利那不勒斯地区的索玛-维苏威火山，坎皮·弗莱格雷火山，伊斯基亚火山和普罗奇达火山的喷发岩层。数据集包括主要氧化物和微量元素的含量，以及Sr和Nd同位素比，喷发期，喷发形成和火山源。在某些样本中缺少一些离散的数值变量，导致数据排除和测量不均匀性。我们的结果表明，尽管存在此类问题，但某些机器学习算法仍具有很高的预测能力，即 ，> 70％。为了促进火山学重建和地层学研究的新数据的管理，取得的成果很有趣。