This paper deals with the classification of volcanic activity into three classes, referred to as Quite, Strombolian and Paroxysm. The main purpose is to give a measure of the reliability with which such a classification, typically carried out by experts, can be performed by Machine Learning algorithms, by using the volcanic tremor as a feature. Both supervised and unsupervised methods are considered. It is experimentally shown that at least the Paroxysm activity can be reliably classified. Performances are rigorously assessed, in comparison with the classification made by expert volcanologists, in terms of popular indices such as the f₁-score and the Area under the ROC curve (AuC). The work is basically a case study carried out on a dataset recorded in the area of the Mt Etna volcano. However, as volcanic tremor is a geophysical signal widely available, considered methods and strategies can be easily applied to similar volcanic areas.

本文将火山活动分为三类，分别称为Quite，Strombolian和Paroxysm。主要目的是给出一种可靠性的度量，通过使用火山震颤作为特征，通常由专家执行的此类分类可以由机器学习算法执行。有监督和无监督的方法都被考虑。实验表明，至少阵发性活动可以可靠地分类。与流行的火山学家进行的分类相比，根据诸如f ₁得分和ROC曲线下面积（AuC）之类的流行指数，来对性能进行严格评估。）。这项工作基本上是对在埃特纳火山地区记录的数据集进行的个案研究。但是，由于火山震颤是一种广泛可用的地球物理信号，因此考虑的方法和策略可以轻松地应用于类似的火山地区。