Advanced techniques in the recognition and classification of seismo-volcanic events are transcendental when studying active volcanoes, not only for their importance as an accurate real time seismic monitoring procedure but also for the use of their results in modeling the dynamics of the volcanic environment. It is well known that real time seismic monitoring deals with such a large amount of data that it would become an overwhelming job for an operator to do manually. Therefore the use of automatic detection and classification techniques based on the Machine Learning approach are suitable in meeting such a challenge.

The aim of this work is to test the capability of the Deep Neural Network (DNN) by using different event parametrization as a confident classifier tool that could permit a reliable seismic catalog to be built in a new and un-analyzed volcanic scenario. We tested different configurations in order to build an approach that was as simple as possible to use this classifier with a limited number of events. In this regard, the feature space was explored in order to select the most significant parameters of the seismic signals. The data used for this analysis corresponds to the Planchon Peteroa Volcanic Complex (PPVC) located in the Transitional Southern Volcanic Zone (TSVZ) between Chile and Argentina, South America. The most significant result of this work was not only that it provided an analysis in terms of performance of this algorithm, especially when the training, validation and test dataset is reliable although definitely reduced, but it also gave an insight of into how an optimal event parametrization can significantly improve the automatic detection and classification of seismo-volcanic events.

在研究活动火山时，先进的识别和分类火山活动的技术是超验的，不仅因为它们作为准确的实时地震监测程序的重要性，而且还因为它们的结果可用于对火山环境动力学进行建模。众所周知，实时地震监测处理的数据量如此之大，以至于操作员手动进行这项工作将是一项繁重的工作。因此，基于机器学习方法的自动检测和分类技术的使用适合应对此类挑战。

这项工作的目的是通过使用不同的事件参数化作为一种​​有信心的分类器工具来测试深层神经网络（DNN）的能力，该工具可以允许在新的未经分析的火山岩场景中建立可靠的地震目录。我们测试了不同的配置，以便构建一种在事件数量有限的情况下尽可能简单地使用此分类器的方法。在这方面，探索特征空间以选择地震信号的最重要参数。用于此分析的数据对应于位于智利和南美阿根廷之间的南部火山过渡带（TSVZ）中的Planchon Peteroa火山综合体（PPVC）。这项工作最重要的结果不仅是就该算法的性能进行了分析，