The automatic classification of volcano-seismic events is a key problem in volcanology. Due to its complexity, deep learning (DL) techniques have become the tool of choice for this problem, outperforming classical classifiers. The main drawback of this approach, when applied to the classification of volcano-seismic events, is its tendency to overfit because of the small-size available databases. In this work, we propose and analyze the use of the Gaussian processes (GPs) and Deep GPs (DGPs), and their hierarchical extension, for volcano-seismic event classification. We empirically prove the adequacy of the proposed modeling with an insightful and exhaustive comparison with state-of-the-art DL-based methods on a seismic database recorded at “Volcán de Fuego,” Colima, Mexico. The hierarchical structure of DGPs and the reduced number of parameters to be automatically estimated become essential to achieve excellent performance even on small databases, capturing well the complex patterns of seismic signals for all classes and, in particular, for those that have been hardly observed.

中文翻译：

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火山地震事件自动分类的深度学习方法的一个贡献：深度高斯过程

火山地震事件的自动分类是火山学中的关键问题。由于其复杂性，深度学习（DL）技术已成为解决此问题的首选工具，胜过传统分类器。当将这种方法应用于火山地震事件的分类时，其主要缺点是由于数据库的规模小，其过拟合的趋势。在这项工作中，我们提出并分析了高斯过程（GPs）和深部GPs（DGPs）的使用以及它们的层次扩展，用于火山地震事件分类。我们通过在墨西哥Colima的“Volcánde Fuego”记录的地震数据库上与基于DL的最新技术进行有见地且详尽的比较，以经验证明了所提出模型的适当性。