Abstract One of the most challenging tasks in volcanic data analysis is the classification of seismic events. By knowing them, it is possible to take decisions in advance, providing benefits for the neighboring societies as, for instance, how such events may impact users' life and cropland areas. Although there are several approaches to perform such task, Deep Neural Networks (DNN) have been barely considered to deal with seismic signals, as discussed in our related work. In this sense, we started our research with a wide set of experiments to analyze the DNN performance while discriminating seismic activities through two common network architectures: a 2D Convolutional Neural Networks (CNN) and the Long Short-Term Memory (LSTM) network. In attempt to draw a parallel of DNN and classical supervised learning strategies, we extended our study by comparing those two architectures against the Multilayer Perceptron (MLP), which would be the simplest and most common baseline to take into account. Our MLP network was implemented after combining a basic architecture with elements from DNN models as dropout, flatten, and batch normalization layers. From this first analysis, we confirmed the need of additional neural network layers to obtain good classification results for seismic events, given the MLP required more DNN-based pipeline operations to improve its overall performance, making it comparable to the previous results obtained by our research group. As a natural extension, we designed a new DNN architecture by assessing additional network layers specifically devoted to extract features from seismic signals, thus improving the overall classification of Volcano activities involving the following events: Volcano-Tectonic (VT), Long Period (LP), Tremor (TR) and Tectonic (TC). Such design motivated the study of current DNN architectures tackling similar problems to classify raw signals from which SoundNet was taken as the most prominent candidate, thus leading to a new CNN architecture here proposed and referred to as SeismicNet. As our final contribution, SeismicNet provided classification results among the best in the literature without demanding explicit signal pre-processing steps though.

中文翻译：

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深度人工神经网络架构对地震事件分类的深入比较

摘要 火山数据分析中最具挑战性的任务之一是地震事件的分类。通过了解它们，可以提前做出决定，为邻近社会带来好处，例如，此类事件可能如何影响用户的生活和农田面积。尽管有多种方法可以执行此类任务，但正如我们在相关工作中所讨论的那样，几乎没有考虑使用深度神经网络 (DNN) 来处理地震信号。从这个意义上说，我们从一系列广泛的实验开始我们的研究，以分析 DNN 性能，同时通过两种常见的网络架构区分地震活动：二维卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络。为了将 DNN 和经典的监督学习策略进行比较，我们通过将这两种架构与多层感知器 (MLP) 进行比较来扩展我们的研究，这是要考虑的最简单和最常见的基线。我们的 MLP 网络是在将基本架构与 DNN 模型中的元素（如 dropout、flatten 和 batch normalization 层）相结合后实现的。从第一次分析中，我们确认需要额外的神经网络层才能获得良好的地震事件分类结果，因为 MLP 需要更多基于 DNN 的管道操作来提高其整体性能，使其与我们之前的研究结果相当团体。作为自然延伸，我们通过评估专门用于从地震信号中提取特征的额外网络层，设计了一种新的 DNN 架构，从而改进了涉及以下事件的火山活动的总体分类：火山构造 (VT)、长期 (LP)、震颤 (TR) 和构造 (TC)。这种设计激发了对当前处理类似问题的 DNN 架构的研究，以对原始信号进行分类，其中 SoundNet 是最突出的候选者，从而导致这里提出了一种新的 CNN 架构，称为 SeismicNet。作为我们的最后贡献，SeismicNet 提供了文献中最好的分类结果，但不需要明确的信号预处理步骤。这种设计激发了对当前处理类似问题的 DNN 架构的研究，以对原始信号进行分类，其中 SoundNet 是最突出的候选者，从而导致这里提出了一种新的 CNN 架构，称为 SeismicNet。作为我们的最后贡献，SeismicNet 提供了文献中最好的分类结果，但不需要明确的信号预处理步骤。这种设计激发了对当前处理类似问题的 DNN 架构的研究，以对原始信号进行分类，其中 SoundNet 是最突出的候选者，从而导致这里提出了一种新的 CNN 架构，称为 SeismicNet。作为我们的最后贡献，SeismicNet 提供了文献中最好的分类结果，但不需要明确的信号预处理步骤。