Abstract The research for developing an automatic recognition system of volcanic microearthquakes have been an important task around the world, based on this, the aim of this paper is to present an automatic recognition system of microearthquakes from the Cotopaxi Volcano based on a deep learning approach. The detection and classification stages were carried out with Convolutional Neural Networks by using spectrograms, which were generated according to the theory of periodograms with different types of windows. In order to enable the training of neural networks with a small database (1187 microearthquakes), the Transfer Learning process was used. This system operates in quasi-realtime, which is able to process records of 20min, accordingly to the requirements of the Instituto Geofisico de la Escuela Politecnica Nacional, with a recognition (detection + classification) time response of one minute, approximately. The system performance presents an accuracy of 99% in the detection stage and an accuracy of 97% in the classification stage.

中文翻译：

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一种自动识别科托帕希火山地震火山事件的深度学习方法

摘要 开发火山微地震自动识别系统的研究一直是世界各国的重要课题，基于此，本文旨在提出一种基于深度学习的科托帕希火山微地震自动识别系统。检测和分类阶段是通过使用根据具有不同类型窗口的周期图理论生成的频谱图的卷积神经网络进行的。为了能够使用小型数据库（1187 次微地震）训练神经网络，使用了迁移学习过程。该系统准实时运行，能够处理 20 分钟的记录，相应于 Instituto Geofisico de la Escuela Politecnica Nacional 的要求，具有大约一分钟的识别（检测 + 分类）时间响应。系统性能在检测阶段表现出99%的准确率，在分类阶段表现出97%的准确率。