A Contribution to Deep Learning Approaches for Automatic Classification of Volcano-Seismic Events: Deep Gaussian Processes

Miguel Lopez-Perez; Luz Garcia; Carmen BenitezRafael Molina

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A Contribution to Deep Learning Approaches for Automatic Classification of Volcano-Seismic Events: Deep Gaussian Processes

机译：A Contribution to Deep Learning Approaches for Automatic Classification of Volcano-Seismic Events: Deep Gaussian Processes

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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.

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《IEEE Transactions on Geoscience and Remote Sensing》 |2021年第5期|3875-3890|共16页
作者
Miguel Lopez-Perez; Luz Garcia; Carmen BenitezRafael Molina;
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作者单位

Department of Signal Theory, Telematics and Communications, University of Granada, Granada, Spain;

Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;

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正文语种英语
中图分类地球物理学;遥感技术;
关键词
Global Positioning System; Databases; Volcanoes; Machine learning; Gaussian processes; Registers; Task analysis; Deep Gaussian processes (DGPs); deep learning (DL); Gaussian processes (GPs); geophysical signal processing; geoscience and remote sensing; remote monitoring; remote sensing; signal processing; volcanic activity; volcanoes;

A Contribution to Deep Learning Approaches for Automatic Classification of Volcano-Seismic Events: Deep Gaussian Processes

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