期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 17, 期 5, 页码 869-873出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2019.2931063
关键词
Feature extraction; Data models; Task analysis; Convolution; Biological system modeling; Volcanoes; Training; Classification of isolated events; deep learning; transfer learning (TL); volcano-seismic signals
类别
资金
- Ministerio de Economia y Empresa (MINECO)/Fondo Europeo de Desarrollo Regional (FEDER) [TEC2015-68752-R]
Domain-specific problems where data collection is an expensive task are often represented by scarce or incomplete data. From a machine learning perspective, this type of problems has been addressed using models trained in different specific domains as the starting point for the final objective-model. The transfer of knowledge between domains, known as transfer learning (TL), helps to speed up training and improve the performance of the models in problems with limited amounts of data. In this letter, we introduce a TL approach to classify isolated volcano-seismic signals at Volcan de Fuego, Colima (Mexico). Using the well-known convolutional architecture (LeNet) as a feature extractor and a representative data set containing regional earthquakes, volcano-tectonic earthquakes, long-period events, volcanic tremors, explosions, and collapses, our proposal compares the generalization capabilities of the models when we only fine-tune the upper layers and fine-tune overall of them. Compared with the other state-of-the-art techniques, classification systems based on TL approaches provide good generalization capabilities (attaining nearly 94% of events correctly classified) and decreasing computational time resources.
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