Paper Info
Title
Classification of Isolated Volcano-Seismic Events Based on Inductive Transfer Learning
Abstract
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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“Volcán de Fuego”</italic> , 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.
Year
DOI
Venue
2020
10.1109/LGRS.2019.2931063
IEEE Geoscience and Remote Sensing Letters
Keywords
DocType
Volume
Feature extraction,Data models,Task analysis,Convolution,Biological system modeling,Volcanoes,Training
Journal
17
Issue
ISSN
Citations 
5
1545-598X
1
PageRank 
References 
Authors
0.35
0
5
Name
Order
Citations
PageRank
Manuel Titos152.57
Ángel Bueno241.29
Luz García3639.48
Carmen Benítez411.71
J. C. Segura520122.54