Paper Info
Title
Detection and Classification of Continuous Volcano-Seismic Signals With Recurrent Neural Networks
Abstract
This paper introduces recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) to detect and classify continuous sequences of volcano-seismic events at the Deception Island Volcano, Antarctica. A representative data set containing volcano-tectonic earthquakes, long-period events, volcanic tremors, and hybrid events was used to train these models. Experimental results show that RNN, LSTM, and GRU can exploit temporal and frequency information from continuous seismic data, attaining close to 90%, 94%, and 92% events correctly detected and classified. A second experiment is presented in this paper. The architectures described above, trained with data from campaigns of seismic records obtained in 1995–2002, have been tested with data from the recent seismic survey performed at the Deception Island Volcano in 2016–2017 by the Spanish Antarctic scientific campaign. Despite the variations in the geophysical properties of the seismic events within the volcano across eruptive periods, results provide good generalization accuracy. This result expands the possibilities of RNNs for real-time monitoring of volcanic activity, even if seismic sources change over time.
Year
DOI
Venue
2019
10.1109/tgrs.2018.2870202
IEEE Transactions on Geoscience and Remote Sensing
Keywords
Field
DocType
Logic gates,Volcanoes,Hidden Markov models,Recurrent neural networks,Computational modeling,Earthquakes,Data models
Computer vision,Data modeling,Volcano,Pattern recognition,Deception,Recurrent neural network,Exploit,Artificial intelligence,Hidden Markov model,Mathematics
Journal
Volume
Issue
ISSN
57
4
0196-2892
Citations 
PageRank 
References 
1
0.35
0
Authors
5
Name
Order
Citations
PageRank
Manuel Titos152.57
Ángel Bueno241.29
Luz García3639.48
M. Carmen Benítez430325.05
Jesús Ibáñez5203.91