@misc{10481/96104,
year = {2024},
month = {7},
url = {https://hdl.handle.net/10481/96104},
abstract = {Monitoring continuous volcano-seismic signals is often
performed by systems trained on scarce or incomplete datasets.
From a machine learning perspective, these types of systems are
typically built by learning from seismic records containing information
not only on the volcanic dynamics, but also on the complex
inner structure of the volcanic edifice. The dual nature of the
information content presents a challengewhen it comes to modeling
events temporally. Here, we show that while existing recurrent-neural-network-based monitoring systems recognize almost 90%
of events annotated in seismic catalogs, the long-range temporal
dependencies are still hard to model. We found that dilated recurrent
neural networks based on multiresolution dilated recurrent
skip connections between layers have the remarkable capability to
simultaneously enhance the efficiency of the model, reducing the
number of training parameters, while increasing the performance
of the model when compared with classical recurrent neural networks
in sequencemodeling tasks involving very long-term seismic
records. Our results offer the potential to increase the reliability of
monitoring tools despite the variations in the geophysical properties
of the seismic events within the volcano across eruptive periods.},
organization = {IMPROVE under
GrantH2020-MSCA-ITN-2019-85809},
organization = {Spanish Project PID2022-143083NB-100 funded by MCIN/AEI/10.13039/501100011033 and FEDER
(EU) “Una manera de hacer Europa”},
organization = {Spanish Project PLEC2022-009271 “DigiVolCan,” funded byMCIN/AEI/10.13039/501100011033 and EU
NextGenerationEU/PRTR},
organization = {Spanish Grant TED2021-132178BI00
funded by MCIN/AEI/10.13039/501100011033 and “European Union
NextGenerationEU/PRTR”},
organization = {Junta de Andalucía-Consejería de
Universidad, Investigacion e Innovacion under Grant P21_00051},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
keywords = {Deep learning},
keywords = {Dilated recurrent neural networks (Dilated-RNNs)},
keywords = {Transfer learning (TL)},
title = {Dilated-RNNs: A Deep Approach for Continuous Volcano-Seismic Events Recognition},
doi = {10.1109/JSTARS.2024.3421921},
author = {Titos Luzón, Manuel Marcelino and Carthy, Joe and García Martínez, María Luz and Barnie, Talfan and Benítez Ortúzar, María Del Carmen},
}