Volcano-Seismic Transfer Learning and Uncertainty Quantification With Bayesian Neural Networks



Bueno, Angel, Benitez, Carmen, De Angelis, Silvio ORCID: 0000-0003-2636-3056, Diaz Moreno, Alejandro and Ibanez, Jesus M
(2020) Volcano-Seismic Transfer Learning and Uncertainty Quantification With Bayesian Neural Networks. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 58 (2). pp. 892-902.

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Abstract

Over the past few years, deep learning (DL) has emerged as an important tool in the fields of volcano and earthquake seismology. However, these methods have been applied without performing thorough analyses of the associated uncertainties. Here, we propose a solution to enhance volcano-seismic monitoring systems, through probabilistic Bayesian DL; we implement and demonstrate a workflow for waveform classification, rapid quantification of the associated uncertainty, and link these uncertainties to changes in volcanic unrest. Specifically, we introduce Bayesian neural networks (BNNs) to perform event identification, classification, and their estimated uncertainty on data gathered at two active volcanoes, Mount St. Helens, Washington, USA, and Bezymianny, Kamchatka, Russia. We demonstrate how BNNs achieve excellent performance (92.08%) in discriminating both the type of event and its origin when the two data sets are merged together, and no additional training information is provided. Finally, we demonstrate that the data representations learned by the BNNs are transferable across different eruptive periods. We also find that the estimated uncertainty is related to changes in the state of unrest at the volcanoes and propose that it could be used to gauge whether the learned models may be exported to other eruptive scenarios.

Item Type: Article
Uncontrolled Keywords: Uncertainty, Volcanoes, Bayes methods, Earthquakes, Neural networks, Seismology, Probabilistic logic, Geophysics computing, neural networks, seismology, uncertainty, volcanoes
Depositing User: Symplectic Admin
Date Deposited: 04 Sep 2019 15:14
Last Modified: 15 Mar 2024 08:47
DOI: 10.1109/TGRS.2019.2941494
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3053436