Llaima volcano dataset: In-depth comparison of deep artificial neural network architectures on seismic events classification

Canário, João Paulo; Mello, Rodrigo Fernandes de; Curilem, Millaray; Huenupán, Fernando; Rios, Ricardo

doi:10.1016/j.dib.2020.105627

Cited by 8 publications

(3 citation statements)

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“…We use the dataset presented by Canário et al (2020b) which encompasses 4 event types (VT, LP, Tremor and Tectonic) from 2010-2016 at Llaima Volcano, Chile. The data are from the vertical component of the seismic station LAV, with labels from Observatorio Volcanológico de los Andes del Sur (OVDAS).…”

Section: Llaima Datasetmentioning

confidence: 99%

“…Therefore, the Llaima dataset is approximately 36% smaller than the Nevado del Ruiz dataset. As described in Canário et al (2020b) the waveforms have been filtered using a band-pass filter to include frequencies between 1 and 10 Hz and subsequently normalised by the maximum value. Each input waveform is 64 s long, and shorter waveforms are padded with 0 values to reach this length.…”

Section: Llaima Datasetmentioning

confidence: 99%

“…Brittle triggering event followed by conduit resonance Nevado del Ruiz Lahr et al (1994), Neuberg et al (2000), Neuberg et al (2006), Harrington and Brodsky, (2007), Rodgers et al (2016) Tremor 1-12 Hz Extended magma movement, explosions Llaima Julian, (1994), Baptie et al (2002), Neuberg et al (2000), Hellweg, (2000), Jellinek and Bercovici, (2011) Tectonic Over 5 Hz Non-volcanic brittle failure events e.g., local or regional seismic events Llaima Marzocchi et al (1993), La Femina et al (2004, Canário et al, (2020b) program for volcano-seismic analysis and classification via a model pre-trained on events from both Mount St Helens and Bezymianny volcanoes. However, no analysis of the potential for active learning to reduce the size of the training set in the field of volcano-seismic event classification has yet been done.…”

Section: Introductionmentioning

confidence: 99%

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A Deep Active Learning Approach to the Automatic Classification of Volcano-Seismic Events

et al. 2022

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Volcano-seismic event classification represents a fundamental component of volcanic monitoring. Recent advances in techniques for the automatic classification of volcano-seismic events using supervised deep learning models achieve high accuracy. However, these deep learning models require a large, labelled training dataset to successfully train a generalisable model. We develop an approach to volcano-seismic event classification making use of active learning, where a machine learning model actively selects the training data which it learns from. We apply a diversity-based active learning approach, which works by selecting new training points which are most dissimilar from points already in the model according to a distance-based calculation applied to the model features. We combine the active learning with an existing volcano-seismic event classifier and apply the model to data from two volcanoes: Nevado del Ruiz, Colombia and Llaima, Chile. We find that models with data selected using an active learning approach achieve better testing accuracy and AUC (Area Under the Receiver Operating Characteristic Curve) than models with data selected using random sampling. Additionally, active learning decreases the labelling burden for the Nevado del Ruiz dataset but offers no increase in performance for the Llaima dataset. To explain these results, we visualise the features from the two datasets and suggest that active learning can reduce the quantity of labelled data required for less separable data, such as the Nevado del Ruiz dataset. This study represents the first evaluation of an active learning approach in volcano-seismology.

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Section: Llaima Datasetmentioning

confidence: 99%

Section: Llaima Datasetmentioning

confidence: 99%