Deep learning and multi-station classification of volcano-seismic events of the Nevados del Chillán volcanic complex (Chile)

Ferreira, Alejandro; Curilem, Millaray; Gomez, Walter; Rios, Ricardo

doi:10.1007/s00521-023-08994-z

Cited by 3 publications

(6 citation statements)

References 23 publications

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“…The VOISS‐Net CNN architecture layer sequence follows a similar structure from earlier studies that have applied CNNs to seismic spectrograms (Ferreira et al., 2023; Kong et al., 2022; Linville et al., 2019; Mousavi et al., 2019; Rouet‐Leduc et al., 2020). We note that Mousavi et al.…”

Section: Methodsmentioning

confidence: 99%

“…The VOISS-Net CNN architecture layer sequence follows a similar structure from earlier studies that have applied CNNs to seismic spectrograms (Ferreira et al, 2023;Kong et al, 2022;Linville et al, 2019;Mousavi et al, 2019;Rouet-Leduc et al, 2020). We note that Mousavi et al ( 2019 2023) utilized a unique approach of concatenating convolutional outputs from multiple stations prior to classifying the type of volcano-seismic signal (i.e., early integration).…”

Section: Model Architecture and Trainingmentioning

confidence: 99%

“…CNNs, which are commonly employed in computer vision (Khan et al., 2018), are also being applied to seismic data. For example, they are used to identify seismic phase arrivals (Lapins et al., 2021; Mousavi et al., 2020; Zhu & Beroza, 2019), classify volcano‐seismic signals (Ferreira et al., 2023; Titos et al., 2018), and differentiate anthropogenic explosions from earthquakes (Kong et al., 2022; Linville et al., 2019). Although CNNs have been used on audible acoustics (Hershey et al., 2017), their application to infrasound, or specifically volcanic infrasound, has been limited.…”

Section: Introductionmentioning

confidence: 99%

“…Although CNNs have been used on audible acoustics (Hershey et al., 2017), their application to infrasound, or specifically volcanic infrasound, has been limited. Prior work in seismoacoustics has also mostly focused on applying machine learning algorithms to classify cataloged events (Ferreira et al., 2023; Kong et al., 2022; Linville et al., 2019; Titos et al., 2018) rather than on continuous data. The application of our models to continuous data opens up the possibility of detecting volcanic tremor in near real‐time in addition to characterizing pre‐existing data.…”

Section: Introductionmentioning

confidence: 99%

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Detection and Characterization of Seismic and Acoustic Signals at Pavlof Volcano, Alaska, Using Deep Learning

Tan,

Fee,

Witsil

et al. 2024

JGR Solid Earth

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Volcanic tremor is a semi‐continuous seismic and/or acoustic signal that occurs at time scales ranging from seconds to years, with variable amplitudes and spectral features. Tremor sources have often been related to fluid movement and degassing processes, and are recognized as a potential geophysical precursor and co‐eruptive geophysical signal. Eruption forecasting and monitoring efforts need a fast, robust method to automatically detect, characterize, and catalog volcanic tremor. Here we develop VOlcano Infrasound and Seismic Spectrogram Network (VOISS‐Net), a pair of convolutional neural networks (one for seismic, one for acoustic) that can detect tremor in near real‐time and classify it according to its spectral signature. Specifically, we construct an extensive data set of labeled seismic and low‐frequency acoustic (infrasound) spectrograms from the 2021–2022 eruption of Pavlof Volcano, Alaska, and use it to train VOISS‐Net to differentiate between different tremor types, explosions, earthquakes and noise. We use VOISS‐Net to classify continuous data from past Pavlof Volcano eruptions (2007, 2013, 2014, 2016, and 2021–2022). VOISS‐Net achieves an 81.2% and 90.0% accuracy on the seismic and infrasound test sets respectively, and successfully characterizes tremor sequences for each eruption. By comparing the derived seismoacoustic timelines of each eruption with the corresponding eruption chronologies compiled by the Alaska Volcano Observatory, our model identifies changes in tremor regimes that coincide with observed volcanic activity. VOISS‐Net can aid tremor‐related monitoring and research by making consistent tremor catalogs more accessible.

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Section: Methodsmentioning

confidence: 99%

Section: Model Architecture and Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection and Characterization of Seismic and Acoustic Signals at Pavlof Volcano, Alaska, Using Deep Learning

Tan,

Fee,

Witsil

et al. 2024

JGR Solid Earth

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“…Such a multi-variate time series, in which there are unknown interactions between variables, is ideally suited to a machine learning (e.g., Malfante et al, 2018;Carniel and Guzmán, 2020;Watson et al, 2020). In particular, application of a deep learning-based approach to time series data can greatly aid in advancing volcano monitoring, and processing of volcanic geophysical and geochemical signals, especially when searching for patterns and interrelations in multi-sensor time series (e.g., Manley et al, 2022;Corradino et al, 2023;Ferreira et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

External factors driving surface temperature changes above geothermal systems: answers from deep learning

Giannoulis,

Pailot-Bonnétat,

Barra

et al. 2024

Front. Earth Sci.

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Introduction: The surface expression of enhanced geothermal heat fluxes above an active hydrothermal system causes a surface thermal anomaly (ΔT). The thermal anomaly is expressed by the difference between the temperature within the heated zone (Th) and the temperature of non-heated surfaces (T0). Given that the resulting thermal anomaly at the surface is of extremely low magnitude (1°C–5°C at Vulcano, Italy), it is extremely sensitive to overprinting by external factors, namely, meteorological influences on surface temperature variation, such as solar heating, wind and rain.Methods: To test the sensitivity of the surface to external drivers, we installed two surface temperature measurement stations within the Vulcano’s Fossa crater, one inside the thermal anomaly and one outside (separation = 50 m), with a weather station co-located with the T0 station. Time series of Th and T0 were collected for 2020, when the Vulcano Fossa hydrothermal system was at a low and stable level of activity so that external drivers would have been the main influences on Th and T0, and hence also ΔT. To test for divergence from normality in terms of diurnal and seasonal variations in Th and T0, and the role of external factors in causing abnormality, we used the deep learning engine DITAN: a domain-agnostic framework to detect and interpret anomalies in time-series data.Results: During the year, DITAN found 16 cases of two types of meteorological events: intense low-pressure systems and high-intensity rainstorms (cloudbursts). Passage of 13 abnormal low-pressure systems were detected (10 between February and May, and three in December), with three abnormal rainstorm events (all in December); all three being coincident with the abnormal low pressure events. We find just two abnormalities in the time series for of Th and T0, both of which coincide with passage of abnormal low-pressure systems, and neither of which coincide with abnormal rain events. We conclude that diurnal and annual heating and cooling cycles, subject to normal meteorological inputs and at a surface above a geothermal-heated source, are immune to anomalous behaviour to the external (meteorological) variations.

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Improving volcanic event recognition through new seismic signals representation

Curilem,

Novoa,

Saldías

et al. 2024

2024 IEEE Latin American Conference on Computational Intelligence (LA-CCI)

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Deep learning and multi-station classification of volcano-seismic events of the Nevados del Chillán volcanic complex (Chile)

Cited by 3 publications

References 23 publications

Detection and Characterization of Seismic and Acoustic Signals at Pavlof Volcano, Alaska, Using Deep Learning

Detection and Characterization of Seismic and Acoustic Signals at Pavlof Volcano, Alaska, Using Deep Learning

External factors driving surface temperature changes above geothermal systems: answers from deep learning

Improving volcanic event recognition through new seismic signals representation

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