Local Infrasound Monitoring of Lava Eruptions at Nyiragongo Volcano (D.R. Congo) Using Urban and Near‐Source Stations

Barrière, Julien; Oth, Adrien; d’Oreye, Nicolas; Subira, Josué; Smittarello, Delphine; Brenot, Hugues; Theys, Nicolas; Smets, Benoît

doi:10.1029/2023gl104664

Cited by 2 publications

(1 citation statement)

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“…Tracking seismic network coherence (Seydoux et al, 2016;Soubestre et al, 2018) has also shown considerable recent success in identifying tremor at several volcanic settings (Journeau et al, 2022;Maher et al, 2023). Acoustic tremor, on the other hand, has been primarily detected and monitored using array processing techniques (Bishop et al, 2020;Matoza et al, 2010), cross-correlation functions on locally deployed acoustic sensors (Barrière et al, 2023), or statistical tests on successive spectral windows (Luo et al, 2023). Ripepe et al (2007) also demonstrated the detection of acoustic tremor from Stromboli Volcano, Italy, using multi-channel semblance analyses (Ripepe & Marchetti, 2002), while Johnson and Palma (2015) used a combination of cross-correlation lag times and semblance analyses on array data to study infrasonic tremor from lahars at Volcán Villarrica, Chile.…”

Section: Introductionmentioning

confidence: 99%

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: Introductionmentioning

confidence: 99%

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

Tan,

Fee,

Witsil

et al. 2024

JGR Solid Earth

View full text Add to dashboard Cite

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Seismo‐Acoustic Characterization of the 2018 Sierra Negra Caldera Resurgence and Fissure Eruption in the Galápagos Islands

Ortiz,

Matoza,

Bernard

et al. 2024

JGR Solid Earth

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The 2018 eruption of Sierra Negra volcano, Galápagos, Ecuador has provided new insights into the mechanisms of caldera resurgence, subsidence, and fissuring at basaltic shield volcanoes. Here, we integrate local (∼0.4 km) seismo‐acoustic records and regional (∼85 km) infrasound array data to present new observations of the 2018 Sierra Negra eruption with improved time and spatial resolutions. These observations include: air‐to‐ground coupling ∼2 hr before the time of the eruption onset, migration of the infrasound tremor from 22:54 June 26 to 12:31 June 27 UT (all times in UT), and persistent infrasound detections during the weeks between 5 July and 18 August from an area that does not coincide with the previously documented eruptive fissures. We interpret air‐to‐ground coupling as infrasound tremor generated in the nearby fissures before the main eruptive phase started, although ambiguity remains in interpreting a single seismic‐infrasonic sensor pair. The progressive location change of the infrasound tremor agrees with the migration of the eruption down the north flank of Sierra Negra at a rate of ∼0.15 ± 0.04 m/s. The weeks‐long persistent detections coincide with a region that has thermal anomalies, co‐eruptive deformation, lava fields, and geological features that could be interpreted as multiple lava tube skylights. Our observations and interpretations provide constraints on the mechanisms underlying fissure formation and magma emplacement at Sierra Negra.

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Local Infrasound Monitoring of Lava Eruptions at Nyiragongo Volcano (D.R. Congo) Using Urban and Near‐Source Stations

Cited by 2 publications

References 50 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

Seismo‐Acoustic Characterization of the 2018 Sierra Negra Caldera Resurgence and Fissure Eruption in the Galápagos Islands

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