Unveiling the pre-eruptive seismic series of the La Palma 2021 eruption: Insights through a fully automated analysis

Suarez, E.D.; Domínguez-Cerdeña, I.; Villaseñor, A.; Aparicio, S. Sainz-Maza; del Fresno, C.; García-Cañada, L.

doi:10.1016/j.jvolgeores.2023.107946

Cited by 4 publications

(2 citation statements)

References 41 publications

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“…In addition, both the performances on LPs and VTs decrease with signal-to-noise ratio, and these models show lower performances for LPs than for VTs at the same signal-to-noise ratio (Figure S15 in Supporting Information S1). Our results suggest that these existing models will likely underreport LPs compared to VTs when directly applied to monitoring volcano seismicity (Bannister et al, 2022;Garza-Girón et al, 2023;Mittal et al, 2022;Suarez et al, 2023;Wilding et al, 2023), which is not ideal since LPs often indicate fluid/magma movements (Chouet & Matoza, 2013;Matoza & Roman, 2022;Song et al, 2023). Therefore, we decided it would be valuable to train a new phase picker specifically for volcano seismicity.…”

Section: Geophysical Research Lettersmentioning

confidence: 99%

“…However, compared to VTs, LPs are more difficult to detect and catalog because they are depleted of high frequency content and have emergent phase onsets (Pitt et al, 2002;Shapiro et al, 2017), which also hinders the study of their source mechanisms. Some recent studies have applied existing deep-learning phase pickers, which were trained using regular tectonic earthquake waveforms, to monitor volcano seismicity (e.g., Bannister et al, 2022;Garza-Girón et al, 2023;Mittal et al, 2022;Retailleau et al, 2022;Suarez et al, 2023;Wilding et al, 2023). However, there is currently no large-scale, systematic evaluation of the efficacy of these existing models for volcano monitoring.…”

Section: Introductionmentioning

confidence: 99%

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Deep‐Learning‐Based Phase Picking for Volcano‐Tectonic and Long‐Period Earthquakes

Zhong,

Tan

2024

Geophysical Research Letters

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The application of deep‐learning‐based seismic phase pickers has surged in recent years. However, the efficacy of these models when applied to monitoring volcano seismicity has yet to be fully evaluated. Here, we first compile a data set of seismic waveforms from various volcanoes globally. We then show that the performances of two widely used deep‐learning pickers deteriorate systematically as the earthquakes' frequency content decreases. Therefore, the performances are especially poor for long‐period earthquakes often associated with fluid/magma movement. Subsequently, we train new models which perform significantly better, including when tested on two data sets where no training data were used: volcanic earthquakes along the Cascadia subduction zone and tectonic low‐frequency earthquakes along the Nankai Trough. Our model/workflow can be applied to improve monitoring of volcano seismicity globally while our compiled data set can be used to benchmark future methods for characterizing volcano seismicity, especially long‐period earthquakes which are difficult to monitor.

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Section: Geophysical Research Lettersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep‐Learning‐Based Phase Picking for Volcano‐Tectonic and Long‐Period Earthquakes

Zhong,

Tan

2024

Geophysical Research Letters

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São Jorge's Volcano-Tectonic Unrest in 2022: A Joint Interpretation Through GNSS and Fully Automated Seismic Analysis

Suarez,

García-Cañada,

Meletlidis

et al. 2024

Pure Appl. Geophys.

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The Azores Triple Junction offers a unique opportunity to investigate the interplay between volcanism and tectonic activity. After 60 years of quiescence in São Jorge Island, in March 2022, the island experienced a volcano-tectonic unrest, accompanied by widely felt earthquakes and surface deformation. We conducted an extensive study of this anomalous activity throughout 2022, through a purely automated analysis based on a deep-learning approach for seismic activity, combined with the processing and analysis of data from GNSS stations in the archipelago. The joint interpretation of ground deformation and seismicity suggests a failed magmatic eruption, which we have summarized in a four-stage conceptual model. The unrest began on 16 March 2022 with a period of vertical uplift that lasted for 2 days. On 19 March, the deformation reversed with a burst of seismicity that marked a rapid dike intrusion in the crust which abruptly stopped a few kilometers under the surface. Over the following weeks, the relocated seismicity suggests an intense overpressure near the Moho discontinuity and reveals, in great detail, a lateral magmatic expansion in a sill-like pattern. Finally, from the second week of April until the end of the year a decrease in seismic activity and a lack of deformation registered, indicates a decline and stabilization of the volcano-tectonic process.

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Volcanomagnetic signals related to the 2021 Tajogaite volcanic eruption in the Cumbre Vieja rift (La Palma, Canary Islands)

Blanco-Montenegro,

Arnoso,

Sánchez

et al. 2024

Journal of Volcanology and Geothermal Research

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Unveiling the pre-eruptive seismic series of the La Palma 2021 eruption: Insights through a fully automated analysis

Cited by 4 publications

References 41 publications

Deep‐Learning‐Based Phase Picking for Volcano‐Tectonic and Long‐Period Earthquakes

Deep‐Learning‐Based Phase Picking for Volcano‐Tectonic and Long‐Period Earthquakes

São Jorge's Volcano-Tectonic Unrest in 2022: A Joint Interpretation Through GNSS and Fully Automated Seismic Analysis

Volcanomagnetic signals related to the 2021 Tajogaite volcanic eruption in the Cumbre Vieja rift (La Palma, Canary Islands)

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