In March 2021, a fissure eruption in Fagradalsfjall marked the onset of the first volcanic activity in over 800 years on the Reykjanes Peninsula, Iceland. Since then, three more fissure eruptions occurred in August 2022, July 2023, and December 2023. Intense seismic swarms that included M_w≥ 4.0  earthquakes preceded all eruptions. Concurrently with swarm activity, large surface deformations related to magma intrusions were observed by Interferometric Synthetic Aperture Radar (InSAR), and Global Navigation Satellite System (GNSS) data. Ground displacements exceeded the deformation expected from earthquakes by far, suggesting the intrusion process was primarily aseismic. However, the intrusions may have influenced the prevalent earthquake faulting style in the pre-eruptive swarms. For instance, seismic moment tensors before and during the early 2021 swarm indicate that right-lateral bookshelf faulting along roughly north-south trending strike-slip faults dominated seismic deformation. This changed to more northeast-southwest oriented oblique-normal to normal faulting mechanisms for the later swarms consistent with graben formation after a dyke intrusion. Seismic moment release during the 2021 seismic swarm, which included ten relatively large M_w 5+ earthquakes, was larger than for subsequent swarms, where only a few events reached M_w 5. This may indicate that the 2021 intrusion, which marked a reawakening of volcanic activity, may have triggered bookshelf faults close to failure that might have otherwise ruptured in the near term due to the underlying oblique rifting.  The transition of source mechanisms toward oblique and normal faulting during the later swarms, though, may reflect an active role of the intrusion processes on fault orientation of triggered seismicity rather than simply inducing seismicity on pre-existing faults. 

How to cite: Rodriguez Cardozo, F. R., Braunmiller, J., Sadeghi Chorsi, T., Mendo Pérez, G., Hjörleifsdóttir, V., Jónsdóttir, K., Cubuk Sabuncu, Y., Thompson, G., McNutt, S., Dixon, J., Dixon, T., and Malservisi, R.: The role of magma intrusions on faulting mechanisms during pre-eruptive seismic swarms in the Reykjanes Peninsula, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4796, https://doi.org/10.5194/egusphere-egu24-4796, 2024.

On March 19, 2021, an effusive eruption lasting for six months began in the Geldingadalir valley on the Reykjanes peninsula, in the southwest of Iceland. This eruption was characterised by episodic lava effusion from 2 May to 18 September and changing eruptive behaviour. Here, we analyse five of such effusion episodes of 8 June 2021 by using drone video data acquired over the active crater lake together with volcanic tremors that were recorded by a seismometer located at 5.5 km distance from the active vent. We are thus able to study each of the five episodes in terms of tremor amplitude evolution and its frequency spectrum, and compare it with the timing, height and dynamics of the lava lake, its bubbling and crater overflow. We observe a slow rise of the lava lake by 25.1 to 26.2 metres within 6.15 ± 2.35 minutes, followed by a rapid fall of the lava lake surface to its previous level within 1.6 ± 0.12 minutes. In contrast, the quiescence period in the tremor lasts ~ 10.12 ± 0.68 minutes followed by another 2.55 ± 0.2 minutes of tremor. Thus, the duration of tremor generation is shorter than the time required for the lava lake to reach its maximum height. Furthermore, the tremor amplitude reaches its maximum after the lava lake has started to sink. We discuss the volcanic tremor generation in relation to lava lake elevation and related processes.

How to cite: Joachim, A., Eibl, E. P. S., Müller, D., and Walter, T. R.: Linking volcanic tremor amplitude to drone records of lava lake level changes during the 2021 Geldingadalir eruption, Iceland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10597, https://doi.org/10.5194/egusphere-egu24-10597, 2024.

Hengill volcano and its associated geothermal fields represent Iceland's most productive harnessed high-temperature geothermal fields, where energy is provided by cooling magmatic intrusions connected to three volcanic systems. The crustal structure in this area is highly heterogeneous and shaped by the intricate interplay between tectonic forces and magmatic/hydrothermal activities, making detailed subsurface characterization challenging. In the Northern part of the Hengill geothermal field, super-hot geothermal resources have been spotted that are currently considered for geothermal exploration.

Over the past years, we have studied the site in great detail, and acquired high-quality datasets from a 40+broadband seismic station array, a dense 500+ station nodal array and distributed acoustic sensing data from a fibre line crossing the area. We compare the results that we obtained from various seismic imaging methods e.g., earthquake tomography, ambient noise methods and discuss their potential and limitation to enable high-resolution seismic methods for exploration and monitoring of geothermal plays in such complex volcanic environments.

How to cite: Obermann, A., Goertz-Allmann, B., Sanchez-Pastor, P., shi, P., Wu, S.-M., and Hjórleifsdóttir, V.: High-Resolution Seismic Methods for Geothermal Exploration and Monitoring across the Hengill volcanic area, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21112, https://doi.org/10.5194/egusphere-egu24-21112, 2024.

The 2018 eruption at Sierra Negra volcano, Galapagos Islands, was accompanied by 8.5 metres of caldera subsidence and intense seismicity, resulting from deflation of a shallow sill-like magma reservoir at ~2 km depth. High-precision hypocentre locations from manually picked phase arrivals show that earthquake sources are tightly constrained within a complex, multi-stranded trapdoor fault system (TDF) above the sill. However, the incompleteness of this high-precision catalogue leaves outstanding questions about the spatio-temporal evolution of seismicity through the eruption, and how it relates to deformation and magma efflux.

Here we present the results of an automated workflow to streamline the production of a ‘temporally-enriched’ seismic catalogue for 2018 eruption at Sierra Negra. We initially utilise PhaseNet, a deep-neural-network-based automatic phase picker, to identify events missing from the initial manually picked catalogue, expanding the detections from 1,618 to 9,871 events. Our catalogue identifies more events in the immediate aftermath of the M_w5.4 earthquake that initiated the eruption, and new small magnitude events (< M_L2.0) in the period more than 72 hours after the eruption onset. We then use a template matching approach to further supplement these detections. Specifically, these events fill gaps in the catalogue where tremor amplitudes make manual event detection more difficult. Hypocentre locations for newly detected events are also constrained to the TDF zone, however there is more variety in depth estimates. This has implications for how we consider the TDF with depth, and allows us to consider other potential sources of seismicity in the system.

Our workflow offers an efficient method of producing ‘temporally-enriched’ catalogues at Sierra Negra, and can be readily adapted for the sparse seismic network that remains during the current inter-eruptive phase. However, our experience at Sierra Negra suggests that applying automated earthquake detection and location methods can be challenging in volcanic settings, and requires careful parameterization and quality control.

How to cite: Butcher, S., Bell, A., Hernandez, S., La Femina, P., Grannell, J., and Ruiz, M.: Temporal enrichment of the seismic record of the 2018 eruption at Sierra Negra using deep neural networks, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7488, https://doi.org/10.5194/egusphere-egu24-7488, 2024.

Popocatépetl, one of Mexico's most active volcanoes, poses significant risks to the dense populations in its vicinity. Effective monitoring of its seismic activity is crucial for understanding and mitigating these hazards. This study employs data collected with a network of 19 seismic stations surrounding the volcano, combined with machine learning techniques and spatial coherence methods, to generate comprehensive seismic catalogs spanning from 2019 to the present. Our automated workflow includes the identification and localization of long period (LP) events, tremors, and volcano-tectonic (VT) earthquakes.

For this purpose, an improved classification model based on Support Vector Machines was developed to distinguish LP events and tremors within continuous recordings. Their locations were determined using a cross-correlation-based method. Additionally, the VT earthquake catalog was compiled using deep learning-based models for phase picking, followed by standard location methods. Our findings not only corroborate trends observed in manual analyses at the volcano's observatory but also uncover additional events, highlighting trends in the volcano’s dynamics not observed before.

To showcase the use of these catalogs, we will present a multiparametric analysis integrating seismic data with thermal anomalies, SO2 emissions, and GPS measurements. This research not only deepens our comprehension of volcanic processes but also underscores the transformative role of technology in geophysical research.

Research supported by the program UNAM-DGAPA-PAPIIT: IN103823.

How to cite: Bernal-Manzanilla, K., Calò, M., Rodríguez García, K. E., Martínez Jaramillo, D., and Valade, S.: Dynamics of the Popocatépetl Volcano, Mexico, revealed by Machine Learning-Based Seismic Catalogs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13020, https://doi.org/10.5194/egusphere-egu24-13020, 2024.

Continuous seismological observations provide valuable information to deepen our understanding of processes occurring in both aerial and submarine volcanoes. However, the wealth of the seismicity recorded near volcanoes makes exhaustive exploration of these seismological chronicles very complex and time-consuming. In this study, we present a systematic analysis of two months of seismological records using a self-supervised learning (SSL) approach for the unsupervised clustering of continuous seismic data acquired by ocean bottom seismometers deployed in the vicinity of the Fani Maoré volcano (Mayotte). The proposed clustering process allows the identification of individual seismic events, seismic crisis and tremors that would be challenging to observe using conventional approaches. We show that our approach detects and classifies both known and new events, including two eruptive sequences previously unknown. We also demonstrate the potential of self-supervised methods for the analysis of seismological records, providing a synoptic view and facilitating the discovery of insightful yet rare events. This approach has numerous applications in exploring various seismological datasets, simplifying analysis while making it more comprehensive.

How to cite: Hibert, C., Rimpot, J., Retailleau, L., Saurel, J.-M., Malet, J.-P., Forestier, G., Weber, J., Stangeland, T. S., Turquet, A., and Pelleau, P.: Self-supervised learning for the exploration of continuous seismic records at the Fani Maoré submarine volcano (Mayotte), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15278, https://doi.org/10.5194/egusphere-egu24-15278, 2024.

Volcanoes often exhibit very long period (VLP) signals, with periods ranging from 2 to 100 seconds. Due to their very long wavelengths, these waveforms are not strongly affected by volcano structural heterogeneities and provide invaluable insights into dike and magma properties that are not easily accessible though other observations. Historically, only a few VLP events have been recorded at the Piton de la Fournaise, primarily associated with collapses in the summit caldera in 1986, 2002 and 2007. However, since 2010, the monitoring network has evolved significantly and it is now equipped with several broadband stations, allowing a wide range of signals to be recorded. In this study, we show that VLP events are quite common at Piton de la Fournaise. Specifically, we identify swarms of VLP events during eruptions and during magma injections preceding eruptions. Source analysis of VLP events during eruptions indicates the resonance of the dike during sudden decreases in magma flow. Pre-eruptive VLP waveforms exhibit significant differences from those observed during eruptions, pointing to a distinct source process as the dike propagates laterally toward the volcano flank.

How to cite: Duputel, Z., Costes, L., Ferrazzini, V., and Lengliné, O.: Very long-period observations at Piton de la Fournaise volcano, La Réunion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4438, https://doi.org/10.5194/egusphere-egu24-4438, 2024.

We present observations showing that during episodes of volcanic tremors, the phase of inter-station cross-correlations becomes stable. We propose a new quantity, the phase coherence, to identify the differential phase stability in recordings obtained from a single pair of stations, which is extrapolated to the seismic network. Then, we present a new approach based on the estimation of differential travel times through the differential phase measurements, to locate the sources of tremors occurring at the end of 2015 at the Klyuchevskoy Volcanic Group in Kamchatka, Russia. We present evidence supporting the existence of two types of activity happening simultaneously during the tremor episode: the main tremor source, originating from a region located between 7 and 9 km depth under the main volcanoes, and the widespread occurrence of weak low-frequency earthquakes occurring at random locations. We show how the phase coherence and the differential phases can be used to provide information on the stability of the tremor source position and to estimate its location.

How to cite: Barajas, A., Shapiro, N., and Prieto, G.: Differential phase analysis for volcanic tremor detection and source location., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16604, https://doi.org/10.5194/egusphere-egu24-16604, 2024.

In this study we utilize 3D numerical models to simulate seismic resonances in a volcanic edifice, arising from the interaction between an externally excited wavefield and a magma chamber-conduit system. The resultant wavefield holds the potential to provide significant insights into the properties of the magmatic system. Contrary to previous assumptions that required an internal source, our findings show that the magma chamber and conduit efficiently capture the incident wavefield of both P- and S-waves, excited by a high-frequency (~10 Hz) earthquake located within the edifice. Due to multiple internal reflections off the boundaries of the chamber and the conduit, prolonged reverberations occur, which are guided along the conduit. Temporal and spectral analyses of synthetic seismograms illustrate that the size of the magma chamber and the width of the conduit are critical in determining the magnitude and dominant frequencies of the seismic resonances. Specifically, models with larger magma chambers and wider conduits consistently yield larger resonance amplitudes at distinct frequencies. At greater distances from the conduit, an intensified scattered wavefield with a broad frequency range indicates the presence of a substantial magma chamber within the volcanic edifice. Resonance frequencies reach up to 23 Hz, underscoring significant frequency shifts. In general, these externally initiated resonances may appear as tremor-like signals at seismic stations on the edifice, accompanying more conventional seismic events in its proximity.

How to cite: Rümpker, G. and Limberger, F.: Numerical modeling predicts seismic resonances in the magma chamber-conduit system due to wavefield capturing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20804, https://doi.org/10.5194/egusphere-egu24-20804, 2024.