Volcanoes are multi-physics systems where different phenomena interact, such as magma transport, degassing, crystallization, and pressure-induced faulting. These interactions create a series of seismic signals, among which we have volcano-tectonic earthquakes, long-period events, and volcanic tremors. Thanks to these signals, there has been an improvement in the comprehension of volcanic systems. However, due to its complexity, there is still a debate regarding the observed seismic signals, i.e., their precise origin and characteristics. In the past, some techniques, such as spectral analysis and simple earthquake location were used. However, these techniques lack the resolution that we currently need. In this regard, network-based methods have been developed to determine the level of wavefield coherence and to classify different seismicity types from complex continuous signals.

In this work, we analyze eight years (from 2011 to 2020) of continuous seismic data of Piton de la Fournaise, la Réunion, France, using a network array including approximately 20 stations. We use a method based on the covariance matrix combining interstation single-component cross-correlations. From the continuous velocity records, we create temporal overlapping windows in which we estimate the covariance matrix in the frequency domain. We then evaluate its rank through the estimation of the width of its eigen-values distribution, in other terms, the number of independent seismic sources. This method allows us to quantitatively measure the presence of coherent sources recorded by the array and to characterize their frequency content.

The resulting distributions of the spectral width show that continuous signals are characterized by multiple narrow spectral peaks clearly observed in the co-eruptive tremors but also during periods without visible volcanic activity. To enhance these peaks, we re-normalize the distribution of spectral width in the frequency and time domains. As a result, we observe in the 1-3 Hz frequency band many spectral peaks that remain nearly constant during very long periods (weeks to months). At the same time, we observe a clear difference in the distribution of these frequencies between the co-eruptive and quiet periods and also some significant variations during long-standing eruptions. We suggest that variations of the spectral lines can be related to the properties of seismo-volcanic sources and eventually to the structural changes and, therefore, can be used in volcano monitoring.

How to cite: Caballero, E., Shapiro, N., Journeau, C., Seydoux, L., Soubestre, J., and Barajas, A.: Long-term evolution of the spectral content of continuous seismo-volcanic signals from a network-based analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2724, https://doi.org/10.5194/egusphere-egu23-2724, 2023.

The Skaftár cauldrons are a pair of depressions at the surface of the Vatnajökull glacier that signify subglacial lakes caused by geothermal heat sources at the underlying bedrock. These subglacial lakes continuously grow in volume and produce fast-rising jökulhlaups (glacial lake outburst floods) at the glacier outlet 35-40 km away. Seismic tremor events coincide with these large floods, but the exact source of this tremor is unknown. To investigate the origin and characteristics of these tremors, network covariance matrix spectral width, real-time seismic amplitude measurements (RSAM), and spectral analyses were conducted for eight jökulhlaups spanning the years 2015 to 2021, with joint interpretation alongside hydrological and GPS data. Seismic data was aquired by the Icelandic Meteorological Office's network. The several-day time period spanning the flood propagation and deepening of the ice cauldrons was dominated by small icequakes along the subglacial flood path and emergent low frequency, temporally regular earthquakes with repetitive waveforms. Once most of the water had drained from the cauldrons, strong tremor bursts with a duration on the order of 10s of minutes and a dominant frequency range between 0.5 and 3 Hz were recorded by seismic stations both on and off of the glacier. This tremor occurred during seven out of the eight floods examined, exhibiting fewer than 10 clear tremor bursts per flood over approximately a 24-hour period. Preliminary results show that the amplitude of the tremor bursts correlates to the magnitude of the flood, with the strongest tremor occurring in 2015, during the largest recent flood from the eastern cauldron. Due to the similarities shared between the shape of this tremor’s seismic envelope and that of the tremor during hydrothermal explosions, we interpret the tremor seen after the Skaftá cauldrons have drained as steam explosions facilitated by a drop in the overlying water pressure.

How to cite: Vanderhoof, B., Jónsdóttir, K., Brandsdóttir, B., Guðmundsson, Ó., Nowe, S., Soubestre, J., Caudron, C., Lecocq, T., Cubuk-Sabuncu, Y., and Einarsson, B.: Investigation of Tremor Events Coinciding with Skaftár-Cauldrons Glacial Floods from 2015 to 2021, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15014, https://doi.org/10.5194/egusphere-egu23-15014, 2023.

The 2021 Fagradalsfjall eruption in the Reykjanes peninsula, Iceland, was marked by episodes with varying volcanic activity. Our study focuses on the period from eruption start on the 19th March 2021 until the 2nd May 2021. This phase was marked by relatively continuous lava flows and non-periodic lava fountaining observed at up to 12 different vents, increasing in intensity throughout the observation period. Seismic tremor emanating from co-eruptive processes like for example lava fountaining, collapse of crater walls and magma and lava migration is non-impulsive, often with emergent onsets and no defined phase arrivals. Thus it is difficult to locate the tremor sources with traditional network based methods. We show that using small aperture arrays it is possible to locate and monitor several tremor sources that were active simultaneously, providing good spatial resolution on the details of the eruptive fissure. We investigate how array processing of 3-component data can assist with the determination of different seismic wave types and lead to a better understanding of the underlying volcanic processes. We find that seismic arrays are well suited to monitor the location, type and strength of volcanic processes that are active simultaneously. This can have important implications for volcanic hazard monitoring, especially when visual monitoring with webcams is difficult for example due to remoteness or poor visibility.

How to cite: Möllhoff, M., Smith, P., Craig, D., Bean, C. J., Vogfjörd, K. S., Li, K. L., and Nooshiri, N.: Seismic monitoring of co-eruptive volcanic processes during the 2021 Fagradalsfjall Eruption, Iceland, using two small-aperture arrays, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11770, https://doi.org/10.5194/egusphere-egu23-11770, 2023.

Recent eruptive activity at Stromboli is mostly characterized by persistent, Strombolian explosive activity from summit craters. Occasionally, so-called major explosions occur, and more rarely paroxysms take place, such as on 3 July and 28 August 2019. We analyze monitoring data recorded between 2016 and 2022 to identify and characterize patterns in multi-parameter time series (considering in particular seismic, geochemical, meteorological, and sea-level gauges), with special interest in analyzing patterns observed before and during the occurrence of major explosions and/or paroxysmal eruptions. In practice, (i) we implement algorithms to automatically identify families of seismic events using waveform features; (ii) we analyze the effect of oceanic microtremor on continuous and discrete amplitude-based measurements; (iii) we study the temporal and size distribution of event occurrences, and use this information to assess likely trends in eruptive behavior. Moreover, (iv) we use noise cross-correlations and auto-correlations to compute seismic velocity variations of the shallow crust. 

Multi-parametric measurements provide interesting insights in the temporal evolution of the eruptive activity at Stromboli; for example, correlated changes in the pattern at which events occur in time, patterns in the distribution of extreme, large events, and evidence of a decrease in seismic velocity, seem to be phenomena occurring before paroxysmal eruptions. Detailed analyses of the produced time series, including also pattern recognition techniques, are required for better revealing likely patterns and for better understanding and interpretation of observations. 

How to cite: Garcia, A., Zaccarelli, L., and Sandri, L.: Analysis of eruptive activity at Stromboli volcano through a continuous monitoring of multi-parameter geophysical data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15097, https://doi.org/10.5194/egusphere-egu23-15097, 2023.

How to cite: Niu, J. and Song, T.-R. A.: Tracking magma plumbing system overpressure and volume through macroscopic seismic source parameters of repetitive volcanic-seismic events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6066, https://doi.org/10.5194/egusphere-egu23-6066, 2023.

Swarms of earthquakes are often associated with eruptive processes, but many swarms occur near volcanoes that are not easily associated with eruptions, complicating their use in eruption forecasting.  In some cases, swarms may be caused by hydrothermal processes and can be considered as part of a volcano’s normal background seismic activity. Other swarms near volcanoes may be considered purely tectonic in origin, or some combination of tectonic, magmatic, and hydrothermal processes. Distinguishing driving processes for a volcanic swarm is often difficult using seismic data alone and yet seismic data may be the only monitoring stream available at many volcanoes.  Even when other monitoring data are available, seismic unrest often manifests itself first in the run-up process to an eruption.  Thus, tools that help distinguish whether a swarm is magmatic or not are desirable for observatories to improve forecasting efforts.

Determining when an eruption will follow a swarm is non-trivial, even if a swarm can be confidently linked to a magma intrusion.  Statistical comparison of an ongoing swarm to prior swarms at the volcano or at other volcanoes provides baseline probabilities for forecasting efforts and may reveal patterns in precursory activity in general. Swarms are often easy to visually identify in an earthquake catalog, but as no standard approach exists to consistently define and detect swarms across time and space, it is difficult to statistically compare them. At individual volcanoes, temporal changes in monitoring networks present challenges, while other factors, such as varying rates of background seismicity, complicate inter-volcano swarm comparisons.

To address some of these challenges, we have created a catalog of earthquake swarms covering 62 eruptions at 79 active volcanoes in the United States. The catalog balances consistency in methodology with the inherent variations between and among the volcanoes and their monitoring networks and is calibrated such that only concerning swarms, significantly above background levels, are retained.  We compute a suite of statistical attributes for each swarm and compare these attributes among various subgroups of swarms and volcanoes. Overall, we find that ~25% of the swarms in the catalog are associated with eruptions but only ~10% began prior to the eruption. In addition, though ~35% of eruptions are associated with swarms, only ~20% of eruptions have swarms that began prior to the eruption.  The eruptive and non-eruptive swarms show significant differences in evolution of moment release and event rate, but these differences vary depending on the types of volcanoes and eruptions considered. When tailored to the specifics of an ongoing swarm, analog swarm comparisons may help decipher driving process and likelihood of eruption.

How to cite: Pesicek, J. and Prejean, S.: A catalog of volcanic earthquake swarms in the United States: comparative analysis and use in eruption forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10114, https://doi.org/10.5194/egusphere-egu23-10114, 2023.

The complexity of time series can be quantitatively measured using Permutation Entropy (PE). PE has recently been introduced as a potential tool in eruption forecasting by applying it to the seismic time series. Examples of successful applications are the eruptions at Strokkur Geyser, Iceland, the 1996 eruption of Gjálp, Iceland, and the eruptions of Shinmoedake volcano, Japan, in 2011, 2017, and 2018. While PE is able to show temporal changes prior to an eruption, these features are not always prominent. To improve this method, we calculate PE not only for the amplitudes of the seismic signals but also for the seismic instantaneous phases, called Phase Permutation Entropy (PPE). To understand the difference between PE and PPE, we performed synthetic tests by creating several synthetic waveforms using different numbers of sin wave superposition. We used more wave superposition with different frequencies to create complex waves containing broader frequency spectrum, while less superposition is used to create simpler waves containing narrower frequency spectrum. PE and PPE values are both low for simple waves and high for complex waves, but their absolute values differ, which might contain valuable information. The gap, dP = PE-PPE, is found to be smaller for complex waves compared to the more simple waves.  We then calculated PE and PPE for seismic data recorded from January 2014 to December 2015, which covered the eruption period of Holuhraun in Iceland. During the time of quiescence, both PE and PPE exhibit a long period variation which seems to be seasonal. Calculating dP weakens the long period noise and generates a more stable baseline.  We observe that the temporal variation of dP started to decrease below the baseline after 24 May 2014, indicating that the ground motion got more complex. An abrupt drop of dP to its lowest level was observed on 16 August 2014, when the dyke started to propagate from Bardarbunga to Holuhraun. While dP increases after the onset of eruption on 29 and 31August 2014, there is no prominent feature between the dyke propagation and the onset of the eruptions.  During the eruption period, dP stays lower than the background dP, indicating a higher ground motion complexity compared to the quiescence time. After January 2015, the gradual increase of dP back to the baseline level is clearly observed, showing the method’s potential to foresee the end of the eruption.

How to cite: Sudibyo, M. R. P., Eibl, E. P. S., Hainzl, S., and Ohrnberger, M.: Increased complexity of seismic ground motion prior and during the 2014 Holuhraun eruption, Iceland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6804, https://doi.org/10.5194/egusphere-egu23-6804, 2023.

The IMPROVE ITN project focuses its interdisciplinary approach on a better understanding of volcanic systems, partly with multiphysics imaging methods. One target of this project is Krafla, a volcano of the northern volcanic zone in Iceland, which erupted last during the Krafla fires in the 1970s and 80s. Also, in this period the national power company of Iceland (Landsvirkjun) built a geothermal powerplant inside the Krafla caldera, increasing the knowledge of the complex system through electro-magnetic and seismic imaging methods and seismological observations.
Nonetheless, the high-resolution imaging of the magmatic system still poses a challenge just as the origins of the seismicity remain poorly understood. To tackle these questions a multi-physics experiment has been carried out in June and July 2022.
The experiment included an active 3D ERT experiment to image the first kilometre of the geothermal system, the densification of the already existing MT measurements and the installation of a dense seismic array of 100 stations deployed for 1 month. In addition, Landsvirkjun provided continuous seismic data acquired from 12 broadband 3-C stations over the last 8 years.
With this dataset we aim to better understand temporal and spatial changes in stress, the anthropogenic influence on the system through the geothermal industrial activity and to image shallow magmatic pockets.
The broadband data of the 12 permanent seismic stations were used to analyse the seismicity with STA/LTA and Template Matching methods. The first P- and S-wave onsets were automatically picked and inverted using a joint hypocentre-velocity approach based on ray theory. It provides a new 3D P-wave velocity model and refined locations of the seismicity.
This updated earthquake catalogue, consisting of seismicity of the last 8 years, covers a deflation and an inflation period of Krafla, yielding the opportunity to better investigate the seismic properties in relation with geothermal industrial activity and long-term deformation of the volcano. The variability of the P-wave velocity will be compared to the available 3D resistivity models obtained from previous MT measurements.
In the future, the dense seismic array will be used for high resolution imaging at the geothermal upflow-systems and jointly interpreted with the ERT and MT data, while the 12 broadband recordings will be used for seismic noise monitoring purposes.

How to cite: Glück, E., Garambois, S., and Vandemeulebrouck, J.: First results of a multiphysics experiment at Krafla geothermal volcano: Seismicity pattern from joint hypocenter-3D travel-time tomography inversion, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3550, https://doi.org/10.5194/egusphere-egu23-3550, 2023.

Volcanic eruption is a continuous threat to many places in the world. Despite recent advances in volcano monitoring techniques and developments in monitoring networks, eruption forecasting remains a challenging task, partly because an accurate description of the current state of a volcano is missing. Such a description requires knowledge about the time varying internal structures of the volcano. An ultimate goal is, therefore, to obtain snapshots of the volcano structures across multi scales. However, due to limitations in imaging techniques in highly heterogeneous volcanic environments, e.g., strong velocity gradient near surface and rich in small-scale scatterers throughout the volcano, only relatively large-scale structures are normally recovered. This is relevant to imaging all important magma chambers, which have often been illusive. Recent studies reveal that magma chambers can have large aspect ratio, i.e., a thin body that extends laterally more than a few times its thickness. This extreme geometry adds to the complexity of the imaging problem. In this research, we present a systematic study of how different factors affect the ability to recover a clear image of a magma body and their relative importance in the imaging problem. Classes of synthetic models with different weights of these factors are generated. The models are then used to generate synthetic seismograms using numerical simulations of full wavefield seismic wave propagation. The seismograms are used as the input to various image techniques for recovering images of the synthetic models. Our preliminary results show that even in a non-scattering environment, imaging a magma chamber can be challenging due to, e.g., weak velocity contrast between the magma body and the surrounding rock materials. The imaging problem is compounded by strong scattering. The aim of the work is to understand the limitations of current imaging and acquisition approaches, and to better understand what we can “expect to see”.

How to cite: Li, K. L., Bean, C. J., and Gudmundsson, Ó.: Challenges in volcano magma chamber imaging: A numerical study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8680, https://doi.org/10.5194/egusphere-egu23-8680, 2023.

We compiled a database for the Campi Flegrei seismic events that occurred from 2011 to 2018 at all stations available (merging permanent and temporary networks). Then we computed the two observables of the crustal anisotropy: time delay between fast and slow S-wave’s arrivals, and polarization direction of the fast S-wave. These results provide useful information about the amount of crustal anisotropy and the main direction, respectively, with this latter representing a proxy for the local stress field. We could thus obtain a picture of their spatial and temporal distributions to be compared with other geophysical and geochemical observations. In particular we could identify common features, such as change points, to several time series. This helps us in building a more complete interpretation of the volcanic system changes that were occurring during the recent ongoing unrest phase, which started in 2005. 

How to cite: Zaccarelli, L., Delle Donne, D., Martino, C., Cusano, P., Galluzzo, D., Ricciolino, P., Bianco, F., and Pino, N. A.: Local stress field spatio-temporal variations at Campi Flegrei from crustal anisotropy measurements, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15894, https://doi.org/10.5194/egusphere-egu23-15894, 2023.

This work intends to contribute to the comprehension of the Campi Flegrei caldera (CFc) unrest, through the relative relocation of the diffuse seismicity recorded during the 1982-84 unrest and after its reactivation in 2005.

The CFc is one of the best monitored volcanic areas in the world, with a multi-parametric network of observing stations operating in the area. The shallow structure of the caldera, between 1 and 3 km, is a high-temperature hydrothermal system formed by a sequence of volcanoclastic, tuffs, lava, and marine deposits. The temperature gradient measured in deep boreholes, down to a depth of about 3 km, exceeds 150°C/km. A zone of pressurized gas and sill intrusion is possibly located at 3-4 km. A long-term magma reservoir is hypothesized in the deep structure (7-9 km), persistently supplying CO₂ to the surface (observable for the continuity of gas emission to the fumaroles of Solfatara-Pisciarelli as well).

While the unrest of 1982-84 has been generally associated with magma injection, a mechanism of fluid pressurization and heating of the CFc hydrothermal system is thought to be the primary forcing of ground deformation and shallow seismicity of the ongoing unrest. However, the mechanisms that control the interaction between the rising of fluids from deeper volumes and the seismicity within and below the hydrothermal system are still debated.

In this work we use the arrival times of located seismic events to perform a double-difference relative relocation of earthquakes that occurred in the years 1982-84 and 2005-2022. Moreover, by using calibration laws for magnitude scales to infer the moment magnitude M_w from available catalogs of duration magnitude M_d, we estimate the spatial distribution of the cumulative seismic energy released during the two considered time spans.

The final distributions of hypocenters and radiated seismic energy, and their spatio-temporal evolution, suggest constraints for the identification of preferential pathways of rising fluids and for the imaging of structural barriers. These results can be interpreted jointly in light of previous works and available tomographic models for the definition of possible scenarios of unrest evolution.

How to cite: Danesi, S., Pino, N. A., Carlino, S., and Kilburn, C.: Relative earthquake location of low-energy volcanic seismicity at Campi Flegrei, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13351, https://doi.org/10.5194/egusphere-egu23-13351, 2023.

On 15 January 2022 at 04:15 UTC, an enormous explosive eruption of the Hunga Tonga-Hunga Haʻapai submarine volcano (short: Hunga) occurred in the Tonga-Kermadec volcanic area in the southern Pacific Ocean. It was one of the strongest volcanic eruption within the last 150 years. The eruption column reached a height of more than 50 kilometres causing heavy atmospheric turbulences. A strong Lamb and a tsunami wave were generated. Besides these phenomena also seismic waves could be observed on seismic stations all over the world.

Consequently, seismic body and surface waves of the Hunga main explosion could be clearly recorded at seismic stations in Germany. After about 19 minutes, the PKP phase was the first arriving body wave reaching the broadband stations of the German Regional Seismic Network and the Gräfenberg Array. Using the short-period WWSSN-SP filter it was possible to determine the onset times of relatively weak PKPbc phases at several stations. The onset times as well as slowness and azimuth determined by array methods allowed an unambiguous assignment to the Hunga event and an epicenter localization deviating approximately 1 to 1.5 degrees from the volcano.

While the PKP phase is only weakly visible in short periods it shows up clearly in the long-period range (SRO-LP filter). The onset times determined therein were still accurate enough to provide a localization similar to that obtained in the short-period range. Furthermore, at least one additional event is detected on the long-period seismograms about 4 minutes after the main event.

To assign a seismic magnitude to the Hunga event, we analyzed the surface wave trains. The Ms magnitudes vary between 5.8 and 6.3 within the individual stations of the GRSN, with a mean value of 6.0.

The Tonga-Kermadec subduction zone is characterized by strong earthquake activity. This allows us to compare the seismic recordings of the Hunga event with those of earthquakes from the same area with shallow focal depths and comparable magnitudes. It turns out that PKP phases of the Hunga eruption have significantly smaller amplitudes in the short-period range than for the compared earthquakes but similarly strong in the long-period range. We conclude that a long-period excitation is characteristic for the seismically relevant focal process of the Hunga event.

How to cite: Plenefisch, T., Steinberg, A., Hupe, P., Pilger, C., Donner, S., Gaebler, P., Ross, O., and Ceranna, L.: The eruption of the Hunga Tonga-Hunga Ha'apai volcano on 15 January 2022 as observed at seismic stations in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9796, https://doi.org/10.5194/egusphere-egu23-9796, 2023.