GMPV9.5

A volcanic eruption is usually preceded by increased seismic activity resulting from magma propagation. Although these precursors can be detected by a modern seismometer, it is still a challenge to answer whether they will be followed by an actual eruption and when the eruption will occur after precursors are detected. The time between the start of volcanic unrest and the actual eruption is crucial. Therefore, there is a need for an assessment tool that is applicable in real-time. Permutation Entropy (PE) has been recently suggested to be a promising tool for the prediction of volcanic eruptions. It is a robust yet simple tool to quantify the complexity of time series. We aim to find out whether there is a distinct feature in the temporal variation of PE that is useful for eruption forecasting. We performed several synthetic tests to understand how PE works and how to choose the optimum input parameters for a signal with certain properties. We then applied this knowledge to calculate PE of seismic data that recorded eruptions of Strokkur geyser, Iceland on the 10^th of June 2018. 78 eruptions occurred within five hours of observation. We used this fast-repeating process to check if the eruptions cause a repetitive pattern of PE. The input parameters used for PE calculation are a window length of 1 second, an embedding dimension of 5, and a delay time of 0.067 seconds. Our results show a distinct, repeating pattern of the PE that is consistent with the phases in the eruptive cycle of Strokkur as described by Eibl et al. (2021). The PE drops in the stage of bubble accumulation at depth, then undergoes repeated increasing and decreasing patterns during regular bubble collapses at depth in the conduit, and finally continuously increases as a precursor towards the time of eruption on the surface. The average duration of this precursor to the eruption is about 10 seconds.

How to cite: Sudibyo, M., Eibl, E. P. S., and Hainzl, S.: Eruption forecasting at Strokkur geyser, Iceland: An application of Permutation Entropy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-447, https://doi.org/10.5194/egusphere-egu22-447, 2022.

The Sacks-Evertson strainmeters are fundamental instruments to monitor deformation of the shallow crust produced by volcanic processes since they can record volumetric strain signals with a nominal high resolution of about 10^-11. However, the recorded strain signal is affected by the effects of different disturbing sources such as earth tides, local barometric pressure variations, precipitations and underground water circulation. The disturbing signals (amplitude ranges 10^-8-10^-7) reduce the signal accuracy and can mask smaller strain transients (10^-9-10^-8) due to volcano processes [1] preventing thus the correct monitoring of the volcano activity.

The effects of the disturbing sources on the recorded strain signal can be filtered by employing dedicated softwares developed to this scope. However, such programs were not designed to be run automatically and thus cannot be directly employed for near real-time signal filtering. To fill this lack, we developed the software STRALERT (STRain and wArning signaLs in nEar Real-Time) to provide in near real-time both the strain signal recorded by a strainmeter station installed at the Etna volcano and the respective filtered signal to the Surveillance Room of the “Istituto Nazionale di Geofisica e Vulcanologia – Osservatorio Etneo”. The software embeds a modified version of the program BAYTAP-G [2] for the filtering operation that allows using a set of optimally defined filter parameters as inputs. The accuracy of the strain signal is improved reaching values of ≈10^-10 and allowing thus the detection of ultra-small strain changes.

Examples of the output of STRALERT are presented for the 2021 period, when frequent eruptive events took place at the Etna volcano. Significant strain changes are clearly observed during the main lava fountain episodes. Thanks to the good accuracy warranted by STRALERT, it was also possible to unravel tiny strain changes due to weak eruptive activity that would have been completely hidden by the tidal and the pressure variations in the recorded raw signal. Moreover, the filtered signal better shows the onset and the end of the transient strain variations allowing to easily mark the timing of the associated eruptive events. Alert thresholds have been defined on the filtered signals to recognize these transient strain changes and automatically deliver a warning signal for the surveillance operations.     

[1] Currenti, G. and Bonaccorso A. (2019). Cyclic magma recharge pulses detected by high-precision strainmeter data: the case of 2017 inter-eruptive activity at Etna volcano, Sci. Rep.-Uk., 9(1), 1–7.

[2] Tamura, Y., T. Sato, M. Ooe and M. Ishiguro (1991). A procedure for tidal analysis with a Bayesian information criterion, Geophys. J. Int., 104(3), 507–516.

How to cite: Carleo, L., Bonaccorso, A., Currenti, G., and Sicali, A.: STRALERT: STRain and wArning signaLs in nEar Real-Time at Etna for volcano surveillance operation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9711, https://doi.org/10.5194/egusphere-egu22-9711, 2022.

Many studies highlight the benefits of using machine learning algorithms for the classification of volcano-seismic signals. However, when it comes to their widespread application, volcano observatories and researchers face two important challenges. i) The performance of these models highly depends on the size of the training set, where large amounts of labeled signals (thousands and sometimes even hundreds of thousands) are needed to get sufficient accuracy. ii) Most of them use data recorded by a single station and from only one component. This “master” station is generally one of the closest to the crater and, in volcanoes, it is common to face technical difficulties that interrupt the continuous recording, especially during periods of increased activity.

This strongly limits the possibility of applying machine learning approaches for efficient monitoring of volcanoes, especially during unrest periods.

Here, we show a simple method that addresses these difficulties using the information provided by the entire network of stations operating at Popocatepetl volcano (about 18 stations among permanent and temporal) and using all the components. Initially, we used a mid-size catalog of 507 single-channel labeled events recorded between 2019 and 2020. Later, to increase the size of our dataset and exploit the information provided by different channels, we added the signals of the three components of all the events, as well as signals of selected events recorded at different stations. This enlarged training set comprises 1725 signals of six classes: 345 noise, 324 explosions, 321 long periods (LP), 306 volcano-tectonics (VT), 264 tremors, and 165 regionals. To characterize the data, we used a previously proposed set of 102 features that describe the shape, statistics, and entropy of the signals. Then we applied two classification algorithms, random forest and support vector machines, to both our datasets. Our results show that the model of the enlarged dataset increases the overall accuracy by over 8% compared with the one produced using one station and only one component, with the additional benefit of guarantying continuous monitoring even when the “master” station is not working.

How to cite: Bernal Manzanilla, K. and Calò, M.: Automatic detection and classification of seismic signals of the Popocatepetl volcano, Mexico, using machine learning methods., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-225, https://doi.org/10.5194/egusphere-egu22-225, 2022.

Volcanic eruptions are spectacular but dangerous phenomena. Depending on their magnitude and location, they also have the potential for becoming major social and economic disasters. Some of the most important volcanic events include ash fallout, lava flows, and related phenomena, such as volcanic debris avalanches and tsunamis. The ongoing demographic congestion around volcanic structures, such as Mount Etna, increases the potential risks and costs that volcanic eruptions represent and leads to a growing demand for implementing effective risk mitigation measures. To fully evaluate the potential damage and losses that a volcanic eruption disaster may cause, the distribution and characterization of all the exposed elements must be considered. Over the past decades, advances in satellite remote sensing and geographic information system techniques have greatly assisted the collection of land cover data. However, assessment of the elements at risk is a lengthy and time-consuming process. In fact, usually data including all exposed elements and land uses are gathered from several Institutional web portals and very high-resolution satellite imagery, not freely available, manipulated by operators. Here, we propose a deep learning approach to automatically identify the elements at risk in high spatial resolution satellite images. In particular, a Convolutional Neural Network (CNN) model is adopted to classify land use and land cover in volcanic areas thus allowing to carefully assess the total exposure by using freely available satellite images. A retrospective analysis is conducted on Mount Etna highlighting changes in the exposure over the last decade.

How to cite: Corradino, C., Pious, A., Amato, E., Torrisi, F., Bucolo, M., Fortuna, L., and Del Negro, C.: Assessing the elements at risk in volcanic areas by combining deep convolutional neural network and multispectral satellite images, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4568, https://doi.org/10.5194/egusphere-egu22-4568, 2022.

Strong explosive eruptions of volcanoes are the most dangerous for aircraft because they can produce in a few hours or days to the atmosphere and the stratosphere till several cubic kilometers of volcanic ash and aerosols. Ash plumes and the clouds, depending on the power of the eruption, the strength and wind speed, can travel thousands of kilometers from the volcano for several days, remaining hazardous to aircraft, as the melting temperature of small particles of ash below the operating temperature of jet engines.

There are 30 active volcanoes in the Kamchatka; scientists of KVERT monitor these volcanoes since 1993. Description of volcanic eruptions is based on video monitoring and various satellite data from the information system "Remote monitoring of the activity of volcanoes of the Kamchatka and the Kuriles" (VolSatView, http://kamchatka.volcanoes.smislab.ru). In 2021, three volcanoes (Sheveluch, Klyuchevskoy, and Karymsky) had eruptions.

The eruptive activity of Sheveluch (growth of the lava dome) is continuing since 1980. In 2021, explosions sent ash up to 7.5 km a.s.l. mainly in August and December; ash plumes were extending more 380 km to the different directions of the volcano. A new plastic lava block Dolphin-2 squeezed at the dome from February till July 2021. Resuspended ash was observed on 02-03 April, 06-07 July, 13-14 and 22 August, and 06-07 and 21 October: ash plumes were extending for 400 km to the east and southeast of the volcano. Satellite data by KVERT showed a thermal anomaly over the volcano all year. Activity of the volcano was dangerous to local aviation.

The terminal explosive-effusive eruptions of Klyuchevskoy volcano took place from 30 September, 2020 to 08 February, 2021. Explosions sent ash up to 8 km a.s.l., gas-steam plumes containing some amount of ash were extending for 500 km to the different directions of the volcano. The lava flows moved along Apakhonchichsky and Kozyrevsky chutes. Satellite data by KVERT showed a thermal anomaly over the volcano all year. The lateral break on the northwestern slope of Klyuchevskoy at an altitude of 2.8 km a.s.l. lasted from 17 February to 20 March, 2021: lava effused from two cracks, a cinder cone 60 m high was formed. By February 23, lava flows 1.2 km long reached the Erman glacier, mud flows passed about 30 km. Activity of the volcano was dangerous to international and local aviation.

Eruptive activity of Karymsky volcano was uneven in 2021. According to satellite data, the strong ash explosions were observed: on 04 April (8.5 km a.s.l.), 10 September (7 km a.s.l.), 03 November (11 km a.s.l.), and 06, 13, and 18 November (8 km a.s.l.); in the other months explosions sent ash up to 6 km a.s.l.; ash plumes and clouds drifted for 2700 km to the different directions from the volcano. The thermal anomaly over the volcano was recorded on satellite images from time to time. Activity of the volcano was dangerous to international and local aviation.

How to cite: Girina, O., Manevich, A., Melnikov, D., Nuzhdaev, A., Romanova, I., Loupian, E., and Sorokin, A.: The 2021 Activity of Kamchatkan Volcanoes and Danger to Aviation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1862, https://doi.org/10.5194/egusphere-egu22-1862, 2022.

In this study we present the results of tomography studies for seismic velocity in the area of Kambalny volcano (Southern Kamchatka). After a long repose stage, on March 24, 2017, it produced a strong phreatic eruption, which ejected an ash cloud to the distance of up to 1000 km. We have obtained the first 3D model of seismic velocities beneath the area of Kambalny based on the data recorded by a temporal network of ten seismic stations installed for one year in 2018-2019. The distributions of velocities of the P and S seismic waves, and especially the Vp/Vs ratio, provide the information on the geometry of the plumbing system beneath the volcano in the upper crust down to ~10 km, which makes it possible to build a scenario of preparation and occurrence of the explosive eruption in 2017. We clearly identify an anomaly of high Vp/Vs ratio in the depth interval of 7-10 km, which is interpreted as a magma reservoir responsible for Holocene activity of Kambalny. This reservoir appears to be connected with the volcano edifice by a linear zone of high Vp/Vs ratio, which may represent a system of fractures originated during the eruption in March 2017 and served as a pathway for magma ascent. We propose that the interaction of hot magma with meteoric fluids in shallow layers caused active boiling and steam formation in a closed reservoir below the volcano. After exceeding a critical pressure, the steam escaped to the surface causing an explosive eruption. We also found evidence that geothermal fields located to the north and northwest of Kambalny might be fed from separate deep sources. The area of Kambalny is characterized by strong geothermal activity, most of which is located to the north and to the west of the volcano. The northern geothermal manifestations mostly occur on the northern part of the Kambalny Ridge and in the Pauzhetka depression. We found that the geothermal activity in these areas is likely associated with a deep source, which appears to be isolated from the magma reservoir below Kambalny volcano. A similar isolated anomaly is observed below geothermal fields in the area of the Koshelev volcano to the west, which may indicate that the geothermal activity appears to be independent of the magmatic system of Kambalny volcano, at least for its upper-crustal part.

This study was partially supported by the Russian Science Foundation project # 20-17-00075.

How to cite: Komzeleva, V., Koulakov, I., Rychagov, S., Gordeev, E., Abkadyrov, I., Stupina, T., and Novgorodova, A.: The structure of the upper crust under the Kambalny volcano (Southern Kamchatka) according to the results of seismic tomography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2115, https://doi.org/10.5194/egusphere-egu22-2115, 2022.

The Klyuchevskoy group of volcanoes (KGV) is a unique complex, which includes extremely productive volcanoes with variable composition and eruption regimes. During the past ten years, a considerable progress in understanding the deep processes beneath KGV was achieved owing to a number of seismic tomography studies based on data of permanent and temporary seismic networks. The purpose of this review consists in summarizing and systematizing these results and in building an integral model of volcano feeding systems beneath KGV.

The regional scale mantle tomography model shows the presence of high-velocity slabs beneath the Kamchatka and Aleutian arcs and a clearly pronounced gap between them. On a crustal scale, seismic velocity structures and seismicity highlight different types of feeding systems beneath separate volcanoes. Beneath Klyuchevskoy, the seismicity traces a "vertical pipe" that delivers magmatic material from a mantle source to the surface. A prominent low-velocity anomaly beneath Bezymianny represents an area of accumulation and fractioning of magma in the middle crust. Linear velocity anomalies and earthquake lineaments beneath the Tolbachinsky complex mark fault zones serving as pathways for rapid ascent of basaltic magma.

The detailed structure of the mantle wedge beneath the Klyuchevskoy group and surroundings was studied based on the data of a large temporary seismic network with more than a hundred seismic stations installed within the KISS Project. Beneath the Klyuchevskoy volcano, the Vp/Vs distribution reveals three flows of melts and volatiles coming out from the slab at depths of 100, 120, and 150 km. These flows unite at shallower depths and form a large reservoir at the base of the crust that feeds the Klyuchevskoy volcano. The low-velocity anomalies of the P and S waves in the mantle wedge indicate the hot asthenospheric flow vertically ascending through the slab window below Shiveluch volcano, and then spreading horizontally toward the volcanoes of the Klyuchevskoy Group. The presence of this flow together with active release of fluids from the slab are the main causes of the extremely high activity of the volcanoes of the Klyuchevskoy group.

The detailed structure of the magmatic system in the upper crust beneath Bezymianny was studied based on the data of a local seismic network, installed a few months before a strong explosive eruption occurred on December 20, 2017. The derived 3D seismic velocity distribution beneath Bezymianny illuminates its eruptive state days before the eruption. It infers the coexistence of magma and gas reservoirs revealed as anomalies of low (1.5) and high (2.0) Vp/Vs ratios, respectively, located at depths of 2-3 km and only 2 km apart. The reservoirs both control the current eruptive activity: while the magma reservoir is responsible for episodic dome growth and lava flow emplacements, the spatially separated gas reservoir may control short but powerful explosive eruptions of Bezymianny.

This research was supported by the Russian Science Foundation Grant #20-17-00075.

How to cite: Koulakov, I.: Multiscale structure of magma feeding system between the Klyuchevskoy volcano group in Kamchatka, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1061, https://doi.org/10.5194/egusphere-egu22-1061, 2022.

The Galápagos Archipelago originates from a plume-like structure that rises from the mantle about 250 km south of the islands. The Isabela Island, located on the western part of the Archipelago, contains several of the most active volcanoes in Galápagos, among them Alcedo, Cerro Azul, and Sierra Negra, whose last eruptions occurred in 1953, 2008, and 2018, respectively.

Several studies from different disciplines have been performed to image the subsurface structures at the volcanoes on Isabela. They report a melt-rich sill located at 2 km depth, a crystal-mush zone below Sierra Negra located at depths approximately between 8 to 15 km, and a magma intrusion for depths between the sill and the crystal mush before the 2010 eruption of Sierra Negra. However, the resolution of these studies is limited along many areas and depths because of multiple reasons, like non-ideal station distribution, limitations on the selected methodologies, or sparse earthquake locations.

Using seismic data recorded by two temporal seismic networks deployed in the Archipelago, we used the ambient seismic noise to obtain a 3D S-wave velocity model; we used this information to improve the understanding of the structure of the subsurface in the area. One of the networks -XE array- was composed of 18 stations deployed between July 2009 and June 2011; the second network -YH array, composed of 10 stations, was deployed between August 1999 and March 2003. Provided the distribution of the seismic stations, a higher resolution was obtained on Isabella Island. Therefore, we focused our analysis on the regional-scale feeding systems of the volcanoes in Isabela, in particular, Alcedo, Sierra Negra, and Cerro Azul volcanoes.

Through an iterative linear-least-squares inversion methodology, we obtained Rayleigh phase-velocity maps for periods in the range 2.5-25 s. Subsequently, we inverted the obtained tomographic maps for retrieving the S-wave velocity distribution as a function of depth. Our results indicate two main discontinuities, located at 3 and 11 km depth, agreeing with the expected depth for the discontinuity between old and new oceanic crust. The first layer presents an average S-wave velocity of 2.4 km/s, while the second and third layers - 3.0 km/s and 3.4 km/s, respectively. Our results show two relevant low-velocity zones in the subsurface: one is located between Sierra Negra and Alcedo volcanoes centered at 20 km depth, the second one is below Sierra Negra at 8 km depth, which we interpret as magma accumulation zones. In addition, our results show a high-velocity zone at 3 km depth, coincident with the previously reported melt-rich sill.

This work not only validates the results obtained by previous works but provides information with higher resolution for certain depths of the subsurface of hazardous volcanoes on Galápagos.

How to cite: Casas, J. A., Magrini, F., Kaus, B., Badi, G., Ruiz, M. Z., Ebinger, C., Draganov, D., and De Siena, L.: S-wave velocity structure at the Galápagos Archipelago (Ecuador) using ambient seismic noise, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2846, https://doi.org/10.5194/egusphere-egu22-2846, 2022.

Oldoinyo Lengai in the North Tanzanian Divergence is one of the few highly active volcanoes in Africa. Its eruptive cycle is characterized by effusions of carbonatite lava and severe explosions. The most recent of these occurred in 2007 and left a circular crater nearly 400 wide and approximately 100 m deep. The crater is currently being filled with new lava which solidifies and has formed several characteristic hornitos. In 2019, we set up a temporary seismic network of 10 short-period stations, equipped with 4.5 Hz geophones, surrounding the crater area at altitudes between about 1990 and 2885 m to monitor the eruptive activity of the volcano. Seven of the stations were recovered in February 2020. The retrieval of the remaining stations was delayed due travel restrictions caused by the pandemic. However, in Sept. 2021, two of the missing stations were returned from the volcano. Due to the limited battery capacity, recordings were restricted to a period of about five weeks between 14/09 and 23/10/2019. Analysis of the data shows tremor activity and more than 80 distinct recordings of high-frequency seismic signals in the immediate vicinity of the network. However, the recordings lack clear S-wave arrivals, and the station configuration is unfavorable for the application of classical localization techniques based on iterative inversion. We, therefore, apply a grid-search approach based on a Bayesian formulation which also accounts for the topography and shape of the volcanic edifice. The results show that the events are located within or close to the circular crater rim. We argue that the events are caused by sliding segments of the crater wall which have become gravitationally unstable, possibly due to magmatic undermining. The interpretation is supported by surface observations of opening cracks at the outer base of the crater rim.

How to cite: Rümpker, G., Kaviani, A., Laizer, A., Reiss, M., and Kazimoto, E.: Seismic signals of crater instability at Oldoinyo Lengai volcano, Tanzania, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11583, https://doi.org/10.5194/egusphere-egu22-11583, 2022.

Oldoinyo Lengai volcano, located in the Natron Basin (Tanzania), is the only active natrocarbonatite volcano worldwide. It thus represents an essential end-member magmatic system in a young rift segment (~3 Ma) of the East African Rift System. Following a period of relative quiescence after the 2007-08 explosive eruption and dike intrusions beneath the volcano itself and neighbouring inactive shield volcano Gelai, seismicity and effusive lava flows within the crater show a heightened level of activity since 2019. Employing data from a recent seismic experiment, Reiss et al. 2021 used seismicity and focal mechanisms patterns to map the complex volcanic plumbing system and its impact on rift processes.

Here, we use the recorded waveforms of local earthquakes to employ the newly developed 3D multi-scale reasonable attenuation tomography (MuRAT) to constrain the complex volcanic plumbing system in unprecedented detail. Our attenuation analysis measures peak delay and coda wave attenuation to separately measure seismic scattering, attenuation and absorption and model those parameters in 3D. Compared to a classical travel time tomography, this allows us to map seismic interfaces such as faults, fluid reservoirs and melt batches. We use over 20 000 waveforms and perform a separate inversion for coda wave attenuation and a regionalisation for peak delay measurements in different frequencies, which are sensitive to different structures and depths.

While the lower frequencies are sensitive to larger-scale features and structures close to the surface, the higher frequencies provide better resolution on smaller features and structures at depth. Accordingly, we map different aspects of the complex 3D plumbing system of Oldoinyo Lengai and the rift itself in different frequencies. Our results show strong scattering and attenuation near fluid-filled, deep-reaching faults, producing seismic swarms. We also detect the existence of previously unknown, small magma reservoirs in the shallowest part of the crust that might have fed previous dike intrusions and clearly shows an interconnected plumbing system stretching from the border fault across a developing magmatic rift segment.

How to cite: Reiss, M. C., de Siena, L., and Muirhead, J.: The complex plumbing system of Oldoinyo Lengai seen by 3D attenuation tomography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5087, https://doi.org/10.5194/egusphere-egu22-5087, 2022.

Taupō volcano, in the centre of North Island, Aotearoa New Zealand, is a frequently active rhyolitic caldera volcano that was the site of Earth’s most recent supereruption (Oruanui ~25 ka)^1,2. It has erupted 28 times since then, and continues to display signs of unrest (seismicity and surface deformation), with periods of elevated unrest on roughly decadal timescales³. Any resumption of eruptive activity at the volcano poses a major source of hazard, and interactions between the magma reservoir and the regional tectonics that lead to unrest and possible eruption are not well understood. The location of the modern magma reservoir has been previously constrained by study of past eruptive products and some geophysical imaging (gravity, broad-scale tomography)². Earthquake patterns during a 2019 unrest episode have also been used to infer the location and size (>~250 km³) of the modern-day reservoir⁴, but its location and extent have not yet been directly imaged. As part of the interdisciplinary ECLIPSE project, seismological methods are being used to investigate the Taupō reservoir, combining data from the national GeoNet seismic network with records from a temporary 13 broadband seismometer network. Development of the ECLIPSE network approximately doubles the number of seismic stations within 10 km of the lake shore.

We present here initial results on the characterisation of the seismicity in the Taupō region. These results include the improvement of earthquake locations with the addition of picks from the ECLIPSE stations and the use of automated machine learning phase picking and association techniques. We also present initial results from the cross correlation of ambient noise between stations in the ECLIPSE network for the use in ambient noise surface wave tomography, with many of the station pairs crossing the region most likely to contain the modern-day magma reservoir.

¹ Wilson CJN J. Volcanol Geotherm Res 112, 133 (2001)
² Barker SJ et al. NZ J Geol Geophys 64, 320 (2021)
³ Potter SH et al. Bull Volcanol 77, 78 (2015)
⁴ Illsley‐Kemp F et al. G-cubed 22, e2021GC009803 (2021)

How to cite: Mestel, E., Illsley-Kemp, F., Savage, M., Wilson, C., and Smith, B.: Using seismology to probe the modern magma reservoir at Taupō volcano, Aotearoa New Zealand, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3187, https://doi.org/10.5194/egusphere-egu22-3187, 2022.

Recent technological advances in broadband seismic instrumentation allow operators to increase station density and installation flexibility on active volcanoes while increasing the observable frequency bandwidth compared with traditional geophone arrays.

Large quantities of instruments increase the footprint or increase the density of an array due to reduced costs of sensors and improved power specifications requiring less auxiliary equipment. This also allows installation in previously inaccessible areas due to portability, widening the scope of array design.

Traditionally, the Güralp 6-series and 40-series instruments have often been popular on volcanic sites due to their ruggedness and simplicity to operate. Advances in Güralp’s pioneering engineering mean that operators are increasingly looking towards new instrumentation: Certimus and Certis.

This new family of instruments presents digital and analogue options of a triaxial broadband sensor that functions at any angle without any need for human intervention. This is especially useful for rapid installations where time is of the essence; there is no need to level the instrument during installation, vastly reducing field complications and deployment times. This feature has been extensively deployed in glacial regions of Iceland where instrument tilt would have prevented previous installations but where the Certimus has triumphed in providing data on sub-glacial volcanic activity.

A user-configurable long period corner between 120s, 10s and 1s allows the operator to alter the response of their instrument depending on the requirement after delivery. Therefore, an array of short-period sensors is immediately adjusted to become a long-period array either locally or remotely.

Sub-300mW power consumption means both Certimus and Certis can be deployed with very small batteries and solar panels. GSL has also developed a compact lithium-ion battery pack to be used with the instruments for the very purpose of remote installations where lead-acid batteries cannot be transported.

Beneath the surface, the same technology is deployed in boreholes and postholes through the narrow-diameter Radian seismometer. A network of 17 Radian instruments is deployed across Mount Teide on the island of Tenerife, cored into the volcano itself to improve noise performance in this remote area.

When utilising instruments such as Certis and Radian that require a datalogger, the Güralp Minimus provides scope for incorporating other auxiliary meteorological, geochemical or geophysical sensors into a single station. As standard, the Minimus increases the number of analogue input channels beyond what is required for a triaxial seismometer which in turn increases the possibility of an observatory-style station.

In addition to land-based technology, Güralp has supplied several Ocean Bottom Seismometer (“OBS”) systems to clients monitoring volcanic activity at axial seamounts. As well as using cabled OBS systems, autonomous units are deployed to increase the spatial footprint of volcanic island arrays and therefore gain greater understanding of volcanic structure at depth.

How to cite: Watkiss, N., Barbara, R., Cilia, M., Reis, W., Mohr, S., and Hill, P.: Smart seismic instrumentation for volcanic networks, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8126, https://doi.org/10.5194/egusphere-egu22-8126, 2022.

The atmospheric injection of gas and material produced by an explosive volcanic eruption determines a rapid compression of the atmosphere, which subsequently propagates as longitudinal elastic waves (sound). The size of the source, generally greater than tens of meters, and its duration, longer than few seconds, result into an emitted signal that is particularly rich in low frequency (f < 20 Hz), thus determining an efficient infrasound radiation. Thanks to the low spectral content and the reduced attenuation in the atmosphere, infrasound is capable of propagating for very large distances.

In this study we show how the infrasonic monitoring of volcanoes at regional distances (> 100 km) is efficient in recording and characterizing volcanic events. For the purpose of our study, detections from the Yasur volcano (Tanna Island, Vanuatu) registered at a source-to-receiver distance of 400 km by the IS22 infrasound array, located in New Caledonia and part of the Comprehensive nuclear Test Ban Treaty (CTBT) International Monitoring System (IMS), were studied for a period of eleven years (2008-2018). The predominantly explosive Strombolian activity of this volcano makes it a perfect subject to be studied by infrasound technology.

Detections of infrasound signals from Yasur volcano, that are modulated according to the seasonal variation of stratospheric winds, are corrected for attenuation accounting for real atmospheric specification between the source and the receiver to retrieve the pressure at the source: next, they are used to evaluate long term (yearly) and short term (hourly) variations of activity over the period of analysis. Results are eventually compared with thermal anomalies recorded by the MODIS (MODerate resolution Imaging Spectroradiometer) installed on NASA's Terra and Aqua satellites and computed by the MIROVA hotspot detection system.

We show that even at regional (400 km) distances it is possible to follow the fluctuations of ordinary explosive activity during periods of optimal propagation of infrasonic waves in the atmosphere, In addition, we show that, when the signal is recorded, the time resolution retrieved from the analysis allows following variations of activity at hourly time scale, thus representing a valuable source of information, in particular in areas where local geophysical observation is missing.

How to cite: Morelli, R. S., Campus, P., Coppola, D., and Marchetti, E.: Analysis of volcanic activity of Yasur volcano with long range infrasound observation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7418, https://doi.org/10.5194/egusphere-egu22-7418, 2022.

Explosive volcanic eruptions often produce a repeatable pattern of electrical activity that can be exploited for volcano hazard monitoring. First, a swarm of small “vent discharges” occurs within the gas thrust region of the plume starting at the onset of an explosion. Vent discharges often persist for several seconds, depending on the duration of the explosion. In addition, vent discharges are known to occur in high-intensity explosions involving the fragmentation of fresh magma. Several seconds after the onset of an explosion, lightning starts to occur throughout the eruption column as charge begins to separate. This chronological sequence of vent discharges followed by lightning has been observed during eruptions from several different volcanoes, including Augustine Volcano, Redoubt Volcano, Eyjafjallajokull, and Sakurajima. In this presentation we demonstrate a proof-of-concept method for an eruption detection algorithm that exploits this common and repeatable pattern. The algorithm leverages a logistic regression classifier to distinguish between radio frequency waveforms of vent discharges and lightning. To demonstrate our method, we use broadband (20-80 MHz) very high frequency (VHF) waveform data of explosive volcanic eruptions from the Minamidake crater of Sakurajima volcano in Japan collected between May 2019 and May 2020. We show that individual VHF impulses produced by vent discharges and lightning can be accurately classified due to differences in the amount of signal clutter surrounding each type of impulse. In particular, we show that impulses from vent discharges are more isolated in time compared to impulses from lightning. The results of the signal classifier are then used to identify the characteristic pattern of volcanic electrical activity to determine if an explosive event has occurred. Implementation of the detection algorithm on an agile and deployable VHF sensor would engender a new method of volcano hazard monitoring, and help facilitate the research necessary to operationalize measurements of volcanic electrical activity in order to inform an eruption response.

How to cite: Behnke, S., Edens, H., Theiler, J., Swanson, D., Senay, S., Iguchi, M., and Miki, D.: Using radio frequency signal classification to monitor explosive eruptive activity, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2972, https://doi.org/10.5194/egusphere-egu22-2972, 2022.

Two paroxysmal explosions occurred on Stromboli in the summer of 2019 (July 3 and August 28). The first of these explosions resulted in the death of one person. Furthermore, an effusive phase began on July 3 and lasted until August 30, 2019. This dangerous eruptive phase of Stromboli was not preceded by evident variations in the geophysical parameters routinely monitored, therefore the volcano was considered to be in a state of normal activity.

To investigate the precursors of the 2019 eruptive crisis and explain the absence of variations in the parameters routinely monitored, we analyzed the seismo-acoustic signals with an unsupervised neural network capable of discovering hidden structures of the data. We clustered about 14,200 seismo-acoustic events recorded in 10 months (November 15, 2018 - September 15, 2019) using a Self-Organizing Map (SOM). Then we compared the clustering result with the images of visible and thermal monitoring cameras, that were installed and managed by the Istituto Nazionale di Geofisica e Vulcanologia, Italy, and with the Ground-Based Interferometric Synthetic Aperture Radar displacement measurements of the summit area of the volcano recorded by BGInSAR devices, which were installed and managed by Università Degli Studi di Firenze, Italy.

The SOM analysis of the seismo-acoustic features associated with the selected dataset of explosions allowed us to recognize three main clusters in the period November 15, 2018 - September 15, 2019. We named these three clusters Red, Blue, and Green. The analysis of a subset of the selected explosions (approximately 180 events) through the videos of the visible and thermal monitoring cameras allowed us to associate distinct explosive types to the three main seismo-acoustic clusters. In particular, the cluster Red was associated with explosions characterized by well collimated oriented jets of ~ 200 m height, which eject incandescent ballistics and produce a significant infrasonic transient. The cluster Blue was associated with gas explosions with a height of 10 - 20 m and with little or no ash and pyroclastic fragment ejection. These types of explosions may not be detected by the camera recordings and infrasonic sensors. On the contrary, they are well recorded in the VLP seismic signals (filtered in the 0.05 - 0.5 Hz frequency band). The cluster Green includes explosions characterized by the emission of incandescent spatter-like fragments, with a wide range of ejection angles and hemispherical shape. The explosions of the cluster Red are mainly generated in the NE vent region, whereas the explosions of clusters Blue and Green are generally located in the central and SW vent regions.

Comparing these results with the temporal evolution of the displacement of the summit area measured by the GBInSAR device, we discovered that the variations of the eruptive style that were highlighted by the SOM clustering of the seismic-acoustic features are recognizable in the ground deformation temporal pattern. Our findings are relevant for the improvement of monitoring of volcanoes with persistent activity and volcano early warning.

How to cite: Giudicepietro, F., Calvari, S., D'Auria, L., Di Traglia, F., Layer, L., Macedonio, G., Caputo, T., De Cesare, W., Ganci, G., Martini, M., Orazi, M., Peluso, R., Scarpato, G., Spina, L., Nolesini, T., Casagli, N., Tramelli, A., and Esposito, A. M.: Variations of Stromboli activity related to the 2019 paroxysmal phase revealed by SOM clustering of seismo-acoustic data and its comparison with video recordings and GBInSAR measurements, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10482, https://doi.org/10.5194/egusphere-egu22-10482, 2022.

The determination of Eruptive Source Parameters (ESPs) is crucial especially for very active volcanoes whose eruptive intensity can vary significantly. In this aim, new strategies are being developed to determine in near real time the total erupted mass (TEM), total grain-size distribution (TGSD) and plume height from ground sampling and remote sensing methods. Since 2011, Etna volcano has produced about 100 paroxysmal episodes characterized by the emission of fountain-fed tephra plumes whose heights reached up to 15 km (above sea level). In this work, we present multi-sensor strategies based on data acquired by the complementary set of remote sensing systems available at Etna. In fact, multi-sensor strategies may help to refine and assess the uncertainty of ESP estimates made by individual sensors, which can present various limitations such as narrow field of views (e.g., visible imagery) and/or low temporal resolution (e.g., satellite-based infrared). First, we show how the combination between tephra-fallout deposit and satellite-based estimates, along with numerical modelling, can help to refine estimates of TEM and TGSD, especially for weak explosive eruption such as the 29 August 2011 paroxysm. We use the model TEPHRA2 and compute synthetic data of ground accumulation to successfully fill significant sampling gaps in the tephra-fallout deposits. Moreover, we find that the Rosin-Rammler equation can be used to inform on missing part of the TGSD, including the tail of very fine ash also detected by satellite-based platforms. Additionally, we compare all estimates of Mass Eruption Rates, Plume height and grain-size distributions made by all available methods including Doppler radar detection, visible and infrared imagery, infrasound arrays, gravimetric signals and tephra-fallout deposit sampling. Accordingly, based on each sensor limitation and capacities, we obtain new constraints on ESP estimates acquired during several paroxysms between 2011-2013 and February 2021. We also bring new insights into the differences and complementarities that exist between the available remote sensing methods, especially in the case of future eruptive events at Mount Etna.

How to cite: Freret-Lorgeril, V., Bonadonna, C., Carbone, D., Corradini, S., Donnadieu, F., Guerrieri, L., Gurioli, L., Lacanna, G., Lemus, J., Marzano, F. S., Mereu, L., Merucci, L., Passarelli, L., Ripepe, M., Rossi, E., Scollo, S., and Stelitano, D.: Determination of eruption source parameters of the 2011-2013 and February 2021 Etna’s paroxysms using multi-sensor strategies., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9104, https://doi.org/10.5194/egusphere-egu22-9104, 2022.

An important characteristic quantity of volcanoes is the temperature of their magma. It depends on the magma composition, the volcanic activity, and partly affects the composition of magmatic gases that are later released to the atmosphere. Lava temperature measurements are thus desired for a manifold of volcanic studies at volcanoes including open magma-atmosphere interface (e.g. lava lakes).

The mostly used commercially available thermal cameras for the relevant temperature range (ca. 600-1200 °C) are still rather expensive, bulky, and have a limited spatial resolution.

We present an approach to use a compact (‘point and shoot’) consumer digital camera with a silicon based detector as a thermometer to record the spatial temperature distribution and variations of volcanic lava. Silicon detectors are commonly sensitive in the near infrared wavelength range (until ca. 1100 nm), which readily allows measurements of temperatures above ca. 500 °C. The camera is modified to block the visible spectrum and the remaining colour filter (Bayer filter) characteristics are used to infer the temperature from differential intensity measurements.

In the frame of this work, we performed a sensitivity study and calibrated the camera with a heated wire in the range of 600-1100 °C. Besides the advantages of the low costs, superior mobility and simple handling, the 16 megapixel spatial resolution of the temperature measurement allows resolving detailed temperature distributions in highly dynamic volcanic emission processes.

How to cite: Marks, J., Kuhn, J., Fuchs, C., Bobrowski, N., and Platt, U.: Quantifying lava temperature with a low-cost silicon-based thermal camera, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7523, https://doi.org/10.5194/egusphere-egu22-7523, 2022.

In October 2017, two remarkable seismic swarms interrupted a 46-year seismic silence in Cumbre Vieja volcanic system, La Palma, Canary Islands, Spain. As a response to this seismic unrest episode, INVOLCAN strengthened the volcano monitoring in the island with the installation of a new automatic geochemical station in the municipality of Fuencaliente (LPG08) in the southern part of the island, which included a Delta Ray^TM Isotope Ratio Infrared Spectrometer (Thermo Fisher Scientific), to measure the content and isotopic composition (δ¹³C-CO₂) of the soil gas CO₂ using a PVC trap buried in the soil at 40 cm depth and transporting the gas through a polyamide pipe. After different seismic swarms occurred in the following years, a volcanic eruption started in Cumbre Vieja on September 19, 2021, lasting 85 days and 8 hours, the longest historical eruption in the island. On September 22, 2021, INVOLCAN installed an additional automatic geochemical station in the municipality of Los Llanos de Aridane (LPG10, around 5 km far from the eruption site) in the western part of the island, including another DeltaRay^TM analyzer. In this work, we show the results from August 2020 to December 2021 measured at LPG08, and from September 2021 to January 2021 measured at LPG10. LPG08 data showed a range of δ¹³C-CO₂ from -24.3 to -17.9‰ vs. VPDB (this last value just before the eruption started), with an average value of -20.9‰, during the study period. A clearly increasing trend to less negative values of δ¹³C-CO₂ was detected from the beginning of 2021 to the moment when the eruption started, showing an increasing magmatic component in the soil CO₂ measured, which was corroborated by plotting δ¹³C-CO₂ vs. 1/[CO₂] mean monthly values. During and after the eruptive period, the values showed a decreasing trend. Regarding LPG10, the values ranged from -18.8 to -7.3‰ vs. VPDB, with a mean value of -13.4‰. In this case, a general decrease trend of the δ¹³C-CO₂ values to more negative values was observed after the eruption finished, while mean monthly values in the δ¹³C-CO₂ vs. 1/[CO₂] plot showed a shift from values ​​with a higher contribution of deep-seated CO₂ at the beginning of the eruption to values ​​with a lower contribution at its end. This data demonstrates that the continuous measuring of carbon isotopic composition in soil gases before, during and after a volcanic eruption constitutes a powerful new tool for volcano monitoring.

How to cite: Asensio-Ramos, M., Padrón, E., Barrancos, J., Hernández, P. A., Melián, G. V., Rodríguez, F., Padilla, G. D., and Pérez, N. M.: Continuous measurement of carbon isotopic composition in soil gases at Cumbre Vieja volcano: a new frontier in volcano monitoring, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8367, https://doi.org/10.5194/egusphere-egu22-8367, 2022.

Located about 30 km North of the city of Yogyakarta on Java island, Merapi is considered one of the most dangerous dome building stratovolcanoes, as about 2 million people live less than 30 km away from the crater. Its recent eruptive activity consists in cyclic effusive growth of viscous lava domes, followed by partial or total destruction of domes. Dome destruction favors gravitational collapses (VEI 2) every 4-5 years, or bigger explosive eruptions (VEI 3-4) every 50-100 years resulting in pyroclastic density currents (PDCs) driven downhill at high velocities that are a major risk for surrounding population. Therefore, it is crucial to assess precisely the location, the shape, the thickness, and the volume of emplaced lava in order to prevent populations from sudden PDCs.

The last major explosive eruption (VEI 3-4) occurred in November 2010, resulting in a horseshoe-shaped crater of 500 m wide and 250 m depth hosting a lava dome shaped like a plateau. Within the crater, a new dome appeared on 11 August 2018 and was partially destroyed as of late 2019. In this study, we take advantage of 2 high resolution remote-sensing datasets, Pléiades (optical acquisitions in tri-stereo mode, 1 m resolution) and TanDEM-X (radar acquisitions in StripMap mode, 2 m resolution), to produce 19 Digital Elevation Models (DEMs) between July 2018 and December 2019. We calculate the difference in elevation between each DEM and a reference DEM derived from Pléiades images acquired in 2013 in order to track the evolution of the dome in the crater between 2018 and 2019. Uncertainties are quantified for each dataset. We show that the DEMs derived from Pléiades (optical) and TanDEM-X (radar) data are consistent with each other and provide good spatio-temporal constraints on the evolution of the dome. Furthermore, the remote-sensing estimate of lava volume is consistent with local drone measurements carried on by BPPTKG at the time of dome growth.

The time period covered by the TanDEM-X data is larger than that covered by the Pléiades acquisitions, allowing coverage of the growth and destruction of the dome. However, the Pléiades data allow us to evidence an accumulation zone below the crater that is not well imaged by TanDEM-X. We show the dome reached 40 meters (+-5 m) high and 0.5 Mm³ (+- 0.1Mm³ ) between August 2018 and February 2019, corresponding to an effusion rate of 3000 m³/day. Its shape was initially radial,then extended asymmetrically to the northwest and southeast from October 2018. From February 2019 onwards, the dome elevation remained constant, but lava was continuously emitted, as evidenced by TanDEM-X amplitude maps. Lava supply was balanced by destabilization southwards downhill in an accumulation zone of 400 meters long and 15 meters (+-5m) high maximum. In late 2019, several minor explosions partially destroyed the center of the dome. This study highlights the strong potential of the combination of TanDEM-X and Pléiades DEMs to quantitatively monitor domes at andesitic stratovolcanoes.

How to cite: Gremion, S., Pinel, V., Beauducel, F., Shreve, T., Putra, R., Solikhin, A., Santoso, A. B., and Humaida, H.: Tracking the evolution of the summit lava dome of Merapi volcano, between 2018-2019, using DEMs obtained from TanDEM-X and Pleiades data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9304, https://doi.org/10.5194/egusphere-egu22-9304, 2022.

Ground and satellite SO₂ measurements have been extensively compared for high altitude volcanic emissions but far less for grounded plumes. The 2018 and 2020 Kilauea eruptions offered perfect opportunities to compare our TROPOMI results with ground measurements. Not only is Kilauea a very well monitored volcano, so the ground measurements are abundant and reliable, the SO₂ plumes were big enough to be picked up by satellite. We compared the results to assess the efficacy of TROPOMI as a remote sensing tool applied at low-lying SO₂ plumes. We concluded that the fluxes for both agreed provided the wind speed is the same for both. Remote sensing is therefore an important tool for effusive eruption monitoring and could be used on its own at remote volcanoes where ground instruments are sparse or lacking.

How to cite: Delbrel, J., Burton, M., Hayer, C., Esse, B., and Varnam, M.: Comparing satellite and ground-based measurements of low-lying SO2 plumes during the Kilauea 2018 and 2020 eruptions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10487, https://doi.org/10.5194/egusphere-egu22-10487, 2022.

Videography is a popular tool for monitoring and characterising volcanic eruptions. Video records of lava fountaining episodes allow us to infer eruption parameters such as fountain heights, exit velocities, and pulse durations and frequencies, which may inform us on the subsurface processes that operate within the sub-volcanic plumbing system. However, the evolving shape and size of the natural features surrounding eruptive vent make it difficult to convert pixels in an image to meters in reality, due to the lack of fixed reference points with which to compare dimensions. Here we present a new method for determining the vertical scale in videos of lava fountains. We measure the vertical pixel-position of clasts near their zenith, over successive frames, and convert this to an acceleration. By assuming that the only force acting on single clasts near their zenith is gravity, we use the clast motion to determine the scale – mapping pixels to metres. Geometric considerations around the viewing angle and lens distortions are discussed and corrected for. We validate this method with laboratory experiments using water fountains and vertically projected light plastic balls, which act as analogues for lava fountains and single clasts, respectively. An example of field application is then provided from the 2018 fissure eruption at Kilauea (Hawaii, USA). This approach will be useful to physical volcanologists for monitoring the dynamics of eruptions that produce fountains and/or ballistics from video records, which are becoming increasingly available both from scientific teams and from a wider community of tourists and volcano-enthusiasts.

How to cite: Loisel, A., Llewellin, E., Tisdale, C., and Houghton, B.: Determining the vertical scale in videos of lava fountains from gravitational acceleration of single clasts at their zenith, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12106, https://doi.org/10.5194/egusphere-egu22-12106, 2022.