Search

The ionosphere is a region of the upper atmosphere (50-1000 km a.s.l.) characterized by free electrons and ions produced mainly by solar radiations (UV, X) and subordinately by cosmic radiations (RAVANELLI, 2021). It’s a very sensitive plasma to energy variations, mostly the F2 layer (240-400 km a.s.l.) that is a region of maximum ionization with an electron density of 10 6 e - /cm 3 . For this reason, the ionosphere (in particular the F region) can be seen as a field of remote sensing monitoring from which to extrapolate various informations by the natural systems that make up our planet.About this, solid Earth (e.g. litoshpere, internal structure) and fluid Earth (e.g. idrosphere, atmosphere) are two open systems that exchange energy continuously. It means that big dynamic processes (e.g. plate tectonics, genesis of magmas) can release amounts of energy, in the form of earthquakes, volcanic eruptions and correlate phenomena (e.g. tsunami), capable to perturb the earth’s matter at every aggregation state and up to planetay scale with propagation of gravity-acoustic waves. In this field, the ionospheric volcanology is a targered discipline for the study of the effects that great volcanic eruptions (VEI > 3-4) cause to Total Electron Content (TEC) in the ionosphere (F2 level) through propagation of internal gravity waves (0.1 – 2 mHz) and acoustic waves (2 – 10 mHz). The study of the TEC’s variations caused by strong geodynamic events represents a new approach with which to contribute to implementation of the monitoring and research systems in order to mitigate the volcanic and seismic risks.The method consists to extrapolate the temporal variations of TEC during the volcanic activity period by RINEX and navigational data GNSS registered by RING (Rete Integrata Nazionale GNSS) and local GPS networks. By the way, others outputs can be derived from TEC series such as spectrograms and hodocrones in order to better understand the evolution of the electron activity in ionosphere excited by the volcanic eruption. This study method is applied for some paroxysmal eruptive activities of Mt.Etna analyzing and comparing the volcanological data with TEC outputs. The latter have been processed with VARION (Variometric Approach for Real-time Ionosphere Observation) algorithm, designed within the Geodesy and Geomatics Division of Sapienza University of Rome in 2015. VARION is based on single time differences of geometry-free combination of GNSS carrier-phase measurements, using a standalone GNSS receiver and standard GNSS broadcast products (orbits and clocks correction) that are available in real time. One of the goals to be pursued in the Research is to comprehend the differences between eruptive mechanisms capable to generate gravity waves rather acoustic waves, and how such mechanisms may depend by physical-geometric features of the plumbing system of the volcano and by chemical-physical features of the magma and its amount of gas.

Over the last 20 years, infrasound signals have been used to investigate and monitor active volcanoes during eruptive and degassing activity. In particular, infrasound amplitude information has been used to estimate eruptive parameters such as plume height, magma discharge rate and lava fountain height. Active volcanoes are characterized by pronounced topography and, during eruptive activity, the topography can change rapidly, affecting the observed infrasound amplitudes. While the interaction of infrasonic signals with topography has been investigated by several authors over the past decade, the impact of changing topography on the infrasonic amplitudes has not yet been explored. In this work, the infrasonic signals accompanying 57 lava fountain paroxysms at Mount Etna (Italy) during 2021 were analyzed. In particular, the temporal and spatial variations of the infrasound amplitudes were investigated. During 2021, significant changes in the topography around the most active crater (the South-East Crater) took place and were reconstructed in detail through unoccupied aerial system surveys. Through analysis of the observed infrasound signals and numerical simulations of the acoustic wavefield, we demonstrate that the observed spatial and temporal variation in the infrasound signals can be explained by the combined effects of changes in the location of the acoustic source and changes in the near-vent topography. This work demonstrates the importance of accurate source locations and high-resolution topographic information, particularly in the near-vent region where the topography is most likely to change rapidly. Changing topography should be considered when interpreting local infrasound observations over long time-scales.

Monitoring the state of the sea is a fundamental task for economic activities in the coastal zone, such as transport, tourism and infrastructure design. In recent years, regular wave height monitoring for marine risk assessment and mitigation has become unavoidable as global warming impacts in more intense and frequent swells.In particular, the Mediterranean Sea has been considered as one of the most responsive regions to global warming, which may promote the intensification of hazardous natural phenomena as strong winds, heavy precipitation and high sea waves. Because of the high density population along the Mediterranean coastlines, heavy swells could have major socio-economic consequences. To reduce the impacts of such scenarios, the development of more advanced monitoring systems of the sea state becomes necessary.In the last decade, it has been demonstrated how seismometers can be used to measure sea conditions by exploiting the characteristics of a part of the seismic signal called microseism. Microseism is the continuous seismic signal recorded in the frequency band of 0.05 and 0.4 Hz that is likely generated by interactions of sea waves together and with seafloor or shorelines.In this work, in the framework of i-WaveNET INTERREG project, we performed a regression analysis to develop a model capable of predicting the sea state in the Sicily Channel (Italy) using microseism, acquired by onshore instruments installed in Sicily and Malta. Considering the complexity of the relationship between spatial sea wave height data and seismic data measured at individual stations, we used supervised machine learning (ML) techniques to develop the prediction model. As input data we used the hourly Root Mean Squared (RMS) amplitude of the seismic signal recorded by 14 broadband stations, along the three components, and in different frequency bands, during 2018 - 2021. These stations, belonging to the permanent seismic networks managed by the National Institute of Geophysics and Volcanology INGV and the Department of Geosciences of the University of Malta, consist of three-component broadband seismometers that record at a sampling frequency of 100 Hz.As for the target, the significant sea wave height data from Copernicus Marine Environment Monitoring Service (CMEMS) for the same period were used. Such data is the hindcast product of the Mediterranean Sea Waves forecasting system, with hourly temporal resolution and 1/24° spatial resolution. After a feature selection step, we compared three different kinds of ML algorithms for regression: K-Nearest-Neighbors (KNN), Random Forest (RF) and Light Gradient Boosting (LGB). The hyperparameters were tuned by using a grid-search algorithm, and the best models were selected by cross-validation. Different metrics, such as MAE, R2 and RMSE, were considered to evaluate the generalization capabilities of the models and special attention was paid to evaluate the predictive ability of the models for extreme wave height values.Results show model predictive capabilities good enough to develop a sea monitoring system to complement the systems currently in use.

The Hunga Tonga-Hunga Ha’apai volcano (Pacific Ocean) generated a cataclysmic explosion on 15 January 2022, triggering several atmospheric disturbances at a global scale, as a huge increase in the total electron content (TEC) in the ionosphere, and a pressure wave travelling in the troposphere. We collected and analysed data over the Mediterranean to study these disturbances, and in particular, (i) data from the barometric and infrasonic stations installed on Italian active volcanoes by the Istituto Nazionale di Geofisica e Vulcanologia (INGV) for investigating the tropospheric pressure waves; (ii) barometric data from the INGV-TROPOMAG and SIAS (Sicilian Agro-meteorological Information System) networks, for investigating the interaction between the orography and pressure waves; (iii) ionograms from the Advanced Ionospheric Sounder-INGV ionosonde at Gibilmanna (Sicily, Italy); (iv) data from the RING (Rete Italiana Integrata GNSS) network, to retrieve the ionospheric TEC; (v) soil CO2 flux data from the INGV surveillance network of Vulcano Island. The analysis of the ground-level barometric data highlights that pressure waves were reflected and diffracted by the topographic surface, creating a complex space–time dynamic of the atmospheric disturbances travelling over Sicily, driven by the interference among the different wavefronts. The ionograms show that a medium-scale travelling ionospheric disturbance (MSTID), with a horizontal wavelength of about 220 km and a period of about 35 min, propagated through the ionospheric plasma in the correspondence of the first barometric variations. Moreover, comparing detrended TEC and barometric data, we further confirmed the presence of the aforementioned MSTID together with its close relation to the tropospheric disturbance.

Mount Melbourne and Mount Rittmann are quiescent, although potentially explosive, alkaline volcanoes located 100 km apart in Northern Victoria Land quite close to three stations (Mario Zucchelli Station, Gondwana and Jang Bogo). The earliest investigations on Mount Melbourne started at the end of the 1960s; Mount Rittmann was discovered during the 1988–89 Italian campaign and knowledge of it is more limited due to the extensive ice cover. The first geophysical observations at Mount Melbourne were set up in 1988 by the Italian National Antarctic Research Programme (PNRA), which has recently funded new volcanological, geochemical and geophysical investigations on both volcanoes. Mount Melbourne and Mount Rittmann are active, and are characterized by fumaroles that are fed by volcanic fluid; their seismicity shows typical volcano signals, such as long-period events and tremor. Slow deformative phases have been recognized in the Mount Melbourne summit area. Future implementation of monitoring systems would help to improve our knowledge and enable near-real-time data to be acquired in order to track the evolution of these volcanoes. This would prove extremely useful in volcanic risk mitigation, considering that both Mount Melbourne and Mount Rittmann are potentially capable of producing major explosive activity with a possible risk to large and distant communities.

The 2022 Tonga event highlight the necessity to have more and more knowledge about the activityof volcanoes. To this point, it is well known that volcanoes explosion can trigger ionosphericperturbation detectable through the Global Navigation Satellite System (GNSS) signal [1].The VARION (Variometric Approach for Real-Time Ionosphere Observation) algorithm has beensuccessfully applied to detection of ionospheric perturbations in several real-time scenarios [2, 3].VARION, thus, estimates sTEC (slant total electron content) variations starting from the single timedifferences of geometry-free combinations of GNSS carrier-phase measurements.The aim of this work is to analyse some Etna explosions occurred in 2021 with the VARION algorithmin order to better study the coupling between volcanoes and ionosphere. This study can pave theway to a real-time ionospheric monitoring of Etna volcano.[1] Manta, Fabio, et al. "Correlation between GNSS‐TEC and eruption magnitude supports the useof ionospheric sensing to complement volcanic hazard assessment." Journal of GeophysicalResearch: Solid Earth 126.2 (2021): e2020JB020726.[2] Ravanelli, Michela, et al. "GNSS total variometric approach: first demonstration of a tool forreal-time tsunami genesis estimation." Scientific Reports 11.1 (2021): 1-12.[3] Savastano, Giorgio, et al. "Advantages of geostationary satellites for ionospheric anomalystudies: Ionospheric plasma depletion following a rocket launch." Remote Sensing 11.14 (2019):1734.

On 13 May 2008, a strong eruption took place along a fracture opened on Mt. Etna's eastern flank accompanied by significant seismic activity. Recent studies show that seismic ambient noise is an effective method to observe the fluctuations of seismic wave velocities due to volcanic and seismic activities. In this study, we analyze the temporal seismic velocity changes (dv/v) from January 2007 to June 2008 at Mt. Etna by using seismic ambient noise. We retrieve a large dataset to calculate dv/v because of the large number of permanent seismic stations located at Mt. Etna. Daily cross-correlation functions (CCFs) are computed at 15 stations for 105 interstation pairs in the 1-2 Hz frequency band by using vertical components. We observe that the dv/v displays a gradual reduction and reaches down to -0.3% associated with the aforementioned volcanic activity. We compared our results with volcano-tectonic earthquakes and volcanic tremor. Our study shows a significant relationship between the magma intrusion and decrease of the dv/v.

The gradual shift over time of the spectral lines of harmonic seismic and/or acoustic tremor, that is commonly known as spectral gliding, has been largely observed at different volcanoes. Despite the clear advantage of the experimental approach in providing direct observation of degassing processes and of the related elastic radiation, experimental studies on gliding tremor are lacking. To fill this gap, we investigated different episodes of gliding of acoustic and seismic tremor observed during analogue degassing experiments performed under different experimental conditions, by systematically changing: 1) analogue magma viscosity (10-1,000 Pa s), 2) gas flux (5-180x10-3 l/s) and 3) conduit surface roughness (fractal dimension of 2-2.99). The occurrence of gliding experimental seismic and acoustic tremor was linked to high gas flux rates and viscosities and generally associated with an increasing trend and often preceding a major burst. In a few cases we observed decreasing secondary sets of harmonic spectral lines. Results suggest that gliding episodes are mainly related to the progressive volume variation of shallow interconnected gas pockets. Spectral analyses performed on acoustic signals provided the theoretical length of the resonator. The latter was compared against the temporal evolution of the gas pockets, quantified from video analyses. The similarities between the observed degassing regime and churn-annular flow in high viscous fluids encourages further studies on churn dynamics in volcanic environments.

Deriving eruption source parameters from geophysical data is critical for volcano hazard mitigation, yet remains a challenging task in most volcanoes worldwide. In this work, we explored the temporal relationship between geophysical signals and eruptive parameters measured during six explosive episodes from the New South-East Crater of Mt. Etna (Italy). The quadratic reduced seismic velocity and pressure were calculated to track the temporal variation of volcanic elastic radiation, and the lava fountain height was estimated by thermal camera image processing. The temporal relationships between these geophysical and eruptive time series were studied. In particular, the first considered lava fountain exhibited a “clockwise hysteresis” pattern: higher seismic amplitude with respect to the fountain height during the waxing phase as compared to the waning phase. We also calculated the regression parameters for both linear and power laws, linking seismo-acoustic and eruptive time series. For the linear regressions, we found fairly constant values of the scaling factors in five out of six eruptive episodes, which can be considered as a promising step to derive eruption source parameters from geophysical data in real-time. Regarding power law regressions, a clear relationship was observed between the exponents determined for the power law linking quadratic reduced velocity and lava fountain height, and the time interval duration from the previous eruption. These results suggest that the condition of the uppermost part of the plumbing system (e.g. viscosity of residing magma and conduit conditions) play a key role in the seismic energy generation during the eruptions.

The most continuous and ubiquitous seismic signal on Earth is the microseism, closely related to ocean wave energy coupling with the solid Earth. A peculiar feature of microseism recorded in Antarctica is the link with the sea ice, making the temporal pattern of microseism amplitudes different with respect to the microseism recorded in low-middle latitude regions. Indeed, during austral winters, in Antarctica the oceanic waves cannot efficiently excite seismic energy because of the sea ice in the Southern Ocean. Here, we quantitatively investigate the relationship between microseism, recorded along the Antarctic coasts, and sea ice concentration. In particular, we show a decrease in sea ice sensitivity of microseism, due to the increasing distance from the station recording the seismic signal. The influence seems to strongly reduce for distances above 1,000 km. Finally, we present an algorithm, based on machine learning techniques, allowing to spatially and temporally reconstruct the sea ice distribution around Antarctica based on the microseism amplitudes. This technique will allow reconstructing the sea ice concentration in both Arctic and Antarctica in periods when the satellite images, routinely used for sea ice monitoring, are not available, with wide applications in many fields, first of all climate studies.

During the COVID-19 pandemic, most countries put in place social interventions, restricting the mobility of citizens, to slow the spread of the epidemic. Italy, the first European country severely impacted by the COVID-19 outbreak, applied a sequence of progressive restrictions to reduce human mobility from the end of February to mid-March 2020. Here, we analysed the seismic signatures of these lockdown measures in densely populated eastern Sicily, characterized by the presence of a permanent seismic network used for earthquake and volcanic monitoring. We emphasize how the anthropogenic seismic noise decrease is visible even at stations located in remote areas (Etna and Aeolian Islands) and that the amount of this reduction (reaching ∼ 50 %–60 %), its temporal pattern and spectral content are strongly station-dependent. Concerning the latter, we showed that on average the frequencies above 10 Hz are the most influenced by the anthropogenic seismic noise. We found similarities between the temporal patterns of anthropogenic seismic noise and human mobility, as quantified by the mobile-phone-derived data shared by Google, Facebook and Apple, as well as by ship traffic data. These results further confirm how seismic data, routinely acquired worldwide for seismic and volcanic surveillance, can be used to monitor human mobility too.

Despite Mt. Etna's long-standing reputation as an effusive volcano, since 1986 there has been an evident increase in mid-intensity explosive eruptions from its summit craters, with more than 240 episodes, better known as paroxysms (otherwise called paroxysmal episodes). These are characterized by strong Strombolian to lava fountaining activity that lasts from tens of minutes to a few days, producing some km-high volcanic plumes and tephra fallouts up to hundreds of km on the ground. Most paroxysms give life to sequences which are clustered like “episodic” eruptions for periods of a few days to a few months, their frequent recurrence causing hazard to air traffic and impacting densely inhabited areas. Nonetheless, a list containing the dates and data of these eruptions is lacking. In this paper, we tried to fill this gap by compiling a complete record, including master data (date, crater), eruption style and seismic parameters for identifying, characterizing and quantifying both the individual episodes and the entire period. This information comes from a critical review of surveillance reports, raw-data analysis and scientific literature. A retrieval of homogenous and comparable seismic data was possible only for episodes after 2006 following the renewal of seismic stations. The eruption list provides a complete picture of the 1986–2021 paroxysms, allowing to evaluate their temporal distribution, make a statistical analysis of their time-interval, and undertake a comprehensive investigation of the features of volcanic tremor. The results show a high probability (72%) of having a paroxysmal episode in the 10 days following the previous one. Moreover, a scaling relationship associated with the number-size distribution of the amplitude increases of volcanic tremor accompanying the explosive activities has been constrained. During sequences of paroxysms, combining these outputs can help improve the hazard assessment in terms of frequency of the associated tephra fallouts, and predict the duration of the entire sequence.

Structure and dynamics of fault systems can be investigated using repeating earthquakes as repeatable seismic sources, alongside ground deformation measurements. Here we utilise a dataset of repeating earthquakes which occurred between 2000 and 2019 along the transtensive Pernicana fault system on the northeast flank of Mount Etna, Italy, to investigate the fault structure, as well as the triggering mechanisms of the seismicity. By grouping the repeating earthquakes into families and integrating the seismic data with GPS measurements of ground deformation, we identify four distinct portions of the fault. Each portion shows a different behaviour in terms of seismicity, repeating earthquakes and ground deformation, which we attribute to structural differences including a segmentation of the fault plane at depth. The recurrence intervals of repeating earthquake families display a low degree of regularity which suggests an episodic triggering mechanism, such as magma intrusion, rather than displacement under a constant stress. Repeating earthquakes display variable behaviour across distinct portions of the Pernicana fault system, Mt. Etna, indicating structural differences and episodic triggering, according to a 20 year earthquake dataset, and GPS ground deformation measurements.

During the COVID-19 pandemic, most countries worldwide put in place social interventions, consisting of restricting the mobility of citizens, aimed at slowing and mitigating the spread of the epidemic. In particular, Italy, as the first European country violently struck by the COVID-19 outbreak, applied a sequence of progressive restrictions to reduce both human mobility and human-to-human contacts from the end of February to mid-March 2020. Here, we analysed the seismic signatures of these lockdown measures in the densely populated Eastern Sicily, characterised by the presence of a permanent seismic network used for both seismic and volcanic monitoring. We specifically emphasize how the amount of the amplitude reduction of anthropogenic seismic noise (reaching ~50–60%), its temporal pattern and spectral content are strongly station-dependent. As for the latter, we exhibited that on average the frequencies above 10 Hz are the most influenced by the anthropogenic seismic noise. Finally, we found an impressive similarity between the temporal patterns of anthropogenic seismic noise and human mobility, as quantified by the mobile phone-derived data shared by Google, Facebook and Apple. These results further confirm how seismic data, routinely acquired worldwide for seismic and volcanic surveillance, can be used to monitor human mobility too.

Human activity causes vibrations that propagate into the ground as high-frequency seismic waves. Measures to mitigate the COVID-19 pandemic caused widespread changes in human activity, leading to a months-long reduction in seismic noise of up to 50%. The 2020 seismic noise quiet period is the longest and most prominent global anthropogenic seismic noise reduction on record. While the reduction is strongest at surface seismometers in populated areas, this seismic quiescence extends for many kilometers radially and hundreds of meters in depth. This provides an opportunity to detect subtle signals from subsurface seismic sources that would have been concealed in noisier times and to benchmark sources of anthropogenic noise. A strong correlation between seismic noise and independent measurements of human mobility suggests that seismology provides an absolute, real-time estimate of population dynamics., P.K. was funded by a Royal Society University Research Fellowship (URF\R1\180377). P.B. and M.J. acknowledge support from the International Training Course “Seismology and Seismic Hazard Assessment” funded by the GeoForschungsZentrum Potsdam (GFZ) and the German Federal Foreign Office through the German Humanitarian Assistance program (grant S08-60 321.50 ALL 03/19). P.B also acknowledges financial support from the Boğaziçi University Research Fund (BAP 15683). O.F.C.d.O acknowledges funding from a Young Investigator Grant from the Human Frontier Science Program (HFSP - project number RGY0072/2017). C.P.E. and E.S. acknowledge funding from the HELPOS Project “Hellenic Plate Observing System” (MIS 5002697). L.E. and S.S.-K. acknowledge funding from a VIDI project from the Dutch Research Council (NWO project number 864.14.005). G.A.F. acknowledges contributions from the Observatorio San Calixto, which is supported by the Air Force Technical Application Center (AFTAC). C.R.L. acknowledge funding from the NSF Graduate Research Fellowship Program (grant No. DGE‐1745301). V.-H.M. and R.D.P. acknowledge support from grant CONACYT-299766. R.D.P. acknowledges support from the UNAM-DGAPA postdoctoral scholarship. J.O. acknowledges support from the Agencia Nacional de Investigación y Desarrollo (Scholarship ANID-PFCHA / Doctorado Nacional / 2020-21200903). S.P. acknowledges financial support from the Natural Environment Research Council (NE/R013144/1). A.E.R. acknowledges support from the K.H. Renlund foundation. M.K.S. acknowledges the New Zealand Earthquake Commission (EQC Project No 20796). H.X. acknowledges support from a Multidisciplinary Research on the Coronavirus and its Impacts (MRCI) grant from UC Santa Barbara. The Australian Seismometers in Schools data used in this research are supported by AuScope, enabled by the Australian Commonwealth NCRIS program. A.O. acknowledges support from the project RESIST, funded by the Belgian Federal Science Policy (contract SR/00/305) and the Luxembourg National Research Fund

The significant efforts of the last years in new monitoring techniques and networks have led to large datasets and improved our capabilities to measure volcano conditions. Thus nowadays the challenge is to retrieve information from this huge amount of data to significantly improve our capability to automatically recognize signs of potentially hazardous unrest.Unrest detection from unlabeled data is a particularly challenging task, since the lack of annotations on the temporal localization of these phenomena makes it impossible to train a machine learning model in a supervised way. The proposed approach, therefore, aims at learning unsupervised low-dimensional representations of the input signal during normal volcanic activity by training a variational autoencoder (VAE) to compress, reconstruct and synthesize input signals. Thanks to the internal structure of the proposed VAE architecture, with 1-dimensional convolutional layers with residual blocks and attention mechanism, the representation learned by the model can be employed to detect deviations from normal volcanic activity. In our experiments, we test and evaluate two techniques for unrest detection: a generative approach, with a bank of synthetic signals used to assess the degree of correspondence between normal activity and an input signal; and a discriminative approach, employing unsupervised clustering in the VAE representation space to identify prototypes of normal activity for comparison with an input signal.

Global warming is making extreme wave events more intense and frequent. Hence, the importance of monitoring the sea state for marine risk assessment and mitigation is increasing day-by-day. In this work, we exploit the ubiquitous seismic noise generated by energy transfer from the ocean to the solid earth (called microseisms) to infer the sea wave height data provided by hindcast maps. To this aim, we use a combined approach based on statistical analysis and machine learning. In particular, a random forest model shows very promising results in the spatial and temporal reconstruction of sea wave height by microseisms. The observed dependence of input importance from the distance sea grid cell-seismic station suggests how the reliable monitoring of the sea state in a wide area by microseisms needs data recorded by dense networks, comprising stations evenly distributed along the coastlines.

Starting from 24 August 2016, a long seismic sequence, including nine M-w > 5.0 earthquakes, struck a wide area of the Central Italy. A large amount of geological, geodetic, and seismological data envisages a complex system of NNW-SSE trending, seismogenic normal faults. These active tectonic structures are well known at the surface and consistent with previous seismotectonic studies. In order to improve the comprehension of the seismotectonic framework of this seismic sequence, we provide a novel reconstruction of the subsurface geology of the area close to the Norcia M-w 6.5 mainshock (30 October 2016), based on previously unpublished seismic reflection profiles and available geological data. All the data have been synthesized along a 47 km long, WSW-ENE trending geological cross section, interpreted down to a depth of 12 km. Comparing the subsurface geological model with the available seismological data, we find that the majority of seismicity is confined within the sedimentary sequence and does not penetrate the underlying basement. The basement has been constrained at depths of 8 to 11 km and coincides with the cutoff of the seismicity. We have also traced the trajectories of the seismogenic normal faults activated during this seismic sequence, reconciling the high-angle (dip>65 degrees) normal faults exposed at the surface, with their angle (dip < 50 degrees) at hypocentral depths. The results of this study may be useful for better understanding the rheological properties of the seismogenic rock volume, as well as the coseismic deformations of the topographic surface observed by geodetic techniques and field mapping.

The evolution of volcanic activity observed at the New South East Crater (NSEC) and Voragine (VOR) between July 2019 and January 2020 has been deciphered by taking into account the changes of textures and chemical zoning of plagioclase and olivine crystals from the erupted lavas and tephra. The petrological observations have been integrated with analyses of the amplitude and source location of volcanic tremor and infrasound data. Characteristics of crystals erupted on July 2019 at the NSEC reflect protracted intrusions of magma into the mid-upper section of the plumbing system, approximately within 290–120 MPa, which acted as the main zone of magma accumulation and crystallization before the beginning of the eruptive activity. Textures and compositions of crystals erupted at VOR emphasize the beginning of volcanic activity driven by recharge/discharge phases that mostly affect the shallowest portion of the Mt. Etna plumbing system (

Volcanic processes occur in a wide range of temporal and spatial scales. However, a key step of magma ascent is recognizable in the dynamics of gas and magma in the shallow plumbing system, where volatiles play a fundamental role in controlling the eruptive style. With the aim of investigating shallow degassing processes, an experimental setup was deployed at Mt. Yasur, an active volcano located in Tanna Island (Vanuatu arc), from 9th to 12th July 2011. The setup comprised high-speed and thermal cameras, as well as a microphone, capable of recording both in the infrasonic and audible range. The analysis of acoustic signals, validated by observing images from the high-speed and thermal cameras, has enabled characterizing the explosive activity during the investigated period. Two types of explosions, distinct for spectral features and waveforms, were observed: (i) minor events, corresponding to small overpressurized bursts, occurring almost continuously; (ii) major events, characterizing the Strombolian activity at Mt. Yasur. By investigating variation in the occurrence rate of the minor events, we found that, on a short timescale, the dynamics responsible for the two types of explosions are decoupled. These results, together with previous literature data, bring additional evidence of the existence of distinct sources of degassing. Finally, major events can be distinguished as emergent events, i.e. long-lasting signals, corresponding to ash-rich explosions, and impulsive events, featuring shorter duration and larger amplitude.

The petrological part of this study was supported by the FIR 2014 research grant to Marco Viccaro from the University of Catania (Italy), grant number 2F119B, title of the project “Dynamics of evolution, ascent and emplacement of basic magmas: case-studies from eruptive manifestations of Eastern Sicily”.

From January 2011 to December 2015, Mt. Etna was mainly characterized by a cyclic eruptive behavior with more than 40 lava fountains from New South-East Crater. Using the RMS (Root Mean Square) of the seismic signal recorded by stations close to the summit area, an automatic recognition of the different states of volcanic activity (QUIET, PRE-FOUNTAIN, FOUNTAIN, POST-FOUNTAIN) has been applied for monitoring purposes. Since values of the RMS time series calculated on the seismic signal are generated from a stochastic process, we can try to model the system generating its sampled values, assumed to be a Markov process, using Hidden Markov Models (HMMs). HMMs analysis seeks to recover the sequence of hidden states from the observations. In our framework, observations are characters generated by the Symbolic Aggregate approXimation (SAX) technique, which maps RMS time series values with symbols of a pre-defined alphabet. The main advantages of the proposed framework, based on HMMs and SAX, with respect to other automatic systems applied on seismic signals at Mt. Etna, are the use of multiple stations and static thresholds to well characterize the volcano states. Its application on a wide seismic dataset of Etna volcano shows the possibility to guess the volcano states. The experimental results show that, in most of the cases, we detected lava fountains in advance.