Montalto, Placido

Volcanic tremor and long period (LP) events are typical seismic signals recorded on active volcanoes and are characterized by different durations, longer than minutes and a few seconds - tens of seconds for the former and latter, respectively. As they share the same frequency content, they are often grouped together in the literature and referred to by the unique name of LP seismicity. The common spectral features, together with observations in some volcanoes of individual LP events merging to form volcanic tremor, led to hypotheses that LP events and volcanic tremor share the same source mechanism. However, it is still open to debate whether volcanic tremor can be considered a simple coalescence of LP events or not. In this work, to help answer such a question, we analysed volcanic tremor and LP events recorded at Mt. Etna during the period February 2019–June 2020, characterized by minor eruptive activity, varying from weak ash emission to explosive and effusive eruptions at all the summit craters. Results from spectral, amplitude and location analyses, as well as the different scaling laws explaining the distributions of the duration/number of events versus size, led us to infer that LP events and volcanic tremor at Mt. Etna are not due to a common source mechanism.

The Istituto Nazionale di Geofisica e Vulcanologia (INGV) is an Italian research institution with focus on earth sciences. Moreover, the INGV is the operational center for seismic surveillance and earthquake monitoring in Italy and is a part of the civil protection system as a center of expertise on seismic, volcanic, and tsunami risks.INGV operates the Italian National Seismic Network and other networks at national scale and is a primary node of the European Integrated Data Archive for archiving and distributing strong‐motion and weak‐motion seismic recordings. In the control room in Rome, INGV staff performs seismic surveillance and tsunami warning services; in Catania and Naples, the control rooms are devoted to volcanic surveillance. Volcano monitoring includes locating earthquakes in the regions around the Sicilian (Etna, Eolian Islands, and Pantelleria) and the Campanian (Vesuvius, Campi Fregrei, and Ischia) active volcanoes. The tsunami warning is based on earthquake location and magnitude (M) evaluation for moderate to large events in the Mediterranean region and also around the world. The technologists of the institute tuned the data acquisition system to accomplish, in near real time, automatic earthquake detection, hypocenter and magnitude determination, and evaluation of several seismological products (e.g., moment tensors and ShakeMaps). Database archiving of all parametric results is closely linked to the existing procedures of the INGV seismic surveillance environment and surveillance procedures. Earthquake information is routinely revised by the analysts of the Italian seismic bulletin. INGV provides earthquake information to the Department of Civil Protection (Dipartimento di Protezione Civile) to the scientific community and to the public through the web and social media. We aim at illustrating different aspects of earthquake monitoring at INGV: (1) network operations; (2) organizational structure and the hardware and software used; and (3) communication, including recent developments and planned improvements.

Infrasonic signals investigation plays a fundamental role for both volcano monitoring purpose and the study of the explosion dynamics. Proper and reliable detection of weak signals is a critical issue in active volcano monitoring. In particular, in volcanic acoustics, it has direct consequences in pinpointing the real number of generated events (amplitude transients), especially when they exhibit low amplitude, are close in time to each other, and/or multiple sources exist. To accomplish this task, several algorithms have been proposed in literature; in particular, to overcome limitations of classical approaches such as short-time average/long-time average and cross-correlation detector, in this paper a subspace-based detection technique has been implemented. Results obtained by applying subspace detector on real infrasound data highlight that this method allows sensitive detection of lower energy events. This method is based on a projection of a sliding window of signal buffer onto a signal subspace that spans a collection of reference signals, representing similar waveforms from a particular infrasound source. A critical point is related to subspace design. Here, an empirical procedure has been applied to build the signal subspace from a set of reference waveforms (templates). In addition, in order to determine detectors parameters, such as subspace dimension and detection threshold, even in presence of overlapped noise such as infrasonic tremor, a statistical analysis of noise has been carried out. Finally, the subspace detector reliability and performance, have been assessed by performing a comparison among subspace approach, cross correlation detector and short-time average/long-time average detector. The obtained confusion matrix and extrapolated performance indices have demonstrated the potentiality, the advantages and drawbacks of the subspace method in tracking volcanic activity producing infrasound events. This method revealed to be a good compromise in detecting low-energy and very close in time events recorded during Strombolian activity

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 mobilephone- 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.

Oggetto del presente report tecnico è la descrizione del nuovo software di gestione del parco auto, il principale strumento impiegato presso la sede dell’INGV-OE per la gestione delle attività relativeall’utilizzo dei veicoli ubicati presso le sedi di Catania, Nicolosi e Lipari.

In this work, we analysed the seismic noise recorded at Mt. Etna by 18 stations during the interval 2007–2015 in the frequency band 0.1–0.3 Hz, chosen to avoid contamination from volcanic tremor. Variations in time of medium seismic velocity in the range − 0.8 to 0.8% were found, mostly affecting the stations located on the volcano summit and flanks. Based on the investigated frequency content, the Δv/v changes took place from the surface to a depth of ~ 4.5–6.5 km. To identify the source mechanism of the observed medium changes, the variations were quantitatively compared by wavelet transform coherence with volcano-tectonic and meteorological parameters. A significant relationship with meteorological parameters with seasonal periodicity (especially air temperature and snow loading) was found, probably caused by thermo-elastic strain and increasing-decreasing surface loading cycles. Moreover, a sharp medium velocity decrease, taking place in mid-December 2009 and clearly time-related to the largest volcano-tectonic strain release phenomenon of the investigated period, was also found. Such a velocity decrease was interpreted as resulting from ascent of fluids and gas exsolution taking place at the same time as the volcano-tectonic swarm.

Real-time assessment of the state of a volcano plays a key role for civil protection purposes. Unfortunately, because of the coupling of highly nonlinear and partially known complex volcanic processes, and the intrinsic uncertainties in measured parameters, the state of a volcano needs to be expressed in probabilistic terms, thus making any rapid assessment sometimes impractical. With the aim of aiding on-duty personnel in volcano-monitoring roles, we present an expert system approach to automatically estimate the ongoing state of a volcano from all available measurements. The system consists of a probabilistic model that encodes the conditional dependencies between measurements and volcanic states in a directed acyclic graph and renders an estimation of the probability distribution of the feasible volcanic states.We test the model with Mount Etna (Italy) as a case study by considering a long record of multivariate data. Results indicate that the proposed model is effective for early warning and has considerable potential for decision-making purposes.

L’Etna è uno stratovulcano composito situato lungo la costa ionica della Sicilia. Le frequenti eruzioni laterali (soprattutto lungo i Rift NE, S e O) fanno sì che ad esso sia associata una elevata pericolosità vulcanica. Se valutiamo anche l’elevato tasso di urbanizzazione dei suoi fianchi risulta evidente il notevole valore esposto al pericolo. Nel quadro del progetto PON SIGMA (Integrated Cloud-Sensor System for Advanced Multirisk Management), abbiamo sviluppato un’analisi in tempo quasi reale e completamente automatizzata volta alla valutazione delle aree soggette alla più alta probabilità di apertura di bocche effusive (vent) e il corrispondente hazard relativo all’accadimento di eruzioni effusive. L’algoritmo bayesiano BET_EF (Bayesian Event Tree_Eruption Forecasting), basato sull’albero degli eventi, è, nel nostro approccio, utilizzato inizialmente per la valutazione di una mappa di pericolosità a lungo termine sulla base dell’attività effusiva degli ultimi 4000 anni. L’analisi e l’inversione dei parametri monitorati in tempo reale, quali, ad esempio, dati sismici e sorgenti di tremore vulcanico, permette di valutare la funzione di densità di probabilità (PDF) a breve termine. Un’ulteriore applicazione dell’algoritmo BET_EF fornisce uno scenario, in termini di mappa di pericolosità, a breve termine per le simulazioni delle colate laviche. L’output della seconda applicazione del BET_EF costituisce l’input per simulare una serie di colate laviche e valutare il relativo hazard, definito in termini di impatto sul territorio. Allo scopo di testare limiti e utilità del nostro approccio integrato, abbiamo utilizzato, come test case, la fase intrusiva iniziale dell’eruzione laterale accaduta all’Etna nel maggio 2008. La previsione di apertura di vent evidenzia la zona con maggiore probabilità e, dall’analisi dei risultati, si nota un buon accordo tra l’area a probabilità più alta e la posizione effettiva del vent. È stata eseguita una serie di 200 simulazioni di colate per valutare le aree soggette a più alta probabilità di invasione lavica. Infine, è stata valutata la densità dei flussi simulati e i valori più alti sono risultati in accordo con l’area effettivamente coperta dal campo lavico dell’eruzione considerata.

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, POSTFOUNTAIN) 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.