Spampinato, Salvatore

With over fifty eruptive episodes (Strombolian activity, lava fountains, and lava flows) between 2006 and 2013, Mt Etna, Italy, underscored its role as the most active volcano in Europe. Seven paroxysmal lava fountains at the South East Crater occurred in 2007-2008 and 46 at the New South East Crater between 2011 and 2013. Month-lasting lava emissions affected the upper eastern flank of the volcano in 2006 and 2008-2009. On this background, effective monitoring and forecast of volcanic phenomena are a first order issue for their potential socio-economic impact in a densely populated region like the town of Catania and its surroundings. For example, explosive activity has often formed thick ash clouds with widespread tephra fall able to disrupt the air traffic, as well as to cause severe problems at infrastructures, such as highways and roads. For timely information on changes in the state of the volcano and possible onset of dangerous eruptive phenomena, the analysis of the continuous background seismic signal, the so-called volcanic tremor, turned out of paramount importance. Changes in the state of the volcano as well as in its eruptive style are usually concurrent with variations of the spectral characteristics (amplitude and frequency content) of tremor. The huge amount of digital data continuously acquired by INGV’s broadband seismic stations every day makes a manual analysis difficult, and techniques of automatic classification of the tremor signal are therefore applied. The application of unsupervised classification techniques to the tremor data revealed significant changes well before the onset of the eruptive episodes. This evidence led to the development of specific software packages related to real-time processing of the tremor data. The operational characteristics of these tools – fail-safe, robustness with respect to noise and data outages, as well as computational efficiency – allowed the identification of criteria for automatic alarm flagging. The system is hitherto one of the main automatic alerting tools to identify impending eruptive events at Etna. The currently operating software named KKAnalysis is applied to the data stream continuously recorded at two seismic stations. The data are merged with reference datasets of past eruptive episodes. In doing so, the results of pattern classification can be immediately compared to previous eruptive scenarios. Given the rich material collected in recent years, here we propose the application of the alert system to a wider range (up to a total of eleven) stations at different elevations (1200-3050 m) and distances (1-8 km) from the summit craters. Critical alert parameters were empirically defined to obtain an optimal tuning of the alert system for each station. To verify the robustness of this new, multistation alert system, a dataset encompassing about eight years of continuous seismic records (since 2006) was processed automatically using KKAnalysis and collateral software offline. Then, we analyzed the performance of the classifier in terms of timing and spatial distribution of the stations.

Eighteen paroxysmal episodes occurred on Mt Etna in 2011, and provided rich material for testing automatic procedures of data processing and alert systems in the context of volcano monitoring. The 2011 episodes represent a typical picture of activity of Mt Etna: in 2000 and 2001, before the 2001 flank eruption, more than one hundred lava fountains were encountered. Other major lava fountains occurred before the flank eruptions of 2002/03 and 2008. All these fountains, which are powerful but usually short lived phenomena, originated from the South-East Crater area and caused the formation of thick ash clouds, followed by the fallout of material with severe problems for the infrastructure of the metropolitan area of Catania. We focus on the seismic background radiation – volcanic tremor – which plays a key role in the surveillance of Mt Etna. Since 2006 a multi-station alert system has been established in the INGV operative centre of Catania exploiting STA/LTA ratios. Besides, it has been demonstrated that also the spectral characteristics of the signal changes correspondingly to the type of volcanic activity. The simultaneous application of Self Organizing Maps and Fuzzy Clustering offers an efficient way to visualize signal characteristics and its development with time, allowing to identify early stages of eruptive events and automatically flag a critical status before this becomes evident in conventional monitoring techniques. Changes of tremor characteristics are related to the position of the source of the signal. The location of the sources exploits the distribution of the amplitudes across the seismic network. The locations were extremely useful for warning throughout both a flank eruption in 2008 as well as the 2011 lava fountains, during which a clear migration of tremor sources towards the eruptive centres could be noticed in advance. The location of the sources completes the picture of an imminent volcanic unrest and corroborates early warnings flagged by the changes of signal characteristics. On-line data processing requires computational efficiency, robustness of the methods and reliability of data acquisition. The amplitude based multi-station approach offers a reasonable stability as it is not sensitive to the failure of single stations. The single station approach, based on our unsupervised classification techniques, is cost-effective with respect to logistic efforts, as only one or few key stations are necessary. Both systems have proven to be robust with respect to disturbances (undesired transients like earthquakes, noise, short gaps in the continuous data flow), and false alarms were not encountered so far. Another critical aspect is the reliability of data storage and access. A hardware cluster architecture has been proposed for failover protection, including a Storage Area Network system. We outline concepts of the software architectures which allow easy data access following predefined user policies. We envisage the integration of seismic data and those originating from other scientific fields (such as volcano imagery, geochemistry, deformation, gravity, magneto-telluric), in order to facilitate cross-checking of the findings encountered from the single data streams, in particular allowing their immediate verification with respect to ground truth.

The early-warning of a volcanic unrest requires continuous, reliable information from monitoring before volcanic activity starts. An optimal source of such information are seismic data, which overcome problems due to prohibitive conditions for field surveys or cloud cover that may hinder visibility. Given the large amount of digital data accumulating in short times, techniques of automatic pattern recognition are necessary in the context of effective extraction of information and data reduction. We designed a multi-station warning system based on pattern recognition techniques. In particular, a classification of patterns of volcanic tremor, the background seismic radiation, has been performed. Two unsupervised classifiers, Self-Organizing Maps (SOM) and fuzzy clustering were applied to automatically detect patterns which are typical footprints of an impending volcanic unrest. Plotting the SOM colors on DEM allows us their geographical visualization according to the stations of detection; this spatial location may give hints on areas potentially impacted by eruptive phenomena. The method implies continuous processing of recorded data streams; it was tested and tuned over year-long data streams on the base of eruptive phenomena occurred at Etna, Italy, in recent years. Here we present results of the application of the classifier, which forecasted in hindsight patterns associated with fast-rising magma (typical of lava fountains) as well as a relatively long lead time of the outburst (lava flows from eruptive fractures). The performance of the multi-station system was evaluated by using Receiver Operating Characteristics (ROC) curves; the result is indicative of a good detection accuracy that cannot be achieved from a mere random choice.

Over fifty eruptive episodes with Strombolian activity, lava fountains, and lava flows occurred at Mt Etna volcano between 2006 and 2013. Namely, there were seven paroxysmal lava fountains at the South-East Crater in 2007-2008 and 46 at the New South-East Crater between 2011 and 2013. Lava emissions lasting months affected the upper eastern flank of the volcano in 2006 and 2008-2009. Effective monitoring and forecast of such volcanic phenomena are particularly relevant for their potential socio-economic impact in densely populated regions like Catania and its surroundings. For example, explosive activity has often formed thick ash clouds with widespread tephra fall able to disrupt the air traffic, as well as to cause severe problems at infrastructures, such as highways and roads. Timely information about changes in the state of the volcano and possible onset of dangerous eruptive phenomena requires efficacious surveillance methods. The analysis of the continuous background seismic signal, the so-called volcanic tremor, turned out of paramount importance to follow the evolution of volcanic activity [e.g., Alparone et al., 2003; Falsaperla et al., 2005]. Changes in the state of the volcano as well as in its eruptive style are usually concurrent with variations of the spectral characteristics (amplitude and frequency) of tremor. The huge amount of digital data continuously acquired by INGV’s broadband seismic stations every day makes a manual analysis difficult. In order to tackle this problem, techniques of automatic classification of the tremor signal are applied. In a comparative study, the robustness of different methods for the identification of regimes in volcanic activity were examined [Langer et al., 2009]. In particular, Langer et al. [2011] applied unsupervised classification techniques to the tremor data recorded at one station during seven paroxysmal episodes in 2007-2008. Their results revealed significant changes in the pattern classification well before the onset of the eruptive episodes. This evidence led to the development of specific software packages, such as the program KKAnalysis [Messina and Langer, 2011], a software that combines an unsupervised classification method (Kohonen Maps) with fuzzy cluster analysis. The operational characteristics of these tools - fail-safe, robustness with respect to noise and data outages, as well as computational efficiency - allowed on-line processing at the operative centre of the INGV-Osservatorio Etneo in 2010 and the identification of criteria for automatic alarm flagging. The system is hitherto one of the main automatic alerting tools to identify impending eruptive events at Etna. The software carries out the on-line processing of the new data stream coming from two seismic stations, merged with reference datasets of past eruptive episodes. In doing so, results obtained for new data are immediately compared to previous eruptive scenarios. Given the rich material collected in recent years, we are able to apply the alert system to eleven stations at different elevations (1200-3050 m) and distances (1-8 km) from the summit craters. Critical alert parameters were empirically defined to obtain an optimal tuning of the alert system for each station. To verify the robustness of this new, multistation alert system, a dataset encompassing about eight years of continuous seismic records (since 2006) was processed automatically using KKAnalysis and collateral software off-line. Then, we analyzed the performance of the classifier in terms of timing and spatial distribution of the stations. We also investigated the performance of the new alert system based on KKAnalysis in case of activation of whatever eruptive centre. Intriguing results were obtained in 2010 throughout periods characterized by the renewal of volcanic activity at Bocca Nuova-Voragine and North-East Crater, and in the absence of paroxysmal phenomena at South-East Crater and New South-East Crater. Despite the low-energy phenomena reported by volcanologists (i.e., degassing, low-to moderate explosions), the triggered alarms demonstrate the robustness of the classifier and its potential: i) to identify even subtle changes within the volcanic system using tremor, and ii) to highlight the activation of a single eruptive centre, even though different from the one for which the classifier was initially tested. It is worth noting that in case of activation of weak sources, the successful performance of the classifier depends upon the general level of signals originating from other sources in that specific time span.

Mt Etna is a well monitored basaltic volcano for which high-quality, multidisciplinary data set are continuously available for around-the-clock surveillance. Particularly, volcano-seismic data sets cover decades long local recordings, temporally encompassing different styles of eruptive activity, from Strombolian eruptions to lava fountains and lava flows. Intense earthquakes swarms have often heralded effusive activity. However, from the seismic point of view, volcanic tremor has proved to be one of the most reliable indicators of impending eruptive activity. Indeed, changes in the volcano feeder show up in the signature of tremor, its spectral characteristics and source location. Some of us (Langer and Messina) have recently developed a new software for the classification of volcanic tremor data, combining Self Organizing Maps (also known as Kohonen Maps) along with Cluster and Fuzzy Analysis. This software allows us to analyse the background seismic radiation at permanent broadband stations located at various distance from the summit craters to identify transitions from pre-eruptive to eruptive activity. Throughout the analysis of the data flow, the software provides an unsupervised classification of the spectral characteristics (i.e., amplitude and frequency content) of the signal. The information embedded in the spectrum is interpreted to assign a specific state of the volcano. An application of this new software is proposed here on the eruptive events at Etna of 2007-2009, which consisted of 7 episodes of lava fountaining, periodic Strombolian activity at the summit craters, followed by lava emissions on the upper east flank of the volcano, with start on 13 May 2008 and end on 6 July 2009. In the study period the source of volcanic tremor was always shallow (less than 3 km) and within the volcano edifice. The upraise of magma to the surface was fast and associated with changes of volcanic tremor features, which covered time windows of variable duration from several hours to a few minutes. We discuss the possible reasons of such variability in the light of the characteristics of the overall seismicity preceding the eruptions in the study period, taking into account field observations and rheology of the ascending magma as well.

We analyze the seismic signals recorded on the island of Vulcano (Italy) during a volcano unrest that started in 2021. From mid-September 2021 onward, a high number of very long-period and long-period events occurred, accompanied by large emissions of CO2 and the increased temperature of fumaroles at various sites of the island. The complexity of the seismic signals recorded during the unrest made standard amplitude-based monitoring techniques, such as RSAM, questionable, as part of the signals are not volcanogenic, such as the frequent close-by passage of ships. We therefore study the inventory of the recorded signals by exploiting machine learning procedures, in particular unsupervised classification techniques. Our studies aim at identifying varying classes of seismic events possibly related to volcanic dynamic as well as irrelevant signals, such as man-made noise. Self-Organizing Maps and Cluster Analysis were applied. As a result, we are able to visualize the development of signal characteristics efficiently. This can provide a useful contribution to volcanic surveillance purposes, which aim to identify changes heralding a “Vulcanian” eruption, an eruptive style with strong explosive characteristics.

From 11 January to 15 November 2011, 18 paroxysmal eruptions occurred at Etna, Italy. These events belong to a long sequence of eruptive episodes, which marked the prevalent explosive style of the volcano since the early 2000s. Applying “KKAnalysis”, a software for pattern classification that combines Self­Organizing Maps and fuzzy clustering, to the background seismic radiation (so-called volcanic tremor), we were able to detect critical changes in the spectral characteristics (amplitude and frequency content) at a very early stage of the volcano unrest. The online implementation for surveillance purposes of KKAnalysis provided automatic alert of the impending eruptive events from hours to a few days in advance. In its original version, the classifier analyzed the data stream continuously recorded at a single seismic station. By using offline a modified version of KKAnalysis, here we apply the software to the seismic signal recorded at 11 broadband stations in 2011. The seismic sensors were located at various distances (from 1 to 8 km) from the active craters. The continuous records and the optimal geometry of the seismic network offer us the possibility to track the spectral variations in time and space. We show the new results of pattern classification and propose a revised, more powerful multi­station alert method that now provides short­ term forecasting also in the form of animated maps that flag the detection of changes at each station. This allows us to observe how the unrest develops in various sectors of the volcano. We discuss the performance of the method and the robustness of the eruption forecasts in the context of the complex dynamics of a volcanic system such as Etna.

Data of local seismicity recorded in the Etna area during the time span 2005-2011 have been selected for sharing. Basically they are of three types. First, raw continuous signals from permanent digital stations, equipped with three-component broad band sensors 40s period, for the most part. The sample rate of the signals is 100 Hz. Taking into account criteria such as: signal quality, availability of at least 3 year of data for each station, and sufficient azimuthal coverage of the Etnean volcanic area, we obtained a network of about twenty stations. We also provide an earthquake catalogue, obtained from off-line analysis of the digital seismograms daily performed by expert personnel at Osservatorio Etneo (INGV). The data are in ASCII format, and concern parametric information (latitude, longitude, depth, magnitude, etc.) about the hypocenter of ca 800 earthquakes, which occurred in the area of Mount Etna between 2005 and 2011. This catalogue reports shocks with magnitude greater than or equal to 2.0 and error threshold not greater than fixed values (e.g., horizontal and vertical hypocentral errors less than or equal to 2.0 km, RMS travel-time residual less than or equal to 0.35s, etc.). The third type of data is the RMS amplitude value of the continuous background seismic signal. These values are calculated by an automatic tool which processes the on-line signal from remote seismic stations. The amplitude data are calculated both in the whole unfiltered continuous signal, and in frequency bands 1 Hz wide, between 0.5 and 15 Hz. The format of data is ASCII. For treatment and characterization of each type of data, appropriate metadata, concerning station position, instrumental and processing specifications and any other useful information, have been considered.

At Stromboli volcano, Italy, continuous seismic monitoring and periodic, visual observations of volcanic activity for surveillance purposes began in the mid-1980s. Since 1985, two eruptions have occurred, one lasting from December, 1985 until April, 1986, and one in May, 1993. There have also been two small overflows, in 1990 and 1994. Since these episodes of lava effusion, the persistent Strombolian activity of the volcano has had several fluctuations during the past 15 years. Some episodes climaxed in powerful explosions. According to seismic records, these paroxysms consisted of a variable number of explosion quakes in rapid succession (i.e. from tens of seconds to a few minutes), associated with a notable increment in the amplitude of volcanic tremor. Throughout these episodes - which are called explosive sequences - lapilli, fragments of old rock, and bombs of varying dimensions were ejected, affecting an area greater than the crater terrace where the active craters are located. In this article, we describe the explosive sequences recorded at Stromboli between 1985 and 1999. We provide a characterization in terms of reduced displacement and duration for nine episodes occurring in 1998 and 1999. Their reduced displacements range from 15 to 124 cm2; their durations are between 6 and 18 min. We find no change in the frequency content of the seismic signal several minutes before and during the sequences. Considering medium- to long-term behavior, the spectral amplitude of the seismic signal decreases or has low values over several months preceding the occurrence of the paroxysms. This feature is common to 20 of the 22 explosive sequences, and is indicative of internal conditions that periodically characterize the feeder. We surmise that the paroxysms are the result of the partial obstruction of the volcanic conduit when the magma column is low or dropping. The onset of the explosive sequence, causing the sudden removal of the material which forms the obstruction, would trigger a sudden depressurization of the conduit and the rapid rise of magma from depth.

Il fianco nord-orientale dell’Etna è interessato da un noto sistema strutturale denominato Pernicana-Provenzana, che ha un andamento WNW–ESE. Esso è collegato ad ovest ad un altro importante elemento strutturale, il Rift di Nord-Est, che mostra avere un ruolo importante nel controllo dei fenomeni di instabilità del fianco orientale del vulcano. La sismicità associata a questo sistema strutturale è di tipo superficiale (max 2-3 km b.s.l.) e rilevanti fenomeni di creeping sono rilevabili sul suo segmento orientale. I terremoti associati a questo sistema di faglie, che possono raggiungere magnitudo sino a 4.3, qualche volta con fenomeni di fagliazione superficiale, hanno provocato danni importanti alle principali strutture alberghiere ed ai paesi ubicati in prossimità della struttura. Nel presente lavoro, sono riportati i risultati di uno studio di dettaglio della sismicità localizzata lungo tale sistema strutturale, nel periodo 1999-2009. I terremoti registrati dalla rete sismica permanente dell’Istituto Nazionale di Geofisica e Vulcanologia – Sezione di Catania, localizzati con un modello 1D utilizzando l’algoritmo Hypoellipse (Gruppo Analisi Dati Sismici, 2010), sono stati rilocalizzati applicando due differenti tecniche di localizzazione: NonLinLoc sviluppato da Lomax et al. (2000) e HypoDD proposto da Waldhauser & Ellsworth (2000). La prima metodologia è basata su un processo di ricerca globale, nello spazio 3D, dei parametri di localizzazione che possono essere ottenuti utilizzando diversi algoritmi. Il metodo HypoDD, che non prevede l’utilizzo di un modello 3D, è invece basato sull’algoritmo della doppia differenza che minimizza i residui tra le differenze dei traveltime osservati e calcolati per coppie di terremoti a stazioni comuni. L’applicazione di tali tecniche ha permesso di ottenere localizzazioni ipocentrali di migliore qualità, fondamentali per la caratterizzazione sismica della struttura. L’applicazione di queste differenti metodologie ha permesso di evidenziare che il sistema strutturale Pernicana- Provenzana risulta composto da segmenti caratterizzati da differenti rilasci di energia sismica. Sono stati individuati due cluster principali di terremoti, la cui distribuzione spaziale ha evidenziato un differente verso nell’immersione dei piani di faglia collegabili a questa sismicità. Infine, l’applicazione di tecniche di cross-correlazione delle forme d’onda registrate nel periodo indagato ha consentito di individuare “famiglie” di terremoti. L’analisi spazio – temporale delle famiglie individuate ha evidenziato per alcune di esse, una ricorrenza temporale ed ha permesso di ipotizzare che l’applicazione di un campo di stress sul sistema Pernicana-Provenzana potrebbe essere capace di attivare le stesse sorgenti sismiche in differenti periodi.