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Messina, Alfio Alex
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Messina, Alfio Alex
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Messina, Alfio
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Messina, A.
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alfio.messina@ingv.it
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- ProductOpen AccessA dataset from a multi-station analysis of volcanic tremor at Mt. Etna, Italy, in 2021(2022-09-02)
; ; ; ; ; ; ; 66 13 - PublicationRestrictedMarCONI - One-Hand Controller for Unmanned Aerial VehiclesHere authors present MarCONI (Multi Channel One haNd Interface), a system born to control remotely piloted aircrafts (RPAs), in particular multi-rotors, by means of new generation peripherals. Among those, used in personal computing environment, a generation of 6 degree-of-freedom (DOF) advanced controllers is the SpaceMouse family by 3Dconnexion. MarCONI is a hardware-software system, acting as a bridge between the USB peripheral and the UAV's radio-controller. A shaping block has been added to the system in order to process raw data flow generated by the SpaceMouse. This step allows the user to adapt the controller feedback to the specific vehicle features and response. Shaping parameters are fully customizable by a specific Web GUI, accessible through a Wi-Fi connection, making possible the setup tuning by means of mobile devices, such as smartphones or laptops. A side benefit of this system is the possibility to pilot UAVs using one hand only, with no restriction.
428 6 - PublicationOpen AccessVariations in eruptive activity at Mount Etna in 2007-2009: pattern classification of volcanic tremor data reveals state transitions(2009-06-11)
; ; ; ; ; ;Behncke, B.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Falsaperla, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Langer, H.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Messina, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Spampinato, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; ; ; ; Eruptive activity at Mount Etna in 2007-2009 consisted of 7 episodes of lava fountaining and periodic Strombolian activity at the summit, followed by an eruption on the upper east flank that started on 13 May 2008 and is continuing as of May 2009, making this the longest-lasting flank eruption of the volcano since 1993. The lava fountains originated from the Southeast Crater, the youngest of Etna’s four summit craters; four occurred from the summit vent of the crater between late-March and early-May 2007, whereas the remaining three, on 4-5 September, 23-24 November 2007 and 10 May 2008, occurred from a new vent on its lower eastern flank of the Southeast Crater cone. The latter three episodes lasted 10, 6 and 4 hours, respectively, and thus were much longer than most other paroxysms at Etna in the past few decades. The 10 May 2008 episode produced some of the longest lava flows (6.2 km) ever erupted from an Etnean summit vent, and had this episode lasted much longer, the lava might have approached close to populated areas (Milo, Zafferana Etnea). Volcanic tremor data recorded during the same period by the seismic network of the Istituto Nazionale di Geofisica e Vulcanologia (Sezione di Catania) show significant variations related to the changes in the eruptive activity. Application of a new, autonomously working software, which combines various methods of pattern classification based on unsupervised learning, is used to detect state transitions in the volcanic tremor data. We investigate changes in seismic radiation and focus on transitions from pre-eruptive to eruptive activity during summit and flank eruptive episodes, taking into account field and other (geological and petrological) observations. The conspicuous eruptive events of 2007-2008 are compared to more recent time windows, falling into the period of continued low-level flank lava effusion accompanied by occasional mild Strombolian activity.239 104 - PublicationOpen AccessPerfomance of a new multistation alarm system for volcanic activity based on neural network techniques(2014-08-25)
; ; ; ; ;Langer, H.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Falsaperla, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Messina, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Spampinato, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; ; ; Numerous eruptive episodes with Strombolian activity, lava fountains, and lava flows occurred at Mt. Etna volcano between 2006 and 2013. In particular, 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, while months-long lava emissions affected the upper eastern flank of the volcano in 2006 and 2008-2009. The monitoring of such volcanic phenomena is particularly relevant for their potential socio-economic impact in this densely populated volcanic region. 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. Early information about changes in the state of the volcano and/or at the onset of potentially dangerous eruptive phenomena requires efficacious surveillance methods. Several studies on seismic data recorded at Mt. Etna highlight that the analysis of the continuous background seismic signal, the so-called volcanic tremor, is of paramount importance to follow the evolution of volcanic activity (e.g., Alparone et al., 2003; Falsaperla et al., 2005; Langer et al., 2009). Indeed, 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. This signal is recorded at Etna by means of the INGV seismic network equipped with broadband sensors. The huge amount of digital data continuously acquired by INGV’s stations every day makes a manual analysis difficult. To overcome this problem, techniques of automatic classification of the tremor signal were applied to explore the robustness of different methods for the identification of regimes in volcanic activity (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. In the wake of this evidence, Messina and Langer (2011) developed KKAnalysis, a software that combines an unsupervised classification method (Kohonen Maps) with fuzzy cluster analysis. This tool was set up at the operative centre of the INGV-Osservatorio Etneo in 2010, and it 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. Here we apply KKAnalysis using 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 with KKAnalysis 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.286 52 - PublicationRestrictedDetecting imminent eruptive activity at Mt Etna, Italy, in 2007–2008 through pattern(2011-02)
; ; ; ; ; ;Langer, H.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Falsaperla, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Messina, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Spampinato, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Behncke, B.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; ; ; ; Volcano monitoring aims at the recognition of changes in instrumentally observable parameters before hazardous activity in order to alert governmental authorities. Among these parameters seismic data in general and volcanic tremor in particular play a key role. Recent major explosive eruptions such as Okmok (Aleutians) and Chaitén (Chile) in 2008 and numerous smaller events at Mt Etna (Italy), have shown that the period of premonitory seismic activity can be short (only a few hours), which entails the necessity of effective automatic data processing near on-line. Here we present a synoptic pattern classification analysis based on Self Organizing Maps and Fuzzy Cluster Analysis which is applied to volcanic tremor data recorded during a series of paroxysmal eruptive episodes and a flank eruption at Etna in 2007–2008. In total, eight episodes were analyzed; in six of these significant changes in the dynamic regime of the volcano were detected up to 9 h prior to the onset of eruptive activity, and long before changes in volcanic tremor amplitude and spectral content became evident in classical analysis. In two cases, the state transition was b1 h before the onset of eruptive activity, which we interpret as evidence for very rapid magma ascent through an open conduit. We further detected twenty failed paroxysms, that is episodes of volcanic unrest that did not culminate in eruptive activity, between March and April 2007. As the application of the software for this synoptic pattern classification is straightforward and requires only moderate computational resources, it was possible to exploit it in an on-line application, which was tested and now is in use at the Istituto Nazionale di Geofisica e Vulcanologia in Catania for the monitoring of Etna. We believe that the pattern classification presented here may become a powerful addition to the repertoire of volcano monitoring tools and early warning techniques worldwide.1787 39 - PublicationRestrictedCoherent control of stimulated emission inside one dimensional photonic crystals: strong coupling regime(2009-06-16)
; ; ; ; ; ; ;Settimi, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Severini, S.; Centro Interforze Studi Applicazioni Militari (CISAM), Via della Bigattiera 10, 56122 San Piero a Grado, Pisa, Italy ;Sibilia, C.; Dipartimento di Energetica, Universit`a “La Sapienza” di Roma, via Scarpa 16, 00161 Roma, Italy ;Bertolotti, M.; Dipartimento di Energetica, Universit`a “La Sapienza” di Roma, via Scarpa 16, 00161 Roma, Italy ;Napoli, A.; Dipartimento di Scienze Fisiche ed Astronomiche, Universit`a di Palermo, via Archirafi 36, 90123 Palermo, Italy ;Messina, A.; Dipartimento di Scienze Fisiche ed Astronomiche, Universit`a di Palermo, via Archirafi 36, 90123 Palermo, Italy; ; ; ; ; The present paper discusses the stimulated emission, in strong coupling regime, of an atom embedded inside a one dimensional (1D) Photonic Band Gap (PBG) cavity which is pumped by two counter-propagating laser beams. Quantum electrodynamics is applied to model the atom-field interaction, by considering the atom as a two level system, the e.m. field as a superposition of normal modes, the coupling in dipole approximation, and the equations of motion in Wigner-Weisskopf and rotating wave approximations. In addition, the Quasi Normal Mode (QNM) approach for an open cavity is adopted, interpreting the local density of states (LDOS) as the local density of probability to excite one QNM of the cavity; and therefore rendering this LDOS dependent on the phase difference of the two laser beams. In this paper we demonstrate that the strong coupling regime occurs at high values of the LDOS. In accordance with the results of the literature, the emission probability of the atom decays with an oscillatory behaviour, so that the atomic emission spectrum exhibits two peaks (Rabi splitting). The novelty of this work is that the phase difference of the two laser beams can produce a coherent control of both the oscillations for the atomic emission probability and, as a consequence, of the Rabi splitting in the emission spectrum. Possible criteria to design active delay lines are finally discussed.350 25 - PublicationOpen Access
58 56 - PublicationOpen AccessVariations in eruptive activity at Mount Etna in 2007-2008: state transitions revealed by pattern classification of volcanic tremor data(2009-04-19)
; ; ; ; ; ;Behncke, B.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Falsaperla, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Langer, H.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Messina, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Spampinato, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; ; ; ; Eruptive activity at Mount Etna in 2007-2008 consisted of 7 episodes of lava fountaining and periodic Strombolian activity at the summit, followed by an eruption on the upper east flank that started on 13 May 2008 and is continuing as of January 2009. The lava fountains originated from the Southeast Crater, the youngest of Etna’s four summit craters, and showed a shift in the main locus of activity from the summit of the Southeast Crater cone to a new vent on its lower eastern flank. The three lava fountaining episodes from the new vent in September and November 2007 and May 2008 were unusually long-lived (up to 10 hours, compared to <1 h during most of the previous episodes from the Southeast Crater), and produced some of the longest lava flows (6.2 km) ever erupted from an Etnean summit vent. Volcanic tremor data recorded during the same period by the seismic network of the Istituto Nazionale di Geofisica e Vulcanologia (Sezione di Catania) showed significant variations related to the changes in the eruptive activity. We explore the application of a new software, which combines various methods of pattern classification based on unsupervised learning, and which is used to detect state transitions in volcanic tremor data collected throughout the aforementioned eruptive episodes. Particular attention is devoted to transitions from pre-eruptive to eruptive activity, such as the onset of Strombolian activity, often heralding episodes of lava fountaining. We investigate possible differences in the regimes of seismic radiation prior to summit (Strombolian or lava fountaining) and flank activity (opening of fissures, short-lived lava fountaining, lava flow emission), and compare these to changes in the patterns of eruptive activity based on field and other visual observations.192 77 - PublicationOpen AccessA Multi-Station Warning System for Short-Term Detection of Volcanic Unrest at Etna Volcano (Italy)(2019-12-10)
; ; ; ; ; ; ; 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.100 14 - PublicationOpen AccessShort-term impending eruptive activity at Mt Etna revealed from a multistation system based on volcanic tremor analysis(INGV, 2014-10-29)
; ; ; ; ;Langer, H.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Falsaperla, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Messina, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Spampinato, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; ; ; ; ; ; ;Cocina, O.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Nicotra, E.; Università di Catania; 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.248 2334