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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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staff
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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 - PublicationRestrictedSynopsis of supervised and unsupervised pattern classification techniques applied to volcanic tremor data atMt Etna, Italy(2009-03-10)
; ; ; ; ; ; ;Langer, H.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Falsaperla, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Masotti, M.; Medical Imaging Group, Department of Physics, University of Bologna ;Campanini, R.; Medical Imaging Group, Department of Physics, University of Bologna ;Spampinato, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Messina, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia; ; ; ; ; States of volcanic activity at Mt Etna develop in well-defined regimes with variable duration from a few hours to several months. Changes in the regimes are usually concurrent with variations of the characteristics of volcanic tremor, which is continuously recorded as background seismic radiation. This strict relationship is useful for monitoring volcanic activity in any moment and in whatever condition.We investigated the development of tremor features and its relation to regimes of volcanic activity applying pattern classification techniques. We present results from supervised and unsupervised classification methods applied to 425 patterns of volcanic tremor recorded between 2001 July and August, when a volcano unrest occurred. Support Vector Machine (SVM) and multilayer perceptron (MLP) were used as pattern classifiers with supervised learning. For the SVM and MLP training, we considered four target classes, that is, pre-eruptive, lava fountains, eruptive and post-eruptive. Using a leave one out testing scheme, SVM reached a score of 94.8 per cent of patterns matching the actual class membership, whereas MLP achieved 81.9 per cent of matching patterns. The excellent results, in particular those obtained with SVM, confirmed the reproducibility of the a priori classification. Unsupervised classification was carried out using cluster analysis (CA) and self-organizing maps (SOM). The clusters identified in unsupervised classification formed well-defined regimes, which can be easily related to the four a priori classes aforementioned. Besides, CA found a further cluster concurrent with the climax of eruptive activity. Applying a proper colour-coding to the microclusters (the so-called best matching units) identified by SOM, it was visually possible to follow the development of the characteristics of the tremor data with time, highlighting transitional stages from a regime of volcanic activity to another one. We conclude that supervised and unsupervised classification methods can be conveniently implemented as complementary tools for an in-depth understanding of the relationships between tremor data and volcanic phenomena.619 34 - 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 AccessShallow velocity model in the area of Pozzo Pitarrone, Mt. Etna, from single station, array methods and borehole data.(2016)
; ; ; ; ; ; ; ; ; ;Zuccarello, L.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Paratore, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;La Rocca, M. ;Ferrari, F.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Messina, A.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Branca, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Contrafatto, D.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia ;Galluzzo, D.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione OV, Napoli, Italia ;Rapisarda, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Catania, Catania, Italia; ; ;; ; ; ; ; Seismic noise recorded by a temporary array installed around Pozzo Pitarrone, NE flank of Mt. Etna, have been analysed with several techniques. Single station HVSR method and SPAC array method have been applied to stationary seismic noise to investigate the local shallow structure. The inversion of dispersion curves produced a shear wave velocity model of the area reliable down to depth of about 130 m. A comparison of such model with the stratigraphic information available for the investigated area shows a good qualitative agreement. Taking advantage of a borehole station installed at 130 m depth, we could estimate also the Pwave velocity by comparing the borehole recordings of local earthquakes with the same event recorded at surface. Further insight on the P-wave velocity in the upper 130 m layer comes from the surface reflected wave observable in some cases at the borehole station. From this analysis we obtained an average P-wave velocity of about 1.2 km/s, compatible with the shear wave velocity found from the analysis of seismic noise.1220 265 - PublicationOpen AccessFixing Criteria for Volcanic Unrest Warning(MISCELLANEA INGV, 2022-09-29)
; ; ; ; ; ; ; In volcanic observatories worldwide, geophysical and geochemical data are usually collected remotely, providing continuous information about the state of volcanoes even in unfavorable conditions with respect to visibility and access to the area of eruptive centers. Early stages of unrest can be detected with high reliability; nonetheless, style and, in particular, intensity of eruptions are diffcult to predict. Consequently, it turns out important to identify critical moments after which the development of a paroxysmal activity becomes highly probable. In this perspective, we exploit a machine learning (ML) method for the analysis of seismic data continuously acquired by the permanent seismic network at Etna, Italy. Threshold criteria, which are based on parameters derived from the ML system and the number of stations where changes are detected, have been established with the scope of automatic alert flagging. As mild unrests may continue for weeks and even months, there is the need to adjust the trigger criteria with respect to style and intensity of the impending phenomenon. Our choice of the criteria was guided by so-called “Receive Operation Characteristics” (ROC) curves. These are based on the trade-off between the rate of False Positives and True Positives. With a more sensitive setting one can flag more paroxysms (True Positives); however, this may have the cost to flag an alert, but no paroxysm occurs. Carrying out various tests considering both the signal characteristics and the number of stations where the thresholds were met, we identified robust configurations allowing us to issue an alert of an impending paroxysm, widely avoiding the risk of false warnings. The system we propose here can provide timely and indicative information on possible eruptive scenarios to Civil Protection and other stakeholders. Also, It can be a guide for fixing onset and end-times of paroxysmal phenomena, which are especially helpful when image-based monitoring is hindered, for instance, by meteorological conditions. Finally, if others the possibility to effectively re-analyze long time spans of data recorded in the past.140 189 - 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
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