Please use this identifier to cite or link to this item: http://hdl.handle.net/2122/7215
AuthorsCannata, A.* 
Sciotto, M.* 
Spampinato, L.* 
Spina, L.* 
TitleInsights into explosive activity at closely-spaced eruptive vents using infrasound signals: Example of Mt. Etna 2008 eruption
Issue Date2011
Series/Report no./208 (2011)
DOI10.1016/j.jvolgeores.2011.09.003
URIhttp://hdl.handle.net/2122/7215
KeywordsEtna
Infrasound
Infrasonic source location
explosive activity
Subject Classification04. Solid Earth::04.06. Seismology::04.06.08. Volcano seismology 
AbstractOn 13 May 2008 an eruptive fissure opened on Mount Etna's eastern flank feeding both explosive activity and lava effusion from multiple vents for about 14 months. During the investigated May-September 2008 eruptive period, infrasound recordings from a 4 station-sparse network allowed tracking of the explosive activity in terms of location and dynamics. In order to focus on activity from the eruptive fissure, the infrasonic events generated by the summit craters were selected by using both spectral features and time delays between pairs of stations and excluded from our analysis. Then, to accurately locate events from the fissure, we used a composite method, based on the semblance and brightness functions. This enabled the study of the co-existence of more than one infrasound source and/or its migration along the eruptive fissure. Hence, results permitted us to discriminate the number of active vents and their location along the fissure even when, due to poor weather conditions, it was not possible to access the vents or carry out direct observations. The eruptive activity was characterised by variations in the number of active vents according to the overall intensity of the eruptive event. Variability of the infrasound waveforms highlighted either that distinct vents produced signals with different waveforms, or that single vents generated different events during distinct periods of time, or finally both the previous phenomena. We applied the strombolian bubble vibration model to model waveform differences and attributed the signal variations to bubble radius changes.
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