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  • 04.06. Seismology
  • Springer Nature
  • American Institute of Physics
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  • 1
    Publication Date: 2022-10-28
    Description: From the 2010s on, pattern classification has proven an effective method for flagging alerts of volcano unrest before eruptive activity at Mt. Etna, Italy. The analysis has been applied online to volcanic tremor data, and has supported the surveillance activity of the volcano that provides timely information to Civil Protection and other authorities. However, after declaring an alert, no one knows how long the volcano unrest will last and if a climactic eruptive activity will actually begin. These are critical aspects when considering the effects of a prolonged state of alert. An example of longstanding unrest is related to the Christmas Eve eruption in 2018, which was heralded by several months of almost continuous Strombolian activity. Here, we discuss the usage of thresholds to detect conditions leading to paroxysmal activity, and the challenges associated with defining such thresholds, leveraging a dataset of 52 episodes of lava fountains occurring in 2021. We were able to identify conservative settings regarding the thresholds, allowing for an early warning of impending paroxysm in almost all cases (circa 85% for the first 4 months in 2021, and over 90% for the whole year). The chosen thresholds also proved useful to predict that a paroxysmal activity was about to end. Such information provides reliable numbers for volcanologists for their assessments, based on visual information, which may not be available in bad weather or cloudy conditions.
    Description: Project IMPACT (A multidisciplinary Insight on the kinematics and dynamics of Magmatic Processes at Mt. Etna Aimed at identifying preCursor phenomena and developing early warning sysTems). IMPACT belongs to the Progetti Dipartimentali INGV [DIP7], https://progetti.ingv.it/index.php/it/progetti-dipartimentali/vulcani/impact#informazioni-sul-progetto.
    Description: Published
    Description: 17895
    Description: 4V. Processi pre-eruttivi
    Description: JCR Journal
    Keywords: Volcanic tremor ; Volcano monitoring ; Pattern recognition ; Self Organizing maps ; Fuzzy clustering ; Mt. Etna ; 04.06. Seismology ; 04.08. Volcanology ; 05.01. Computational geophysics
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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  • 2
    Publication Date: 2022-03-16
    Description: Data visualization, and to a lesser extent data sonification, are classic tools to the scientific community. However, these two approaches are very rarely combined, although they are highly complementary: our visual system is good at recognizing spatial patterns, whereas our auditory system is better tuned for temporal patterns. In this article, data representation methods are proposed that combine visualization, sonification, and spatial audio techniques, in order to optimize the user’s perception of spatial and temporal patterns in a single display, to increase the feeling of immersion, and to take advantage of multimodal integration mechanisms. Three seismic data sets are used to illustrate the methods, covering different physical phenomena, time scales, spatial distributions, and spatio-temporal dynamics. The methods are adapted to the specificities of each data set, and to the amount of information that the designer wants to display. This leads to further developments, namely the use of audification with two time scales, the switch from pure audification to time-modulated noise, and the switch from pure audification to sonic icons. First user feedback from live demonstrations indicates that the methods presented in this article seem to enhance the perception of spatio-temporal patterns, which is a key parameter to the understanding of seismically active systems, and a step towards apprehending the processes that drive this activity.
    Description: Published
    Description: 125–142
    Description: 7T. Variazioni delle caratteristiche crostali e "precursori"
    Description: JCR Journal
    Keywords: 04.06. Seismology
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: article
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