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  • 1
    Publication Date: 2019-12-05
    Description: Early-warning assessment of a volcanic unrest requires that accurate information from monitoring is continuously gathered before volcanic activity starts. Seismic data are an optimal source of such information, overcoming safety problems due to dangerous conditions for field surveys or cloud cover that may hinder visibility. We designed a multi-station warning system based on the classification of patterns of the background seismic radiation, so-called volcanic tremor, by using Self-Organizing Maps (SOM) and fuzzy clustering. The classifier automatically detects patterns that are typical footprints of volcanic unrest. The issuance of the SOM colors on DEM allows their geographical visualization according to the stations of detection; this spatial location makes it possible to infer areas potentially impacted by eruptive phenomena. Tested at Mt. Etna (Italy), the classifier 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). Receiver Operating Characteristics (ROC) curves gave an Area Under the Curve (AUC) ∼0.8 indicative of a good detection accuracy that cannot be achieved from a mere random choice.
    Description: This work was supported by the MED-SUV project, which has received funding from the European Union’s Seventh Program for research, technological development and demonstration under grant agreement No 308665.
    Description: Published
    Description: id 6506
    Description: 4V. Processi pre-eruttivi
    Description: JCR Journal
    Keywords: Etna, Volcanic tremor ; Volcano Monitoring, Pattern recognition ; Self organizing map, Fuzzy clustering ; 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: 2018-03-21
    Description: To communicate the importance of knowing the risk of non-structural damage caused by earthquakes, we developed applications based on Augmented Reality (AR) features. These applications run on mobile devices, such as tablets and smartphones, by using their video camera and other on-board sensors, such as GPS, accelerometer, and gyrocompass, from which AR users do take advantage. Combined with a specifically designed exhibit, our AR applications can contribute to increase the common awareness on seismic risk, providing useful information on how to have safer homes in case of an earthquake. Building codes do not take into account non-structural elements, leaving communities at risk of injuries, blocking escapes and even causing deaths. In this framework, the personal preparedness is of paramount importance. The development of our AR applications is supported by the European project KnowRISK (Know your city, Reduce seISmic risK through non-structural elements).
    Description: Published
    Description: Reykjavik, Iceland
    Description: 2TM. Divulgazione Scientifica
    Keywords: Non-structural damage ; Earthquake hazard ; Augmented reality ; Risk reduction ; Dissemination ; 04. Solid Earth::04.06. Seismology::04.06.11. Seismic risk
    Repository Name: Istituto Nazionale di Geofisica e Vulcanologia (INGV)
    Type: Conference paper
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