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A Neural Approach for Hybrid Events Discrimination at Stromboli Volcano

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Multidisciplinary Approaches to Neural Computing

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 69))

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Abstract

Stromboli volcano is considered one of the most active volcanoes in the world. During its effusive phases, it is possible to record a particular typology of events named “hybrid events”, that rarely are observed in the daily volcano activity. These ones are often associated to fault failure in the volcanic edifice due to magma movement and/or pressurization. Their identification, analysis and location can improve the volcano eruptive process comprehension. However, it is not easy to distinguish them from the other usually recorded events, i.e. explosion-quakes, through a visual seismogram analysis. Thus, we present an automatic supervised procedure, based on a Multi-layer Perceptron (MLP) neural network, to identify and discriminate them from the explosions-quakes. The data are encoded by using LPC coefficients and then adding to this coding waveform features. The 99% of accuracy was reached when waveform features are coded together with LPC coefficients as input to the network, emphasizing the importance of temporal features for discriminating hybrid events from explosion-quakes. The results allow us to assert that the proposed neural strategy can be included in a more complex automatic system for the monitoring of Stromboli volcano and of other volcanoes in the world.

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Author information

Authors and Affiliations

  1. Istituto Nazionale di Geofisica e Vulcanologia, Sezione di Napoli Osservatorio Vesuviano, Naples, Italy

    Antonietta M. Esposito & Flora Giudicepietro

  2. Dipartimento di Fisica “E.R.Caianiello”, INFN gruppo coll. di Salerno, Università di Salerno Fisciano (SA), sez. Di Napoli, Italy

    Silvia Scarpetta

  3. Indian Institute of Technology Delhi, Delhi, India

    Sumegha Khilnani

  4. Istituto Internazionale per gli Alti Studi Scientifici (IIASS), Vietri sul Mare (SA), Italy

    Sumegha Khilnani

Authors
  1. Antonietta M. Esposito
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  2. Flora Giudicepietro
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  3. Silvia Scarpetta
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  4. Sumegha Khilnani
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Corresponding author

Correspondence to Antonietta M. Esposito .

Editor information

Editors and Affiliations

  1. Dipartimento di Psicologia, Università della Campania “Luigi Vanvitelli”, Caserta, Italy

    Anna Esposito

  2. Fundació Tecnocampus, Pompeu Fabra University, Mataro, Spain

    Marcos Faudez-Zanuy

  3. Department of Civil, Environmental, Energy, and Material Engineering, Mediterranea University of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dipartimento di Elettronica e Telecomunicazioni, Politecnico di Torino, Laboratorio di Neuronica, Torino, Italy

    Eros Pasero

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Esposito, A.M., Giudicepietro, F., Scarpetta, S., Khilnani, S. (2018). A Neural Approach for Hybrid Events Discrimination at Stromboli Volcano. In: Esposito, A., Faudez-Zanuy, M., Morabito, F., Pasero, E. (eds) Multidisciplinary Approaches to Neural Computing. Smart Innovation, Systems and Technologies, vol 69. Springer, Cham. https://doi.org/10.1007/978-3-319-56904-8_2

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  • DOI: https://doi.org/10.1007/978-3-319-56904-8_2

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