Convolutive independent component analysis for processing massive datasets: a case study at Campi Flegrei (Italy)

P. Capuano (), E. Lauro (), S. Martino (), M. Falanga () and S. Petrosino ()
Additional contact information
P. Capuano: Università di Salerno
E. Lauro: Università di Salerno
S. Martino: Università di Salerno
M. Falanga: Università di Salerno
S. Petrosino: Sezione di Napoli - Osservatorio Vesuviano

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2017, vol. 86, issue 2, No 13, 417-429

Abstract: Abstract A novel procedure is proposed to analyse continuous seismic signal on hourly scales to have a prompt discrimination among the different sources. Specifically, this approach is applied to a massive dataset recorded at Campi Flegrei caldera during the year 2006 when a swarm of volcano-tectonic earthquakes occurred. The convolutive independent component analysis is adopted to obtain a clear separation among meteo-marine microseism, anthropogenic noise, hydrothermal tremor in the absence of volcano-tectonic activity, whereas in non-stationary conditions a contribution connected to the corner frequency of the earthquakes emerges. A coarse-grained variable to be monitored continuously is introduced, i.e. the frequency associated with the maximum amplitude of the power spectral density of the deconvolutive independent components. That parameter is sensitive to the variation in the frequency bands of interest (e.g. that corresponding to the corner frequencies of volcano-tectonic events) and can be used as marker of the insurgence of seismic activity.

Keywords: Convolutive independent component analysis; Campi Flegrei; Seismic signal discrimination (search for similar items in EconPapers)
Date: 2017
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11069-016-2545-0

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