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Authors: Capuano, Paolo* 
De Lauro, Enza* 
De Martino, Salvatore* 
Falanga, Mariarosaria* 
Petrosino, Simona* 
Title: Coarse grained parameters for detection of volcano-tectonic activity inferred by CICA
Issue Date: Jul-2015
Keywords: Campi Flegrei
Abstract: The main aim of this work is to recognize the occurrence of seismic VT activity and its discrimination among other active and persistent natural and/or artificial sources, such as meteo-marine and anthropogenic noise. This task is of interest because it would support the routine practices of the observatories, considering that on the hourly scale events of very small energy are often completely hidden and not even the tedious work of operators can be resolved. To achieve that goal we adopt a robust automatic method, namely the Convolutive Independent Component Analysis (CICA), which in involves higher-order statistics in frequency domain. This technique is successful in seismological framework in the case of seismic signals, which can be considered as the convolution of time delayed source signals. In this work, we focus on Volcano Tectonic (VT) activity at Campi Flegrei Caldera (Italy) during the 2006 ground uplift. The activity is characterized approximately by 300 low-magnitude (Md < 2; for the definition of duration magnitude, see Petrosino et al., [2008]) VT earthquakes. Most of them were concentrated in distinct seismic sequences with hypocentres mainly clustered beneath the Solfatara-Accademia area, at depths ranging between 1 and 4 km b.s.l.. CICA is fruitfully applied to massive data on hourly scale obtaining a separation among different independent sources. Specifically, the identification of meteo-marine (< 1 Hz), anthropogenic noise (mainly affecting [8-14] Hz frequency range), and hydrothermal tremor (at about 0.8 Hz) is achieved in absence of VT activity, defining the background level. Variations of that underlying condition appear in approaching to the period of intense low-energy VT activity. Namely, a further component in 13-15 Hz compatible with the typical corner frequency of VTs is extracted. We propose a coarse-grained procedure directly applied to massive data separated through CICA, which would provide fast alert on the occurrence of even very-small VTs representing a suitable “observable” to monitor in the observatory practice.
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