Neural networks for blind-source separation of Stromboli explosion quakes

Author

Acernese, Fausto ; Ciaramella, Angelo ; De Martino, Salvatore ; De Rosa, Rosario ; Falanga, Mariarosaria ; Tagliaferri, Roberto

Author_Institution

Dipt. di Sci. Fisiche, Universita di Napoli "Federico II", Italy

Volume

14

Issue

1

fYear

2003

fDate

1/1/2003 12:00:00 AM

Firstpage

167

Lastpage

175

Abstract

Independent component analysis (ICA) is used to analyze the seismic signals produced by explosions of the Stromboli volcano. It has been experimentally proved that it is possible to extract the most significant components from seismometer recorders. In particular, the signal, eventually thought as generated by the source, is corresponding to the higher power spectrum, isolated by our analysis. Furthermore, the amplitude of the source signals has been found by using a simple trick and so overcoming, for this specific case, the classical problem of ICA regarding the amplitude loss of the separated signals.

Keywords

blind source separation; independent component analysis; neural nets; seismology; volcanology; Stromboli explosion quakes; Stromboli volcano; amplitude loss; blind-source separation; independent component analysis; neural networks; seismic signals; seismometer recorders; Data analysis; Explosions; Frequency; Independent component analysis; Neural networks; Noise reduction; Signal analysis; Source separation; Speech analysis; Volcanoes;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2002.806649

Filename

1176136