Title :
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
fDate :
1/1/2003 12:00:00 AM
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;
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2002.806649