Neural networks for blind-source separation of Stromboli explosion quakes

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 g...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2003-01, Vol.14 (1), p.167-175
Main Authors: Acernese, F., Ciaramella, A., De Martino, S., De Rosa, R., Falanga, M., Tagliaferri, R.
Format: Article
Language:English
Subjects:
Data analysis
Explosions
Frequency
Independent component analysis
Neural networks
Noise reduction
Signal analysis
Source separation
Speech analysis
Volcanoes
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Summary: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.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2002.806649