Neural networks for blind-source separation of Stromboli explosion quakes

F Acernese; A Ciaramella; S De Martino; R De Rosa; M Falanga; R Tagliaferri

doi:10.1109/TNN.2002.806649

Neural networks for blind-source separation of Stromboli explosion quakes

IEEE Trans Neural Netw. 2003;14(1):167-75. doi: 10.1109/TNN.2002.806649.

Authors

F Acernese¹, A Ciaramella, S De Martino, R De Rosa, M Falanga, R Tagliaferri

Affiliation

¹ Dipt. di Sci. Fisiche, Universita di Napoli "Federico II", Italy.

PMID: 18237999
DOI: 10.1109/TNN.2002.806649

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.