http://www.cnr.it/ontology/cnr/individuo/prodotto/ID34751

Wavelet analysis as a tool to characterise and remove environmental noise from self-potential time series (Articolo in rivista)

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Wavelet analysis as a tool to characterise and remove environmental noise from self-potential time series (Articolo in rivista) (literal)

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Chianese D, Colangelo G, D'Emilio M, Lanfredi M, Lapenna V, Ragosta M, Macchiato MF (2004)
Wavelet analysis as a tool to characterise and remove environmental noise from self-potential time series
in Annals of geophysics (Testo stamp.)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

Chianese D, Colangelo G, D'Emilio M, Lanfredi M, Lapenna V, Ragosta M, Macchiato MF (literal)

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Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

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Titolo

Wavelet analysis as a tool to characterise and remove environmental noise from self-potential time series (literal)

Abstract

Multiresolution wavelet analysis of self-potential signals and rainfall levels is performed for extracting fluctuations in electrical signals, which might be addressed to meteorological variability. In the time-scale domain of the wavelet transform, rain data are used as markers to single out those wavelet coefficients of the electric signal which can be considered relevant to the environmental disturbance. Then these coefficients are filtered out and the signal is recovered by anti-transforming the retained coefficients. Such methodological approach might be applied to characterise unwanted environmental noise. It also can be considered as a practical technique to remove noise that can hamper the correct assessment and use of electrical techniques for the monitoring of geophysical phenomena. (literal)

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