WAVELET ANALYSIS AS A TOOL TO CHARACTERISE AND REMOVE ENVIRONMENTAL NOISE FROM SELF-POTENTIAL TIME SERIES

Abstract

Multiresolution wavelet analysis of self-potential signals and rainfall levels is performed for extracting fluctua- tions 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 sig- nal 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 geo- physical phenomena.