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.
Highlights
Environmental conditions act on self-potential variability driving non-stationary patterns which can highly distort background behaviours
The main idea we present in this paper is that the joint multiresolution wavelet analysis of self-potential signals and support data related to environmental forcing can represent a suitable basis to extract environment-induced electrical fluctuations
Wavelet transform translates the complexity of mixed global behaviours and transient patterns described by the electrical signals in simpler time sequences of coefficients over several resolutions or scales
Summary
Self-potential signals are good examples of inhomogeneous signals containing both regularities and isolated singularities in the form of pulses, jumps, power or deltalike singularities. In order to single out unwanted interferences, our first task is to analyse electrical fluctuations retaining information on the localization of discontinuities and transient variations To this purpose, wavelet analysis is a useful tool, able to carry out multiresolution studies and to enhance local features against long term dynamic structures. We transform the self-potential signal and, in the previously selected regions, we extract the excited wavelet coefficients Such coefficients account for local fluctuations which are candidates for describing the electrical responses to the external disturbance. Excited coefficients can be filtered out and the signal can be recovered by anti-transforming the retained coefficients In this exploratory work, we focus on hourly electrical variability observed during rainy periods using hourly rainfall levels as support data. With additional support data, our methodological approach can be useful to pick up long range features as well
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