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Wavelet analysis as a tool to characteriseand remove environmental noisefrom self-potential time series

机译:小波分析作为特征的工具,从潜在的时间序列中去除环境噪声

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摘要

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.
机译:对自势信号和降雨水平进行多辨积小波分析,用于提取波动 在电信号中,这可能被解决到气象变异性。在小波变换的时尺度域中,雨量数据用作标记,以单个可以被认为与环境干扰相关的电信号的那些小波系数。然后滤除这些系数 并且通过抗变换保留的系数来恢复信号。这种方法论方法可能 适用于表征不需要的环境噪音。它也可以被认为是一种实用的技术 去除可以妨碍正确评估和使用电气技术的噪音,以监测地球物理现象。

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