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Electromagnetic noise reduction in grounded electrical‐source airborne transient electromagnetic signal using a stationarywavelet‐based denoising algorithm
Author(s) -
Li Dongsheng,
Wang Yuan,
Lin Jun,
Yu Shengbao,
Ji Yanju
Publication year - 2017
Publication title -
near surface geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.639
H-Index - 39
eISSN - 1873-0604
pISSN - 1569-4445
DOI - 10.3997/1873-0604.2017003
Subject(s) - noise (video) , noise reduction , transient (computer programming) , signal (programming language) , algorithm , wavelet transform , wavelet , computer science , acoustics , physics , artificial intelligence , image (mathematics) , programming language , operating system
The grounded electrical‐source airborne transient electromagnetic signal is significantly affected by electromagnetic noise (sferics and aircraft engine electromagnetic noise), leading to inferior image formation. We calculate the theoretical response of the grounded electrical‐source airborne transient electromagnetic system and propose a denoising algorithm based on the transient characters of the theoretical electromagnetic response and the measured noise. The proposed algorithm utilises multi‐resolution analysis via a stationary wavelet transform of the data. Primarily, the measured data are decomposed into detailed coefficients and approximated coefficients. Then, the logarithmic slope of the measured data and a threshold are calculated to identify the noise in the detailed coefficients; the corresponding detailed coefficients are processed to reduce the noise. Finally, the undisturbed data are reconstructed using inverse stationary wavelet transform. The algorithm is verified using synthetic signal and is applied for electromagnetic noise suppression in synthetic data and real data collected over the coastal areas in Yanwei Harbor, China. The results confirm that the denoising algorithm can effectively remove the electromagnetic noise from the grounded electrical‐source airborne transient electromagnetic signal. Moreover, the image formations clearly reveal the structure of the Huaiyin–Xiangshui fault zone.