Magnetotelluric noise elimination for oil and gas exploration based on CEEMDAN and ICA
CAO Xiaoling1,2, TANG Xingong1, JIANG Tao3
1. Key Laboratory of Exploration Technologies for Oil and Gas Resources(Yangtze University), Ministry of Education, Wuhan, Hubei 430100, China; 2. School of Information and Mathematics, Yangtze University, Jingzhou, Hubei 434023, China; 3. School of Electronic Information, Yangtze University, Jingzhou, Hubei 434023, China
Abstract:The noise in magnetotelluric signals seriously affects the observation results of magnetotelluric exploration, which leads to serious deviations in subsequent inversion and interpretation and affects the effect of oil and gas exploration. Therefore, this paper presents a denoising method based on complete ensemble empirical mode decomposition (CEEMD) with adaptive noise (CEEMDAN) and independent component analysis (ICA). This method effectively combines the CEEMDAN method in EMD with the ICA method in blind source separation (BSS). Firstly, the improved endpoint detection technology is used to identify the noisy signal segments in the electromagnetic signal. Then, the extracted noisy signal segments are decomposed by CEEMDAN, and the representative components of the intrinsic mode function (IMF) are extracted and processed by ICA to eliminate the noise. After that, the obtained independent components are used for the reverse reconstruction of useful MT signals. Finally, the MT signal without noise pollution is spliced with the useful MT signals after denoising to obtain the final complete denoised MT signal. The results of experiments on synthetic signals and measured MT signals show that the noise in MT signals can be effectively eliminated by this method.
曹小玲, 唐新功, 蒋涛. 基于CEEMDAN和ICA的油气勘探大地电磁噪声消除方法[J]. 石油地球物理勘探, 2023, 58(3): 740-750.
CAO Xiaoling, TANG Xingong, JIANG Tao. Magnetotelluric noise elimination for oil and gas exploration based on CEEMDAN and ICA. Oil Geophysical Prospecting, 2023, 58(3): 740-750.
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