Magnetotelluric data de-noising based on wavelet transform and independent component analysis
Cao Xiaoling1,2,3, Liu Kaiyuan4, Yan Liangjun1,2
1. Key Laboratory of Exploration Technology for Oil and Gas Resources in Ministry of Education, Yangtze University, Wuhan, Hubei 430100, China; 2. Hubei Cooperation Innovation Center of Unconventional Oil and Gas, Wuhan, Hubei, 430100, China; 3. School of Information and Mathematics, Yangtze University, Jingzhou, Hubei 434023, China; 4. Geophysical Prospecting Company, Chuanqing Drilling Engineering Limited Company, CNPC, Chengdu, Sichuan 610213, China
Abstract:A de-noising method for magnetotelluric data with independent component analysis based on wavelet analysis is presented.Simulation experiments on synthetic signals show that the de-noising stability of the proposed method is better than the conventional wavelet-threshold de-noising method.Tests on real magnetotelluric data demonstrate that this method can effectively remove noise except at the extreme-low frequency band.So the proposed method provides a guarantee for the subsequent data processing quality.
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Cao Xiaoling, Liu Kaiyuan, Yan Liangjun. Magnetotelluric data de-noising based on wavelet transform and independent component analysis. Oil Geophysical Prospecting, 2018, 53(1): 206-213.
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