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Open AccessArticle

Magnetotelluric Signal-Noise Separation Using IE-LZC and MP

by 1,2,3,4, 1,4,*, 2,3,*, 3, 2, 2 and 5
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment, Monitoring Ministry of Education, School of Geoscience and Info-Physics, Central South University, Changsha 410083, China
Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha 410081, China
Key Laboratory of Geophysical Electromagnetic Probing Technologies of Ministry of Natural Resources, Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Science, Langfang 065000, China
Hunan Key Laboratory of Nonferrous Resources and Geological Hazard Exploration, Changsha 410083, China
Hebei Instrument & Meter Engineering Technology Research Center, Chengde Petroleum College, Chengde 067000, China
Authors to whom correspondence should be addressed.
Entropy 2019, 21(12), 1190;
Received: 29 October 2019 / Revised: 21 November 2019 / Accepted: 2 December 2019 / Published: 4 December 2019
(This article belongs to the Special Issue Shannon Information and Kolmogorov Complexity)
Eliminating noise signals of the magnetotelluric (MT) method is bound to improve the quality of MT data. However, existing de-noising methods are designed for use in whole MT data sets, causing the loss of low-frequency information and severe mutation of the apparent resistivity-phase curve in low-frequency bands. In this paper, we used information entropy (IE), the Lempel–Ziv complexity (LZC), and matching pursuit (MP) to distinguish and suppress MT noise signals. Firstly, we extracted IE and LZC characteristic parameters from each segment of the MT signal in the time-series. Then, the characteristic parameters were input into the FCM clustering to automatically distinguish between the signal and noise. Next, the MP de-noising algorithm was used independently to eliminate MT signal segments that were identified as interference. Finally, the identified useful signal segments were combined with the denoised data segments to reconstruct the signal. The proposed method was validated through clustering analysis based on the signal samples collected at the Qinghai test site and the measured sites, where the results were compared to those obtained using the remote reference method and independent use of the MP method. The findings show that strong interference is purposefully removed, and the apparent resistivity-phase curve is continuous and stable. Moreover, the processed data can accurately reflect the geoelectrical information and improve the level of geological interpretation. View Full-Text
Keywords: magnetotelluric (MT); signal-noise separation; information entropy (IE); Lempel–Ziv complexity (LZC); matching pursuit (MP) magnetotelluric (MT); signal-noise separation; information entropy (IE); Lempel–Ziv complexity (LZC); matching pursuit (MP)
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MDPI and ACS Style

Zhang, X.; Li, D.; Li, J.; Li, Y.; Wang, J.; Liu, S.; Xu, Z. Magnetotelluric Signal-Noise Separation Using IE-LZC and MP. Entropy 2019, 21, 1190.

AMA Style

Zhang X, Li D, Li J, Li Y, Wang J, Liu S, Xu Z. Magnetotelluric Signal-Noise Separation Using IE-LZC and MP. Entropy. 2019; 21(12):1190.

Chicago/Turabian Style

Zhang, Xian; Li, Diquan; Li, Jin; Li, Yong; Wang, Jialin; Liu, Shanshan; Xu, Zhimin. 2019. "Magnetotelluric Signal-Noise Separation Using IE-LZC and MP" Entropy 21, no. 12: 1190.

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