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Entropy 2019, 21(2), 197; https://doi.org/10.3390/e21020197

Magnetotelluric Signal-Noise Identification and Separation Based on ApEn-MSE and StOMP

1
Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China
2
State Key Laboratory of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China
3
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
*
Authors to whom correspondence should be addressed.
Received: 15 December 2018 / Revised: 4 February 2019 / Accepted: 13 February 2019 / Published: 19 February 2019
(This article belongs to the Special Issue The 20th Anniversary of Entropy - Approximate and Sample Entropy)
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Abstract

Natural magnetotelluric signals are extremely weak and susceptible to various types of noise pollution. To obtain more useful magnetotelluric data for further analysis and research, effective signal-noise identification and separation is critical. To this end, we propose a novel method of magnetotelluric signal-noise identification and separation based on ApEn-MSE and Stagewise orthogonal matching pursuit (StOMP). Parameters with good irregularity metrics are introduced: Approximate entropy (ApEn) and multiscale entropy (MSE), in combination with k-means clustering, can be used to accurately identify the data segments that are disturbed by noise. Stagewise orthogonal matching pursuit (StOMP) is used for noise suppression only in data segments identified as containing strong interference. Finally, we reconstructed the signal. The results show that the proposed method can better preserve the low-frequency slow-change information of the magnetotelluric signal compared with just using StOMP, thus avoiding the loss of useful information due to over-processing, while producing a smoother and more continuous apparent resistivity curve. Moreover, the results more accurately reflect the inherent electrical structure information of the measured site itself. View Full-Text
Keywords: magnetotelluric; signal-noise identification and separation; approximate entropy (ApEn); multiscale entropy (MSE); stagewise orthogonal matching pursuit (StOMP) magnetotelluric; signal-noise identification and separation; approximate entropy (ApEn); multiscale entropy (MSE); stagewise orthogonal matching pursuit (StOMP)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Li, J.; Cai, J.; Peng, Y.; Zhang, X.; Zhou, C.; Li, G.; Tang, J. Magnetotelluric Signal-Noise Identification and Separation Based on ApEn-MSE and StOMP. Entropy 2019, 21, 197.

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