Magnetotelluric signal-noise separation method based on SVM–CEEMDWT

Li Jin; Cai Jin; Tang Jing-Tian; Li Guang; Zhang Xian; Xu Zhi-Min

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Magnetotelluric signal-noise separation method based on SVM–CEEMDWT

机译：基于SVM–CEEMDWT的大地电磁信噪分离方法

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To better retain useful weak low-frequency magnetotelluric (MT) signals with strong interference during MT data processing, we propose a SVM–CEEMDWT based MT data signal-noise separation method, which extracts the weak MT signal affected by strong interference. First, the approximate entropy, fuzzy entropy, sample entropy, and Lempel–Ziv (LZ) complexity are extracted from the magnetotelluric data. Then, four robust parameters are used as the inputs to the support vector machine (SVM) to train the sample library and build a model based on the different complexity of signals. Based on this model, we can only consider time series with strong interference when using the complementary ensemble empirical mode decomposition (CEEMD) and wavelet threshold (WT) for noise suppression. Simulation results suggest that the SVM based on the robust parameters can distinguish the time periods with strong interference well before noise suppression. Compared with the CEEMDWT, the proposed SVM–CEEMDWT method retains more low-frequency low-variability information, and the apparent resistivity curve is smoother and more continuous. Moreover, the results better reflect the deep electrical structure in the field.

机译：为了更好地保留在MT数据处理过程中有用且弱干扰的弱低频大地电磁（MT）信号，我们提出了一种基于SVM–CEEMDWT的MT数据信噪分离方法，该方法提取受强干扰影响的弱MT信号。首先，从大地电磁数据中提取近似熵，模糊熵，样本熵和Lempel-Ziv（LZ）复杂度。然后，将四个鲁棒参数用作支持向量机（SVM）的输入，以训练样本库并基于信号的不同复杂度建立模型。基于此模型，当使用互补集成经验模式分解（CEEMD）和小波阈值（WT）进行噪声抑制时，我们只能考虑具有强烈干扰的时间序列。仿真结果表明，基于鲁棒参数的支持向量机可以在噪声抑制之前很好地区分具有强烈干扰的时间段。与CEEMDWT相比，提出的SVM–CEEMDWT方法保留了更多的低频低变异性信息，并且视电阻率曲线更平滑，更连续。此外，结果更好地反映了现场深层的电气结构。

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《应用地球物理（英文版）》 |2019年第2期|160-170|共11页
作者
Li Jin; Cai Jin; Tang Jing-Tian; Li Guang; Zhang Xian; Xu Zhi-Min;
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作者单位

Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China;

School of Geosciences and Info-Physics, Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment Monitor, Ministry of Education, Central South University, Changsha 410083, China;

Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China;

School of Geosciences and Info-Physics, Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment Monitor, Ministry of Education, Central South University, Changsha 410083, China;

School of Geosciences and Info-Physics, Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment Monitor, Ministry of Education, Central South University, Changsha 410083, China;

State Key Laboratory Breeding Base of Nuclear Resources and Environment, East China University of Technology, Nanchang 330013, China;

Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, College of Information Science and Engineering, Hunan Normal University, Changsha 410081, China;

School of Geosciences and Info-Physics, Key Laboratory of Metallogenic Prediction of Non-Ferrous Metals and Geological Environment Monitor, Ministry of Education, Central South University, Changsha 410083, China;

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Magnetotelluric signal-noise separation method based on SVM–CEEMDWT

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