De-noising magnetotelluric data based on mathematical morphology and K-SVD dictionary learning

被引：0

作者：

Gui T.-F. ^{[1
,2
,3
]}

Deng J.-Z. ^{[1
,3
]}

Li G. ^{[1
,2
,3
]}

Liu X.-Q. ^{[1
,2
]}

Chen H. ^{[1
,3
]}

He Z.-S. ^{[1
,2
,3
]}

机构：

[1] Earthquake Prevention and Mitigation and Engineering Geological Disaster Detection Engineering Research Center in Jiangxi Province, East China University of Technology, Nanchang

[2] Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology, East China University of Technology, Nanchang

[3] School of Geophysics and Measurement and Control Technology, East China University of Technology, Nanchang

来源：

Zhongguo Youse Jinshu Xuebao/Chinese Journal of Nonferrous Metals | 2021年 / 31卷 / 12期

基金：

中国国家自然科学基金;

关键词：

K-SVD dictionary learning; Low-frequency signal; Magnetotelluric; Mathematical morphological filtering; Strong interference suppression;

D O I：

10.11817/j.ysxb.1004.0609.2021-0078

中图分类号：

学科分类号：

摘要：

In order to solve the problem that the conventional magnetotelluric (MT) time-domain denoising methods have the limitation of damageing low-frequency signal, a new method based on mathematical morphological filtering (MMF) and K-singular value decomposition (K-SVD) dictionary learning was proposed to suppress strong cultural noise near 1Hz of MT data. First, MMF was used to separate the low-frequency signal and protect it entirely. Second, K-SVD dictionary learning was used to process residual noisy signals. The field data was used to extract noise contours from auto-learning cultural noise features, which could achieve the purpose of removing noise. The method was verified by testing a synthetic data set and then was used to process two measured data sets. The results show that the proposed method can eliminate all kinds of strong cultural noises without losing useful signals and improve the signal-to-noise ratio and data quality. Moreover, the denoising effect of the proposed method is better than the traditional methods such as wavelet transform. © 2021, China Science Publishing & Media Ltd. All right reserved.

引用

页码：3713 / 3729

页数：16

共 29 条

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