Denoising of magnetotelluric data based on Hilbert-Huang transform

CHEN Jun; YAN Liang-Jun; ZHOU Lei

doi:10.11720/wtyht.2021.0047

2021 Vol. 45, No. 6

Article Contents

Article navigation > Geophysical and Geochemical Exploration > 2021 > 45(6): 1462-1468

Next Article Previous Article

CHEN Jun, YAN Liang-Jun, ZHOU Lei. 2021. Denoising of magnetotelluric data based on Hilbert-Huang transform. Geophysical and Geochemical Exploration, 45(6): 1462-1468. doi: 10.11720/wtyht.2021.0047

Citation:

CHEN Jun, YAN Liang-Jun, ZHOU Lei. 2021. Denoising of magnetotelluric data based on Hilbert-Huang transform. Geophysical and Geochemical Exploration, 45(6): 1462-1468. doi: 10.11720/wtyht.2021.0047

Denoising of magnetotelluric data based on Hilbert-Huang transform

1. Key Laboratory of oil and Gas Resources and Exploration Technology of Ministry of Education, Yangtze University, Wuhan 430100, China;
2. Cooperative Innovation Center of Unconventional Oil and Gas, Wuhan 430100, China

Received Date: 26 January 2021

Revised Date: 20 December 2021

Publish Date: 21 December 2021

Abstract

Abstract

Near-field effects are liable to rise in magnetotelluric signals due to noise, which seriously affects the quality of the collected signals. The widely used remote reference method-Robust-still has some shortcomings in suppressing near-field interference. In this paper, after being verified using numerical simulation in terms of denoising effects, the Hilbert-Huang transform (HHT) was applied to a magnetotelluric signal with serious near-field interference, achieving remarkable effects. Meanwhile, the polarization distribution of the electric and magnetic fields was calculated using the distribution function of polarization directions. It was found that the distribution function was closer to a Gaussian distribution function, also verifying the effectiveness of the HHT in suppressing near-field interference.
- HHT /
- magnetotelluric /
- cultural noise /
- near-field interference /
- error distribution function

FullText(HTML)

References

[1]	胡家华, 陈清礼. MT资料的噪声源分析及减小观测噪声的措施[J]. 石油天然气学报, 1999, 21(4):69-71. Google Scholar
[2]	Hu J H, Chen Q L. Noise source analysis of MT data and measures to reduce observation noise[J]. Acta Petrologica Sinica, 1999, 21(4):69-71. Google Scholar
[3]	王书明, 王家映, 肖建玲. 系统的构建及其功率谱和高阶谱的比较[J]. 石油物探, 2005, 44(3):202-205. Google Scholar
[4]	Wang S M, Wang J Y, Xiao J L. System construction and comparison of power spectrum and high order spectrum[J]. Petroleum Geophysical Prospecting, 2005, 44(3):202-205. Google Scholar
[5]	王书明, 王家映. 大地电磁信号统计特征分析[J]. 地震学报, 2004, 26(6):669-674. Google Scholar
[6]	Wang S M, Wang J Y. Statistical characteristics analysis of magnetotelluric signals[J]. Acta Seismica Sinica, 2004, 26(6):669-674. Google Scholar
[7]	王辉, 程久龙, 腾星智, 等. 矿区近场源噪声对大地电磁测深数据的影响及其压制方法[J]. 地球物理学进展, 2016, 31(3):1358-1366. Google Scholar
[8]	Wang H, Cheng J L, Teng X Z, et al. The influence of near-field source noise in mining area on magnetotelluric sounding data and its suppression methods[J]. Progress in Geophysics, 2016, 31(3):1358-1366. Google Scholar
[9]	刘俊峰, 邓居智, 陈辉, 等. 基于多结构元素形态滤波的大地电磁去噪[J]. 物探与化探, 2014, 38(1):109-114. Google Scholar
[10]	Liu J F, Deng J Z, Chen H, et al. Magnetotelluric denoising based on multi structural element morphological filtering[J]. Geophysical and Geochemical Exploration, 2014, 38(1):109-114. Google Scholar
[11]	李晋, 汤井田, 燕欢, 等. 基于递归分析和聚类的大地电磁信噪辨识及分离[J]. 地球物理学报, 2017, 60(5):1918-1936. Google Scholar
[12]	Li J, Tang J T, Yan H, et al. Magnetotelluric signal-to-noise identification and separation based on recursive analysis and clustering[J]. Journal of Geophysics, 2017, 60(5):1918-1936. Google Scholar
[13]	Huang N E, Wu Z. A review on Hilbert-Huang transform: Method and its applications to geophysical studies[J]. Reviews of Geophysics, 2008, 46(2). Google Scholar
[14]	白大为, 杜炳锐, 张鹏辉. 基于希尔伯特-黄变换的低频探地雷达弱信号处理技术及其在天然气水合物勘探中的应用[J]. 物探与化探, 2017, 41(6):1060-1067. Google Scholar
[15]	Bai D W, Du B R, Zhang P H. Weak signal processing technology of low frequency mine detection based on Hilbert Huang transform and its application in natural gas hydrate exploration[J]. Geophysical and Geochemical Exploration, 2017, 41(6):1060-1067. Google Scholar
[16]	杨洋, 何继善, 李帝铨. 在频率域基于小波变换和Hilbert解析包络的CSEM噪声评价[J]. 地球物理学报, 2018, 61(1):344-357. Google Scholar
[17]	Yang Y, He J S, Li D Q. CSEM noise evaluation based on wavelet transform and Hilbert analytic envelope in frequency domain[J]. Journal of Geophysics, 2018, 61(1):344-357. Google Scholar
[18]	蔡剑华. 基于Hilbert-Huang变换的大地电磁信号处理方法与应用研究[D]. 长沙:中南大学, 2010. Google Scholar
[19]	Cai J H. Magnetotelluric signal processing method and application research based on Hilbert-Huang transform[D]. Changsha: Central South University, 2010. Google Scholar
[20]	汤井田, 化希瑞, 曹哲民, 等. Hilbert-Huang变换与大地电磁噪声压制[J]. 地球物理学报, 2008, 51(2):603-610. Google Scholar
[21]	Tang J T, Hua X R, Cao Z M, et al. Hilbert-Huang transform and magnetotelluric noise suppression[J]. Acta Geophysica Sinica, 2008, 51(2):603-610. Google Scholar
[22]	Weckmann U, Magunia A, Ritter O. Effective noise separation for magnetotelluric single site data processing using a frequency domain selection scheme[J]. Geophysical Journal of the Royal Astronomical Society, 2010, 161(3):635-652. Google Scholar
[23]	毛炜, 金荣洪, 耿军平, 等. 一种基于改进Hilbert-Huang变换的非平稳信号时频分析法及其应用[J]. 上海交通大学学报, 2006, 40(5):724-729. Google Scholar
[24]	Mao W, Jin R H, Geng J P, et al. A non-stationary signal time-frequency analysis method based on improved Hilbert Huang transform and its application[J]. Journal of Shanghai Jiaotong University, 2006, 40(5):724-729. Google Scholar