Application of Hilbert-Huang transform in EH-4 data processing

HUANG Ze-Jiao; XU Zi-Dong; LUO Han; HUANG Yuan-Sheng

doi:10.11720/wtyht.2022.1437

2022 Vol. 46, No. 5

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Article navigation > Geophysical and Geochemical Exploration > 2022 > 46(5): 1232-1240

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HUANG Ze-Jiao, XU Zi-Dong, LUO Han, HUANG Yuan-Sheng. 2022. Application of Hilbert-Huang transform in EH-4 data processing. Geophysical and Geochemical Exploration, 46(5): 1232-1240. doi: 10.11720/wtyht.2022.1437

Citation:

HUANG Ze-Jiao, XU Zi-Dong, LUO Han, HUANG Yuan-Sheng. 2022. Application of Hilbert-Huang transform in EH-4 data processing. Geophysical and Geochemical Exploration, 46(5): 1232-1240. doi: 10.11720/wtyht.2022.1437

Application of Hilbert-Huang transform in EH-4 data processing

1. The key Laboratory of Marine Geological Resources and Environment of Hainan Province,Haikou 570206,China；
2. Hainan Investigation Institute of Hydrogeology and Engineering Geology, Haikou 571100, China

Received Date: 12 August 2021

Revised Date: 20 October 2022

Publish Date: 03 January 2023

Abstract

Abstract

Industrial frequency noise comes from the electromagnetic noise produced in social activities, and it causes apparent resistivity curves to become pathological or divergent. To improve the accuracy of data processing and interpretation, this study used the Hilbert-Huang transform (HHT) to remove the common power frequency noise in EH-4 data. According to the time series processing and analysis results of measured data, this method can self-adaptively decompose signals according to the time-scale characteristics of the data and successfully remove the industrial frequency noise in the data, thus providing an effective way to remove the noise in magnetotelluric signals. In addition, this study also analyzed the serious modal aliasing and "end effect" occurring in the process of the empirical mode decomposition and decomposed simulation signals and the time series of measured data using the ensemble empirical mode decomposition (EEMD), effectively solving problems such as modal aliasing.
- EH-4 data denoising /
- empirical mode decomposition(EMD) /
- Hilbert-Huang transform /
- ensemble empirical mode decomposition (EEMD)

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References

[1]	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
[2]	Wang S M, Wang J Y. Analysis on statistic characteristics of magnetotelluric signal[J]. Acta Seismologica Sinica, 2004, 26(6): 669-674. Google Scholar
[3]	王书明, 王家映. 关于大地电磁信号非最小相位性的讨论[J]. 地球物理学进展, 2004, 19(2): 216-221. Google Scholar
[4]	Wang S M, Wang J Y. Discussion on the nonminimum phase of magnetotelluric signals[J]. Progress in Geophysics, 2004, 19(2): 216-221. Google Scholar
[5]	何兰芳, 王绪本, 何展翔, 等. MT时间序列的小波去噪分析[J]. 地震地质, 2001, 23(2): 222-226. Google Scholar
[6]	He L F, Wang X B, He Z X, et al. Wavelet-based denoising of MT time series[J]. Seismology and Geology, 2001, 23(2): 222-226. Google Scholar
[7]	Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis[J]. Proceeding of the Royal Society of London,1998, 454(1971):903-995. Google Scholar
[8]	Huang N E, Shen Z, Long S R. A new view of the nonlinear water waves:The Hilbert spectrum[J]. Annual Review of Fluid. 1999, 31(1):417-457. Google Scholar
[9]	谭善文. 多分辨希尔伯特—黄(Hilbert-Huang)变换方法的研究[D]. 重庆: 重庆大学, 2001. Google Scholar
[10]	Tan S W. Research on multi-resolution Hilbert-Huang transformation method[D]. Chongqing: Chongqing University, 2001. Google Scholar
[11]	杨生. 大地电磁测深法环境噪声抑制研究及应用[D]. 长沙: 中南大学, 2004. Google Scholar
[12]	Yang S. Research and application of environmental noise restraining by magnetotelluric sounding[D]. Changsha: Central South University, 2004. Google Scholar
[13]	化希瑞, 汤井田, 朱正国, 等. EH-4系统的数据二次处理技术及应用[J]. 地球物理学进展, 2008, 23(4): 1261-1268. Google Scholar
[14]	Hua X R, Tang J T, Zhu Z G, et al. The improvement and applications of EH-4 system data processing technique[J]. Progress in Geophysics, 2008, 23(4): 1261-1268. Google Scholar
[15]	汤井田, 蔡剑华, 任政勇, 等. Hilbert-Huang变换与大地电磁信号地时频分析[J]. 中南大学学报:自然科学版, 2009, 40(5):1400-1405. Google Scholar
[16]	Tang J T, Cai J H, Ren Z Y, et al. Hilbert-Huang transform and time-frequency analysis of magnetotelluric signal[J]. Journal of Central South University:Science and Technology, 2009, 40(5):1400-1405. Google Scholar
[17]	练继建, 荣钦彪, 董霄峰, 等. 抑制模态混叠的HHT结构模态参数识别方法研究[J]. 振动与冲击, 2018, 37(18):1-8. Google Scholar
[18]	Lian J J, Rong Q B, Dong X F, et al. Structural model parameter identification method based on an improved HHT for suppressing mode mixing[J]. Journal of Vibration and Shock, 2018, 37(18):1-8. Google Scholar
[19]	严家斌. 大地电磁信号处理理论及方法研究[D]. 长沙: 中南大学, 2003. Google Scholar
[20]	Yan J B. Research on the theory and method of magnetotelluric signal processing[D]. Changsha: Central South University, 2003. Google Scholar
[21]	张双喜, 陈超, 王林松, 等. 二维经验模态分解及其在位场去噪和分离中的应用[J]. 地球物理学进展, 2015, 30(6): 2855-2862. Google Scholar
[22]	Zhang S X, Chen C, Wang L S, et al. The bidimensional empirical mode decomposition and its applications to denoising and separation of potential field[J]. Progress in Geophysics, 2015, 30(6): 2855-2862. Google Scholar
[23]	白大为, 底青云, 王光杰, 等. Hilbert-Huang变换与E信号处理[J]. 地球物理学进展, 2009, 24(3): 1032-1038. Google Scholar
[24]	Bai D W, Di Q Y, Wang G J, et al. Hilbert-Huang transformation and ELF signal processing[J]. Progress in Geophysics, 2009, 24(3): 1032-1038. Google Scholar
[25]	于彩霞, 巍文博, 景建恩, 等. 希尔伯特—黄变换在海底大地电磁测深数据处理中的应用[J]. 地球物理学进展, 2010, 25(3): 1046-1056. Google Scholar
[26]	Yu C X, Wei W B, Jing J E, et al. Application of Hilbert-Huang transformation to marine magnetotelluric sounding data processing[J]. Progress in Geophysics, 2010, 25(3): 1046-1056. Google Scholar
[27]	齐天, 裘焱, 吴亚锋. 利用聚合经验模态分解抑制振动信号中的模态混叠[J]. 噪声与振动控制, 2010, 30(2): 103-106. Google Scholar
[28]	Qi T, Qiu Y, Wu Y F. Application of EEMD to suppression of mode mixing in oscillation signals[J]. Noise and Vibration Control, 2010, 30(2): 103-106. Google Scholar
[29]	吕建新, 吴虎胜, 田杰. EEMD的非平稳信号降噪及其故障诊断应用[J]. 计算机工程与应用, 2011, 47(28): 223-227. Google Scholar
[30]	Lyu J X, Wu H S, Tian J. Signal denoising based on EEMD for non-stationary signals and its application in fault diagnosis[J]. Computer Engineering and Applications, 2011, 47(28): 223-227. Google Scholar
[31]	陈可, 李野, 陈澜. EEMD分解在电力系统故障信号检测中的应用[J]. 计算机仿真, 2010, 27(3): 263-266. Google Scholar
[32]	Chen K, Li Y, Chen L. Ensemble empirical mode decomposition for power quality detection applications[J]. Computer Simulation, 2010, 27(3): 263-266. Google Scholar
[33]	Wu Z, Huang N E. Ensemble empirical mode decomposition: A noise assisted data analysis method[J]. Advance in Adaptive Data Analysis, 2009, 1(1): 1-41. Google Scholar
[34]	刘慧婷, 张吴, 程家兴. 基于多项式拟合算法的EMD端点问题的处理[J]. 计算机工程与应用, 2004, 40(16): 84-86,100. Google Scholar
[35]	Liu H T, Zhang W, Cheng J X. Dealing with the end issue of EMD based on polynomial fitting algorithm[J]. Computer Engineering and Applications, 2004, 40(16): 84-86,100. Google Scholar
[36]	黄大吉, 赵进平, 苏纪兰. 希尔伯特—黄变换的端点延拓[J]. 海洋学报, 2003, 25(1): 1-11. Google Scholar
[37]	Huang D J, Zhao J P, Su J L. Endpoint extension of Hilbert-Huang transform[J]. Acta Oceanologica Sinica, 2003, 25(1): 1-11. Google Scholar