Application of Hilbert-Huang transform in EH-4 data processing
HUANG Ze-Jiao1,2(), XU Zi-Dong1,2(), LUO Han2, HUANG Yuan-Sheng2
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
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.
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.
[4]
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.
[5]
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.
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.
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.
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.
[11]
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.
[11]
Yan J B. Research on the theory and method of magnetotelluric signal processing[D]. Changsha: Central South University, 2003.
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.
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.
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.
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.
