An analysis method for magnetotelluric data based on the Hilbert–Huang Transform

Cai, Jinhua; Tang, Jingtian; Xi-rui, Hua; Gong, Yurong

doi:10.1071/eg08124

Cited by 34 publications

(19 citation statements)

References 12 publications

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“…8 For a time series x(t), all the local maxima and the local minima are connected respectively by two special lines which are obtained by Department of Physics and Electronics, Hunan University of Arts and Science, Changde, China using an interpolation method, called the upper envelope and the lower envelope. Their mean is defined as m l .…”

Section: Principle Of Methods Empirical Mode Decompositionmentioning

confidence: 99%

Fault diagnosis of rolling bearing based on empirical mode decomposition and higher order statistics

Cai

2014

Proceedings of the Institution of Mechanical Engineers, Part C:

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In order to solve the problem of the faulted rolling bearing signal getting easily affected by Gaussian noise, a new fault diagnosis method was proposed based on empirical mode decomposition and high-order statistics. Firstly, the vibration signal was decomposed by empirical mode decomposition and the correlation coefficient of each intrinsic mode function was calculated. These intrinsic mode function components, which have a big correlation coefficient, were selected to estimate its higher order spectrum. Then based on the higher order statistics theory, this method uses higher order spectrum of each intrinsic mode function to reconstruct its power spectrum. And these power spectrums were summed to obtain the primary power spectrum of bearing signal. Finally, fault feature information was extracted from the reconstructed power spectrum. A model, using higher order spectrum to reconstruct power spectrum, was established. Meanwhile, analysis was conducted by using the simulated data and the recorded vibration signals which include inner race, out race, and bearing ball fault signal. Results show that the presented method is superior to traditional power spectrum method in suppressing Gaussian noise and its resolution is higher. New method can extract more useful information compared to the traditional method.

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Section: Principle Of Methods Empirical Mode Decompositionmentioning

confidence: 99%

Fault diagnosis of rolling bearing based on empirical mode decomposition and higher order statistics

Cai

2014

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…This paper is not going to cover the basic principles of HHT which have been introduced in much of the literature [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. We will focus only on mode mixing problems of the EMD method in HHT.…”

Section: Mode Mixing Problemmentioning

confidence: 99%

“…The key part of the method is the empirical mode decomposition (EMD), which can decompose the signal into a finite number of intrinsic mode functions (IMF) in the temporal domain, with each IMF being a narrow-band signal [6,7]. However, the EMD method can not guarantee orthogonality between the decomposed IMF and aliasing between IMFs, which is unfavorable for seismic data analysis and may lead to an incorrect interpretation [5,8]. The mode mixing problem of EMD will be described in detail in Section 2.…”

Section: Introductionmentioning

confidence: 99%

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Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

Huang

Malekian

et al. 2017

Energies

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Hilbert-Huang transform (HHT) is a popular method to analyze nonlinear and non-stationary data. It has been widely used in geophysical prospecting. This paper analyzes the mode mixing problems of empirical mode decomposition (EMD) and introduces the noncontact measurement and detection of instantaneous seismic attributes using complementary ensemble empirical mode decomposition (CEEMD). Numerical simulation testing indicates that the CEEMD can effectively solve the mode mixing problems of EMD and can provide stronger anti-noise ability. The decomposed results of the synthetic seismic record show that CEEMD has a better ability to decompose seismic signals. Then, CEEMD is applied to extract instantaneous seismic attributes of 3D seismic data in a real-world coal mine in Inner Mongolia, China. The detection results demonstrate that instantaneous seismic attributes extracted by CEEMD are helpful to effectively identify the undulations of the top interfaces of limestone.

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“…Another way to suppress MT noise in the time-frequency domains is by using the wavelet transform [Garcia and Jones, 2008;Trad and Travassos, 2000] or the Hilbert-Huang transform (HHT) [Chen et al, 2012;Cai et al, 2009]. These methods analyze the MT data by dividing the data into a series of details and approximations or empirical mode components.…”

mentioning

confidence: 99%

Synthesis of natural electric and magnetic Time‐series using Inter‐station transfer functions and time‐series from a Neighboring site (STIN): Applications for processing MT data

et al. 2017

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Synthesis of natural electric and magnetic Time series using Interstation transfer functions and time series from a Neighboring site (STIN) is a new approach for recovering natural electric and magnetic fields and reduce the influence of anthropogenic noise. The proposed approach modifies the windows of the local electric and magnetic time series that are affected by noise with time series resulted from modifying the spectra of the magnetic time series from a neighboring site with the interstation transfer functions between the local and neighboring sites. The STIN method was tested with artificially contaminated electric and magnetic time series. Comparison between STIN‐corrected time series and original noncontaminated time series shows high similarity, both in the time and frequency domains. Differences were quantified using the normalized root‐mean‐square error, the correlation coefficient, and the signal‐to‐noise ratio. The STIN method was also applied to two sites affected by unconstrained anthropogenic noise, thus demonstrating the ability and accuracy of STIN in synthesizing natural electric and magnetic fields and reducing the influence of anthropogenic noise. The synthesized time series provided by STIN show the method to be valuable for magnetotelluric (MT) geophysical applications, by increasing the reliability when constrains the MT impedance tensor and by reducing the scatter of data points when the time series are affected by noise, particularly for longer periods. As STIN is based on interstation transfer functions, the electric and magnetic time series can be treated independently, enabling computation of the MT impedance tensor even when the electric and magnetic time series of the local site were recorded at different times.

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An analysis method for magnetotelluric data based on the Hilbert–Huang Transform

Cited by 34 publications

References 12 publications

Fault diagnosis of rolling bearing based on empirical mode decomposition and higher order statistics

Fault diagnosis of rolling bearing based on empirical mode decomposition and higher order statistics

Noncontact Measurement and Detection of Instantaneous Seismic Attributes Based on Complementary Ensemble Empirical Mode Decomposition

Synthesis of natural electric and magnetic Time‐series using Inter‐station transfer functions and time‐series from a Neighboring site (STIN): Applications for processing MT data

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