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| 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 |
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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.
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Received: 12 August 2021
Published: 03 January 2023
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Corresponding Authors:
XU Zi-Dong
E-mail: 1020236730@qq.com;70199117@qq.com
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Sieve graph divided by empirical mode decomposition(EMD)
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The simulation voltage signal to be decomposed
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The components of simulation signal
a—intermittent skip signal; b—sinusoidal signal; c—linear signal
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The result of EMD decomposition
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EMD decomposition and levels of intrinsic mode function based on measured EH-4 signal
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The result of EEMD decomposition
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EEMD decomposition and each intrinsic mode function based on measured EH-4 signal
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Time-frequency-energy spectrum of EH-4 signal
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The frequency spectrum of IMF decomposed by EEMD
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The result of signal reconfiguration after denoising
a—original signal ;b—signal reconfiguration decomposed by EEMD;c—signal reconfiguration decomposed by EMD; d—the error of signal reconstruction
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The original time series of measuring point
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The time series after de-noising power frequency
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Contrast of apparent resistivity curve of TE mode(a) and TM mode(b) before and after de-noising power frequency
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