Audio Magnetotelluric Signal-Noise Identification and Separation Based on Multifractal Spectrum and Matching Pursuit

Li, Jin; Zhang, Xian; Tang, Jingtian; Cai, Jin; Liu, Xiaoqiong

doi:10.1142/s0218348x19400073

Cited by 17 publications

(6 citation statements)

References 27 publications

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“…In this research, a library of 200 data samples was used from field measurements [ 29 ]: where 50 samples were MT signals without interference collected from a remote area with no man-made activities in the Qinghai province. The rest of the contaminated data (referred to as “square wave interference, triangle wave interference, and pulse wave interference”) were collected from the ore concentration area.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Magnetotelluric Signal-Noise Separation Using IE-LZC and MP

Zhang

Jin

et al. 2019

Entropy

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Eliminating noise signals of the magnetotelluric (MT) method is bound to improve the quality of MT data. However, existing de-noising methods are designed for use in whole MT data sets, causing the loss of low-frequency information and severe mutation of the apparent resistivity-phase curve in low-frequency bands. In this paper, we used information entropy (IE), the Lempel–Ziv complexity (LZC), and matching pursuit (MP) to distinguish and suppress MT noise signals. Firstly, we extracted IE and LZC characteristic parameters from each segment of the MT signal in the time-series. Then, the characteristic parameters were input into the FCM clustering to automatically distinguish between the signal and noise. Next, the MP de-noising algorithm was used independently to eliminate MT signal segments that were identified as interference. Finally, the identified useful signal segments were combined with the denoised data segments to reconstruct the signal. The proposed method was validated through clustering analysis based on the signal samples collected at the Qinghai test site and the measured sites, where the results were compared to those obtained using the remote reference method and independent use of the MP method. The findings show that strong interference is purposefully removed, and the apparent resistivity-phase curve is continuous and stable. Moreover, the processed data can accurately reflect the geoelectrical information and improve the level of geological interpretation.

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Section: Experiments and Resultsmentioning

confidence: 99%

Magnetotelluric Signal-Noise Separation Using IE-LZC and MP

Zhang

Jin

et al. 2019

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“…Therefore, using modern data processing technology to remove the strong interference existing in measured MT signals has become an important research topic in the field of electromagnetic exploration, and the improvement of MT data quality in the strong interference area will provide strong technical support for the subsequent inversion interpretation (Ren et al ., 2013; Qi et al ., 2020). A wide variety of methods have been proposed for solving this problem, such as short‐time Fourier transform (Vozoff, 1972; Kao and Rankin, 1977; Griffin and Lim, 1984), remote reference (RR) method (Goubau et al ., 1978; Gamble et al ., 1979; Clarke et al ., 1983; Kappler, 2012), robust estimation (Egbert and Booker, 1986; Larsen, 1989; Chave and Thomson, 1989, 2004; Larsen et al ., 1996; Egbert, 1997), wavelet transform (Trad and Travassos, 2000; He et al ., 2009; Carbonari et al ., 2017), Hilbert–Huang transform (HHT) and empirical mode decomposition (EMD; Chen et al ., 2012; Cai, 2014; Chen and Fomel, 2018; Liu et al ., 2019), mathematical morphological filtering (MMF; Tang et al ., 2012b), inter‐station transfer functions (Wang et al ., 2017), Self‐organizing Map (SOM) neural networks (Carbonari et al ., 2018), multifractal spectrum and matching pursuit (MP; Li et al ., 2019), Mahalanobis distance and magnetic field constraints (Platz and Weckmann, 2019), shift‐invariant sparse coding (Li et al ., 2020) etc. These methods have certain advantages and promote the development of MT signal–noise separation research to a certain extent, but there are still some shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Denoising of magnetotelluric data using K‐SVD dictionary training

Peng

Tang

et al. 2021

Geophysical Prospecting

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Magnetotelluric is one of the mainstream exploration geophysical methods, which plays a vital role in studying deep geological structures and finding deep hidden blind ore bodies. The seriousness of human electromagnetic noise causes a large number of abnormal waveforms in the time series of measured magnetotelluric data, and the data can no longer objectively reflect the underground electrical distribution. In this work, we propose a magnetotelluric time series data processing method based on K singular value decomposition dictionary training. First, a training matrix and a to-beprocessed matrix are built with the pending magnetotelluric signals. Then, let the K singular value decomposition dictionary training process the training matrix to obtain an over-complete dictionary reflecting the characteristics of the pending signal. Lastly, orthogonal matching pursuit is combined with an over-complete dictionary updated in real time to sparsely represent the to-be-processed matrix and remove human electromagnetic interference in the signal. Experimental results show that the method can update the over-complete dictionary in real-time according to the pending magnetotelluric signals, realize the self-learning signal-noise separation of magnetotelluric signals, and effectively retain low-frequency information. Compared with method of directions dictionary learning, remote reference method, and orthogonal matching pursuit method, the reconstructed data of the proposed method can more accurately reflect the underground electrical structure information.

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“…Because the angular spectrum is seriously distorted in adverse environments, the estimated DOA when using PS may have a large offset, resulting in instability. Matching pursuit (MP) [20] can be used to improve the performance of PS by calculating the maximum inner product, but the choice of matching structure and atom width needs careful consideration. On the basis of source contributions, iterative contribution removal (ICR) [15] filters out the TF bins associated with the current estimated sound source during each iteration and then reconstructs the angular spectrum from the remainder to the next source localization.…”

Section: Introductionmentioning

confidence: 99%

“…e filtered GCC is termed GCCTF. In the localization and counting step, PS with the single-point amplitude is replaced by MP [8,20] with the inner product of the atom from the perspective of contribution removal.…”

Section: Introductionmentioning

confidence: 99%

Multiple Sound Source Localization and Counting Using One Pair of Microphones in Noisy and Reverberant Environments

Fang

2020

Mathematical Problems in Engineering

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A multiple sound source localization and counting method based on an angular spectrum is proposed in this paper. Local signal-to-noise ratio tracking, onset detection, and a coherence test are introduced to filter the generalized cross-correlation angular spectrum in the time-frequency domain for multiple sound source localization and counting in noisy and reverberant environments. Then, dual-width matching pursuit is introduced to replace peak search as the method of localization and counting. A comprehensive comparison of two statistical indicators, mean precision and mean absolute estimated error, indicates that the proposed localization and counting algorithm using both the filtered angular spectrum and dual-width matching pursuit method is more robust and accurate than the classic counterpart, especially in environments with low signal-to-noise ratio, strong reverberation, and abundant sound sources.

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Audio Magnetotelluric Signal-Noise Identification and Separation Based on Multifractal Spectrum and Matching Pursuit

Cited by 17 publications

References 27 publications

Magnetotelluric Signal-Noise Separation Using IE-LZC and MP

Magnetotelluric Signal-Noise Separation Using IE-LZC and MP

Denoising of magnetotelluric data using K‐SVD dictionary training

Multiple Sound Source Localization and Counting Using One Pair of Microphones in Noisy and Reverberant Environments

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