scholarly journals Separation of magnetotelluric signals based on refined composite multiscale dispersion entropy and orthogonal matching pursuit

2021 ◽  
Author(s):  
Xian Zhang ◽  
Jin Li ◽  
Diquan Li ◽  
Yong Li ◽  
Bei Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Multiscale Entropy ◽  
Characteristic Parameters ◽  
Signal Noise ◽  
Useful Signal ◽  
Reconstructed Signal ◽  
Noise Identification

Abstract Magnetotelluric (MT) data processing can increase the reliability of measured data. Traditional MT de-noising methods are usually filtered in entire MT time-series sequence, which result in losing of useful MT signals and the decrease of imaging accuracy of electromagnetic inversion. However, targeted MT noise separation can retain the part of data not affected by strong noise, and enhance the quality of MT data. Thus, we proposed a novel method for MT noise separation, which using refined composite multiscale dispersion entropy (RCMDE) and orthogonal matching pursuit (OMP). Firstly, the RCMDE characteristic parameters are extracted from each segment of the MT time-series. Then, the characteristic parameters are input to the fuzzy c-mean (FCM) clustering for automatic identification of MT signal and noise. Next, OMP method is utilized to remove the identified noise segments independently. Finally, the reconstructed signal consists of the denoised data segments and the identified useful signal segments. We conducted the simulation experiments and algorithm evaluation on the EMTF data, simulated data and measured sites. The results indicate that the RCMDE can improve the stability of multiscale dispersion entropy (MDE) and multiscale entropy (MSE) by analyzing the characteristics of the signal samples library, effectively dividing MT signals and noise. Compared with the existing techniques of the entire time domain de-noising and signal-noise identification, the proposed method used RCMDE and OMP as characteristic parameter and noise separation, simplified the multi-features fusion, and improved the accuracy of signal-noise identification. Moreover, the de-noising efficiency has accelerated, and the MT data quality of low-frequency band has improved greatly.

scholarly journals Separation of Magnetotelluric Signals Based on Refined Composite Multiscale Dispersion Entropy and Orthogonal Matching Pursuit

2020 ◽  
Author(s):  
Xian Zhang ◽  
Jin Li ◽  
Diquan Li ◽  
Yong Li ◽  
Bei Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Multiscale Entropy ◽  
Characteristic Parameters ◽  
Signal Noise ◽  
Useful Signal ◽  
Reconstructed Signal ◽  
Noise Identification

Abstract Magnetotelluric (MT) signal processing can increase the reliability of data. Traditional MT de-noising methods are usually filtered in entire MT time-series sequence, which result in losing of useful MT signals and the degradation of electromagnetic inversion imaging accuracy. However, targeted MT noise separation will retain the part of data that is not affected by strong noise, and enhance the quality of MT data. Thus, we proposed a novel method which using refined composite multiscale dispersion entropy (RCMDE) and orthogonal matching pursuit (OMP) for MT noise separation. Firstly, we extracted the RCMDE characteristic parameters from each segment of the MT time-series. Then, the characteristic parameters are input to the fuzzy c-mean (FCM) clustering for automatic signal-noise identification. Next, the identified noise segments were independently denoised by using OMP method. Finally, the reconstructed signal consists of the denoised data segments and the identified useful signal segments. We conducted the simulation experiments and algorithm evaluation on the EMTF data, simulated data and measured sites. The results indicate that a variety of entropy without scale factor is not stable enough, and the RCMDE can improve the stability of multiscale dispersion entropy (MDE) and multiscale entropy (MSE) by analyzing the characteristics of the signal samples library, and thus effectively dividing MT signals and noise. In this paper, the existing techniques of the entire time domain de-noising and signal-noise identification method are compared. Only for RCMDE and OMP as characteristic parameter and noise separation method, the proposed method simplified the multi-features fusion method, and improved the accuracy of signal-noise identification, accelerated de-noising efficiency, and improved the MT data quality of low-frequency band.

scholarly journals Separation of magnetotelluric signals based on refined composite multiscale dispersion entropy and orthogonal matching pursuit

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Xian Zhang ◽  
Jin Li ◽  
Diquan Li ◽  
Yong Li ◽  
Bei Liu ◽  
...  
Keyword(s):  
Time Series ◽  
Clustering Algorithm ◽  
Feature Fusion ◽  
Matching Pursuit ◽  
Simulated Data ◽  
Orthogonal Matching Pursuit ◽  
Multiscale Entropy ◽  
Automatic Identification ◽  
Useful Signal ◽  
Reconstructed Signal

AbstractMagnetotelluric (MT) data processing can increase the reliability of measured data. Traditional MT data denoising methods are usually applied to entire MT time-series, which results in the loss of useful MT signals and a decrease of imaging accuracy of electromagnetic inversion. However, targeted MT noise separation can retain part of the signal unaffected by strong noise and enhance the quality of MT responses. Thus, we propose a novel method for MT noise separation that uses the refined composite multiscale dispersion entropy (RCMDE) and the orthogonal matching pursuit (OMP) algorithm. First, the RCMDE is extracted from each segment of the MT data. Then, the RCMDEs for each segment are input to the fuzzy c-mean (FCM) clustering algorithm for automatic identification of the MT signal and noise. Next, the OMP method is utilized to remove the identified noise segments independently. Finally, the reconstructed signal consists of the denoised signal segments and the identified useful signal segments. We conducted simulation experiments and algorithm evaluations on electromagnetic transfer function (EMTF) data, simulated data and measured sites. The results indicate that the RCMDE can improve the stability of multiscale dispersion entropy (MDE) and multiscale entropy (ME) by analyzing the characteristics of the signal samples library, effectively distinguishing MT signals and noise. Compared with the existing technique of denoising entire time series, the proposed method uses the RCMDE as characteristic parameter and uses the OMP algorithm for noise separation, simplifies the multi-feature fusion, and improves the accuracy of signal-noise identification. Moreover, the denoising efficiency is accelerated, and the MT response in the low-frequency band is greatly improved.

scholarly journals Magnetotelluric Signal-Noise Identification and Separation Based on ApEn-MSE and StOMP

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 197
Author(s):  
Jin Li ◽  
Jin Cai ◽  
Yiqun Peng ◽  
Xian Zhang ◽  
Cong Zhou ◽  
...  
Keyword(s):  
Noise Suppression ◽  
Matching Pursuit ◽  
Low Frequency ◽  
Orthogonal Matching Pursuit ◽  
Multiscale Entropy ◽  
Magnetotelluric Data ◽  
Signal Noise ◽  
Structure Information ◽  
Noise Identification ◽  
Stagewise Orthogonal Matching Pursuit

Natural magnetotelluric signals are extremely weak and susceptible to various types of noise pollution. To obtain more useful magnetotelluric data for further analysis and research, effective signal-noise identification and separation is critical. To this end, we propose a novel method of magnetotelluric signal-noise identification and separation based on ApEn-MSE and Stagewise orthogonal matching pursuit (StOMP). Parameters with good irregularity metrics are introduced: Approximate entropy (ApEn) and multiscale entropy (MSE), in combination with k-means clustering, can be used to accurately identify the data segments that are disturbed by noise. Stagewise orthogonal matching pursuit (StOMP) is used for noise suppression only in data segments identified as containing strong interference. Finally, we reconstructed the signal. The results show that the proposed method can better preserve the low-frequency slow-change information of the magnetotelluric signal compared with just using StOMP, thus avoiding the loss of useful information due to over-processing, while producing a smoother and more continuous apparent resistivity curve. Moreover, the results more accurately reflect the inherent electrical structure information of the measured site itself.

scholarly journals Magnetotelluric Signal-Noise Separation Using IE-LZC and MP

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1190
Author(s):  
Xian Zhang ◽  
Diquan Li ◽  
Jin Li ◽  
Yong Li ◽  
Jialin Wang ◽  
...  
Keyword(s):  
Apparent Resistivity ◽  
Matching Pursuit ◽  
Low Frequency ◽  
Reference Method ◽  
Test Site ◽  
Phase Curve ◽  
Characteristic Parameters ◽  
Geological Interpretation ◽  
Useful Signal ◽  
Fcm Clustering

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.

scholarly journals SIGNAL-NOISE IDENTIFICATION OF MAGNETOTELLURIC SIGNALS USING FRACTAL-ENTROPY AND CLUSTERING ALGORITHM FOR TARGETED DE-NOISING

Fractals ◽  
2018 ◽  
Vol 26 (02) ◽  
pp. 1840011 ◽  
Author(s):  
JIN LI ◽  
XIAN ZHANG ◽  
JINZHE GONG ◽  
JINGTIAN TANG ◽  
ZHENGYONG REN ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Low Frequency ◽  
Approximate Entropy ◽  
Phase Curve ◽  
Box Dimension ◽  
Characteristic Parameters ◽  
Signal Noise ◽  
Structure Information ◽  
Strong Interference ◽  
Noise Identification

A new technique is proposed for signal-noise identification and targeted de-noising of Magnetotelluric (MT) signals. This method is based on fractal-entropy and clustering algorithm, which automatically identifies signal sections corrupted by common interference (square, triangle and pulse waves), enabling targeted de-noising and preventing the loss of useful information in filtering. To implement the technique, four characteristic parameters — fractal box dimension (FBD), higuchi fractal dimension (HFD), fuzzy entropy (FuEn) and approximate entropy (ApEn) — are extracted from MT time-series. The fuzzy c-means (FCM) clustering technique is used to analyze the characteristic parameters and automatically distinguish signals with strong interference from the rest. The wavelet threshold (WT) de-noising method is used only to suppress the identified strong interference in selected signal sections. The technique is validated through signal samples with known interference, before being applied to a set of field measured MT/Audio Magnetotelluric (AMT) data. Compared with the conventional de-noising strategy that blindly applies the filter to the overall dataset, the proposed method can automatically identify and purposefully suppress the intermittent interference in the MT/AMT signal. The resulted apparent resistivity-phase curve is more continuous and smooth, and the slow-change trend in the low-frequency range is more precisely reserved. Moreover, the characteristic of the target-filtered MT/AMT signal is close to the essential characteristic of the natural field, and the result more accurately reflects the inherent electrical structure information of the measured site.

AUDIO MAGNETOTELLURIC SIGNAL-NOISE IDENTIFICATION AND SEPARATION BASED ON MULTIFRACTAL SPECTRUM AND MATCHING PURSUIT

Fractals ◽  
2019 ◽  
Vol 27 (01) ◽  
pp. 1940007 ◽  
Author(s):  
JIN LI ◽  
XIAN ZHANG ◽  
JINGTIAN TANG ◽  
JIN CAI ◽  
XIAOQIONG LIU
Keyword(s):  
Support Vector Machine ◽  
Time Series Data ◽  
Matching Pursuit ◽  
Low Frequency ◽  
Multifractal Spectrum ◽  
Phase Curve ◽  
Series Data ◽  
Support Vector ◽  
Signal Noise ◽  
Noise Identification

To avoid the blindness of the overall de-noising method and retain useful low frequency signals that are not over processed, we proposed a novel audio magnetotelluric (AMT) signal-noise identification and separation method based on multifractal spectrum and matching pursuit. We extracted two sets of multifractal spectrum characteristic from AMT time-series data to analyze the singularity. We used a support vector machine approach to learn the multifractal spectrum characteristics in a sample’s library and generate a model of support vector machine to distinguish between sections with and without interference in the measured AMT data. The matching pursuit algorithm was used to separate only those sections identified as having interference. Experimental results showed that the proposed method can effectively identify interference in the EMTF mathematical model and measured AMT data. Sections without interference were accurately preserved and reconstructed AMT signals were close to the natural electromagnetic field. The resulting apparent resistivity-phase curve is more continuous and smooth, and effectively improves the quality of AMT data. Moreover, the proposed method provides more reliable AMT data for subsequent electromagnetic inversion.

Speech Enhancement on Over-Complete Dictionary and Threshold Orthogonal Matching Pursuit

2014 ◽  
Vol 1044-1045 ◽  
pp. 1463-1468
Author(s):  
Xiao Cui ◽  
Wu Qing Zhang
Keyword(s):  
Discrete Cosine Transform ◽  
Speech Enhancement ◽  
Speech Signal ◽  
Matching Pursuit ◽  
Signal To Noise Ratio ◽  
Orthogonal Matching Pursuit ◽  
Cosine Transform ◽  
Perceptual Evaluation ◽  
Transform Coefficients

In order to suppress the noise, improve equipment's ability to further process information and improve the quality of voice, speech enhancement is often an important part of the speech signal preprocess. Contrastively analyze the characteristic that the clean speech signal coefficients in over-complete discrete cosine dictionary are much sparser than the traditional discrete cosine transform coefficients. Under noisy conditions, by setting the iterative threshold of orthogonal matching pursuit (OMP) algorithm, clean speech can be gotten, thus realize the speech enhancement. Simulation results of the signal waveform and spectrogram enhanced by the proposed algorithm are very similar to the original signal,comparative experiments also indicate that the signal to noise ratio (SNR) and the perceptual evaluation of speech quality (PESQ) score of the processed signal are superior to traditional discrete cosine transform (DCT).

Thermal Light Longitudinal Correlated Imaging with Random Orthogonal Matching Pursuit Algorithm

2018 ◽  
Vol 32 (12) ◽  
pp. 1854030 ◽  
Author(s):  
Lu Gao ◽  
Ke Xiao ◽  
Hanquan Song ◽  
Xiaoman Qi
Keyword(s):  
Imaging System ◽  
Matching Pursuit ◽  
Optical Tomography ◽  
Reference Beam ◽  
Orthogonal Matching Pursuit ◽  
Test Beam ◽  
Tomography Imaging ◽  
Thermal Light ◽  
Longitudinal Distance

A thermal light correlated longitudinal imaging experiment is proposed. The quasi-thermal light beam is split into two beams, a test beam and a reference beam, respectively. The light in the test beam is scattered by two amplitude objects with a specific longitudinal distance between them, while the light of the reference beam travels uninterrupted. At the end of the test and reference beams, two charge-coupled detectors (CCDs) are used to measure the intensity of the optical field. Through intensity correlation measurement the images of the two detected objects can be achieved simultaneously, only if the distance between the objects is less than the longitudinal coherent length. The theoretical analysis shows that the longitudinal coherent length is determined by both the transverse size of the incoherent thermal light source and the length of the optical path. The quality of the correlated images of the two objects is improved greatly by making use of the orthogonal matching pursuit (OMP) and the proposed variant random orthogonal matching pursuit (Random-OMP) algorithms. The experimental results show that the Random-OMP algorithm is more effective than the OMP algorithm for increasing both the visibility and continuity of the images. The experimental scenario can mimic an optical tomography imaging system, and the two objects with longitudinal distance can be taken as the two transverse layers of a three-dimensional object. The proposed Random-OMP algorithm is effective for improving the quality of the tomography image and has potential value in optical tomography imaging technology using incoherent light sources.

Ultrasonic Testing Signal De-Noising Processing Based on Orthogonal Matching Pursuit Algorithm

2013 ◽  
Vol 423-426 ◽  
pp. 2468-2471
Author(s):  
Yong Fang Yu ◽  
Fei Yi ◽  
Huan Xin Cheng ◽  
Li Cheng
Keyword(s):  
Ultrasonic Testing ◽  
Matching Pursuit ◽  
Orthogonal Matching Pursuit ◽  
Algorithm Optimization ◽  
Reconstructed Signal ◽  
Processing Module ◽  
Testing Signal ◽  
Matching Pursuit Algorithm ◽  
Key Indicators ◽  
Ultrasonic Ndt

Ultrasonic NDT signal de-noising effect is the key indicators to determine whether the pipe is defective.Orthogonal matching pursuit algorithm optimization process, the sampling signal and reconstructed signal by the method of least squares.Using Orthogonal Matching Pursuit Algorithm for the sampling signal to optimize and reconstruct. It is successfully to verify the OMP algorithm can recover the signal based on the MATLAB platform.Designed a signal de-noising processing module to help staff to determine pipeline defects conveniently,it achieved de-noising successfully.

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