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 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 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.

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.

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