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Separation of magnetotelluric signals based on refined composite multiscale dispersion entropy and orthogonal matching pursuit
Jin Li
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6606-6967
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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.
Keywords
magnetotelluric (MT), refined composite multiscale dispersion entropy (RCMDE), orthogonal matching pursuit (OMP), noise separation
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CURRENT STATUS: Under Revision
Version 2
Posted 07 Jan, 2021
No community comments so far
Editorial decision: Major Revision
On 18 Jan, 2021
Review #2 received
Received 17 Jan, 2021
Reviewer #2 agreed
On 04 Jan, 2021
Editor assigned
On 22 Dec, 2020
Reviewers invited
Invitations sent on 22 Dec, 2020
Reviewer #1 agreed
On 22 Dec, 2020
Review #1 received
Received 22 Dec, 2020
Submission checks complete
On 22 Dec, 2020
Editor invited
On 22 Dec, 2020
No comments provided
Editorial decision: Major Revision
On 26 Oct, 2020
Review #2 received
Received 25 Oct, 2020
Review #1 received
Received 18 Sep, 2020
Reviewer #2 agreed
On 24 Aug, 2020
Reviewer #1 agreed
On 20 Aug, 2020
Reviewers invited
Invitations sent on 16 Aug, 2020
Editor assigned
On 11 Aug, 2020
Editor invited
On 10 Aug, 2020
Submission checks complete
On 09 Aug, 2020
First submitted
On 06 Aug, 2020
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This preprint is under consideration at Earth, Planets and Space. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Full paper
Separation of magnetotelluric signals based on refined composite multiscale dispersion entropy and orthogonal matching pursuit
Jin Li
Corresponding Author
ORCiD: https://orcid.org/0000-0001-6606-6967
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 2
Posted 07 Jan, 2021
No community comments so far
Editorial decision: Major Revision
On 18 Jan, 2021
Review #2 received
Received 17 Jan, 2021
Reviewer #2 agreed
On 04 Jan, 2021
Editor assigned
On 22 Dec, 2020
Reviewers invited
Invitations sent on 22 Dec, 2020
Reviewer #1 agreed
On 22 Dec, 2020
Review #1 received
Received 22 Dec, 2020
Submission checks complete
On 22 Dec, 2020
Editor invited
On 22 Dec, 2020
No comments provided
Editorial decision: Major Revision
On 26 Oct, 2020
Review #2 received
Received 25 Oct, 2020
Review #1 received
Received 18 Sep, 2020
Reviewer #2 agreed
On 24 Aug, 2020
Reviewer #1 agreed
On 20 Aug, 2020
Reviewers invited
Invitations sent on 16 Aug, 2020
Editor assigned
On 11 Aug, 2020
Editor invited
On 10 Aug, 2020
Submission checks complete
On 09 Aug, 2020
First submitted
On 06 Aug, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Due to technical limitations, full-text HTML conversion of this manuscript could not be completed. However, the manuscript can be downloaded and accessed as a PDF.