Abstract
Magnetotelluric (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.
Highlights
Magnetotelluric (MT) sounding is one of the most mature electrical exploration techniques in recent years (Tikhonov 1950; Cagniard 1953) and is mainly used in geoelectrical structure exploration to measure theZhang et al Earth, Planets and Space (2021) 73:76 phase angle in polarization direction
Curve 3 is obtained from the RR method; that is, test2.asc of the electromagnetic transfer function (EMTF) time-series data is used as reference data to suppress noisy data
Due to the high noise energy, which is added to the relevant noise data, there are some frequency jumps in the low-frequency part of ρyx, which does not yield the ideal effect
Summary
Zhang et al Earth, Planets and Space (2021) 73:76 phase angle in polarization direction. For this reason, we hope to obtain a high-quality MT response under strong electromagnetic interference, which can provide technical support for subsequent inversion interpretation (Qi et al 2020; Li et al 2020a). Ritter et al (1998) used indicators such as the transfer function between the magnetic field at the measured sites and reference site to judge the noise of each data segment and remove the noisy data segment, which did not participate in the impedance estimation. The robust impedance estimation method can effectively reduce the dispersion of the apparent resistivity-phase curve and eliminate non-Gaussian noise in the MT data, the robust method is incapable of removing noise caused by the input and cannot eliminate the nearsource interference with strong energy
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