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

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