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.