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.

大地电磁（MT）数据处理可以提高测量数据的可靠性。传统的MT数据去噪方法通常应用于整个MT时间序列，这会导致有用MT信号的丢失并降低电磁反演的成像精度。但是，有针对性的MT噪声分离可以保留不受强噪声影响的部分信号，并提高MT响应的质量。因此，我们提出了一种新的MT噪声分离方法，该方法使用改进的复合多尺度色散熵（RCMDE）和正交匹配追踪（OMP）算法。首先，从MT数据的每个段中提取RCMDE。然后，将每个段的RCMDE输入到模糊c均值（FCM）聚类算法中，以自动识别MT信号和噪声。下一个，OMP方法被用来独立地去除识别出的噪声段。最后，重构的信号由去噪的信号段和识别的有用信号段组成。我们对电磁传递函数（EMTF）数据，模拟数据和测量位置进行了仿真实验和算法评估。结果表明，RCMDE通过分析信号样本库的特征，可以有效区分MT信号和噪声，从而提高多尺度色散熵（MDE）和多尺度熵（ME）的稳定性。与现有的对整个时间序列进行去噪的技术相比，该方法以RCMDE为特征参数，采用OMP算法进行噪声分离，简化了多特征融合，并提高了信号噪声识别的准确性。而且，加速了去噪效率，并且大大改善了低频频带中的MT响应。