Traditional magnetotelluric signal processing usually uses time–frequency transformation method. Wavelet is also a time–frequency transformation method that used to suppress the magnetotelluric noise. However, the selection of the threshold is very significant, and the unsuitable threshold will lead to excessive distortion of the reconstructed signal. Thus, we propose a method for magnetotelluric noise suppression using grey wolf optimized wavelet threshold. First, the magnetotelluric signal is decomposed by wavelet with appropriate wavelet basis and decomposition layers. The generalized cross‐validation criterion is used as the fitness function of grey wolf optimizer algorithm, which optimizes the threshold of each decomposition layer. Then, the detail coefficients of each layer and the maximum layer of the approximation coefficients are used in the optimized threshold. Next, the inverse wavelet transform is performed. Finally, the noise contour is obtained through iteratively searching for the optimal threshold, and the useful magnetotelluric signal is reconstructed. Simulation experiments and measured magnetotelluric data processing show that the large‐scale interference can effectively be suppressed, and the reconstructed magnetotelluric signal retains the more abundant low‐frequency of useful information. Compared with the remote reference method, fixed threshold method and Birge–Massart layered threshold method, the proposed method realizes the wavelet denoising with adaptive threshold selection in the magnetotelluric noise suppression. The results obtained show smoother and more continuous apparent resistivity–phase curves, which verifies the effectiveness of the optimization.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.