De-noising magnetotelluric data based on mathematical morphology and K-SVD dictionary learning

In order to solve the problem that the conventional magnetotelluric (MT) time-domain denoising methods have the limitation of damageing low-frequency signal, a new method based on mathematical morphological filtering (MMF) and K-singular value decomposition (K-SVD) dictionary learning was proposed to suppress strong cultural noise near 1Hz of MT data. First, MMF was used to separate the low-frequency signal and protect it entirely. Second, K-SVD dictionary learning was used to process residual noisy signals. The field data was used to extract noise contours from auto-learning cultural noise features, which could achieve the purpose of removing noise. The method was verified by testing a synthetic data set and then was used to process two measured data sets. The results show that the proposed method can eliminate all kinds of strong cultural noises without losing useful signals and improve the signal-to-noise ratio and data quality. Moreover, the denoising effect of the proposed method is better than the traditional methods such as wavelet transform. © 2021, China Science Publishing & Media Ltd. All right reserved.