A Novel Decomposition-Enhanced Denoising Method for Magnetotelluric Data Based on AMSE-REWT in the Time–Frequency Domain

Abstract

Magnetotelluric (MT) natural signals are characterized by randomness, nonstationarity, and nonlinearity. At low frequencies, long-duration noise frequently reduces the signal-to-noise ratio (SNR). Especially around the dead band below 1 Hz, the data quality is poor due to the low energy of the natural MT field. This study presents a novel approach using adaptive multiscale sample entropy (AMSE) to identify noisy segments, mainly targeting highly predictable noise types such as square wave, impulse, and triangular-wave interference in low frequency. The primary method employs a robust empirical wavelet transform (REWT) for effective noise suppression. To enhance time–frequency resolution and improve the constraints of direct spectral segmentation in traditional empirical wavelet transform (EWT), the short-time Fourier transform (STFT) is applied to REWT components for enhanced signal-to-noise separation. In addition, Gaussian white noise is introduced to mitigate MT noise effects further. Results show that AMSE effectively identifies noisy segments, and the proposed REWT method successfully retains valuable low-frequency information while significantly suppressing square wave, triangular wave, and impulse noise. Field data show that this method enhances the quality of MT responses, resulting in smoother, more continuous apparent resistivity-phase curves with reduced errors, which improves the accuracy of inversion interpretation and provides a reliable dataset for subsequent calculation of inversion profiles.