Low-Frequency Magnetotelluric Data Denoising Using Improved Denoising Convolutional Neural Network and Gated Recurrent Unit

The magnetotelluric (MT) signals are susceptible to anthropogenic noise and the existing denoising methods have significant shortcomings in low-frequency situations. To address the problem, we propose an innovative denoising approach. It is different from the existing methods that attempt to achieve signal-noise separation through one step. The denoising process is divided into two steps in the proposed approach. The effective low-frequency dominant component and high-frequency component are sequentially extracted through deep learning and dictionary learning. We propose a new deep learning network named DnCNN-GRU which combines the powerful feature extraction capability of Denoising Convolutional Neural Network (DnCNN) and the strong temporal sequence processing ability of Gated Recurrent Unit (GRU), enabling accurate extraction of the low-frequency MT signal. Furthermore, we integrate this network with the K-Singular Value Decomposition (KSVD) dictionary learning to achieve accurately extraction of effective high-frequency components. Tests of synthetic data indicate that our method is the best compared to a series of state-of-the-art (SOTA) algorithms. It is the only method that can completely remove various types and scales of cultural noises while brilliantly preserves both the low and high-frequency signals. In addition, our method is validated on apparent resistivity and phase data and is significantly superior to the commonly used Robust estimation method. These results demonstrate that our method can solve the problem mentioned above and can be a substitute for Robust estimation or remote reference processing.