A Divide-and-Conquer Strategy for Magnetotelluric Noise Suppression: An Adaptive Wavelet Denoising Method Guided by a ResNet Multi-Classification Model

The magnetotelluric (MT) signals are susceptible to various types of noise during acquisition, such as square waves, triangular waves, and pulse noise. Consequently, the effective signal is overwhelmed, leading to a significant degradation in the signal-to-noise ratio. Traditional wavelet threshold (WT) denoising can separate signals from noise through multi-scale analysis. However, its performance depends heavily on the selection of wavelet basis functions, decomposition levels, threshold functions, and threshold selection methods, which leads to insufficient adaptability in complex noise environments, causing issues such as signal distortion or residual noise. To address the above challenges, this paper proposes a divide-and-conquer strategy based on a ResNet-guided adaptive wavelet denoising method. The method first constructs a synthetic dataset containing multiple types of typical noise, and then establishes the optimal wavelet parameter combinations for different noise categories through systematic experiments. Subsequently, a residual neural network (ResNet) is trained on this dataset to construct a multi-class classification model capable of classifying the dominant noise type in each data window. In practical applications, the ResNet model is employed to recognize the dominant noise type in MT data, and based on the recognition results, the optimal wavelet parameter combinations are adaptively invoked for processing, thereby achieving targeted, category-specific denoising. Experimental results on simulated data and field data from two sites demonstrate that the proposed method effectively preserves the features of the desired signal under strong noise backgrounds and improves MT data quality.