Audio-Frequency Magnetotellurics Signal Denoising Based on Multilevel Wavelet Convolutional Neural Networks

Abstract

Audio-frequency magnetotellurics (AMT) is one of the commonly used methods in geophysical exploration; however, its signal energy is relatively weak and easily submerged by various cultural noises, making denoising a critical step in AMT data processing. Currently, deep learning-based neural networks have achieved superior denoising performance compared to traditional methods in many fields, but in AMT denoising, the neglect of the sparsity characteristic of cultural noise results in degraded denoising performance. To enable the neural network to consider sparsity features during the denoising process and thereby enhance denoising accuracy, we adopt a convolutional neural network (CNN) as the network backbone and design a multilevel wavelet convolutional neural network (MWCNN) from the perspective of sparse representation. This network improves CNN blocks via shortcut connections and enhances feature transmission efficiency by replacing pooling layers and interpolation with wavelet transforms, thereby enabling the network to account for the sparsity of cultural noise, capture underlying noise spectral information and improve denoising performance. Furthermore, we discuss the influence of various network parameters on denoising performance. Finally, we validate the effectiveness of MWCNN in AMT denoising through comparative experiments on both synthetic and field AMT datasets against wavelet transform, bounded influence remote reference processing, data-driven tight frame, CNN and residual networks. Comprehensive evaluations based on signal-to-noise ratio, wavelet time-frequency spectra, denoised results and residuals, apparent resistivity and phase curves, error analysis, one-dimensional inversion results and Nyquist diagrams confirm the superiority of MWCNN for AMT denoising.