关键词: 重力数据, 小波变换, 深度学习, 残差连接, 通道注意力

Abstract: In order to reduce the influence of interference noise on gravity measured data and further improve the accuracy of gravity data processing, a gravity data denoising method based on Multilevel Wavelet Residual Network (MWRNet) was proposed, which combined the wavelet transform and neural network to realize the removal of noise components in gravity data. Firstly, the gravity data were decomposed by wavelet transform, and then a neural network was utilized for noise extraction, while the Residual Channel Attention (RCA) module was introduced to enhance the noise extraction ability of the network. The proposed gravity data denoising method was tested using simulated data and measured data, and the experimental results show that the proposed method has better results compared with other gravity data denoising methods. In an experiment with a noise standard deviation of 50, the proposed method has a 21% and 9% improvement in Peak Signal-to-Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM), respectively, compared with traditional denoising algorithms Block Matching 3D (BM3D), and more than 0.4% and 1% improvement compared to the deep learning-based denoising algorithms Denoising Convolutional Neural Network (DnCNN) and Multi-level Wavelet-CNN (MWCNN), respectively.

Key words: gravity data, wavelet transform, deep learning, residual connectivity, channel attention