Suppressing seismic random noise based on non-subsampled shearlet transform and improved FFDNet - Fan, Hua; Zhang, Yang; Wang, Wenxu; Li, Tao (Frontiers in Earth Science, 2024)
Abstract：Traditional denoising methods often lose details or edges, such as Gaussian filtering. Shearlet transform is a multi-scale geometric analysis tool, which has the advantages of multi-resolution and multi-directivity. Compared with wavelet transform, curvelet transform and contourlet transform, it can retain more edge details while denoising, and the subjective vision and objective evaluation indexes are better than other multi-scale geometric analysis methods. Deep learning has made great progress in the field of denoising, such as U_Net, DnCNN, FFDNet and GAN, and the denoising effect is better than the traditional optimal method BM3D. Therefore, based on non-subsampled shearlet transform (NSST) and improved FFDNet, a random noise suppression network ST-hFFDNet is proposed. It combines the advantages of NSST, huber norm and FFDNet, and has three characteristics: (1) shearlet transform is an effective feature extraction tool, which can obtain the high and low frequency features of the signal at different scales and in different directions, and so the network can learn signal and noise features of different scales and directions; (2) the noise level map can improve the noise reduction performance of different noise levels; (3) Huber norm can reduce the sensitivity of the network to abnormal data and improve the robustness of network. The network training process is as follows: (1) BSD500 datasets is enhanced by flipping, rotating, scaling and cutting; (2) AWGN with noise level σ∈[0,75] is added to the enhanced datasets to obtain the training datasets; (3) NSST multi-scale and multi-direction decomposition is performed on each pair of samples of the training datasets to obtain high and low frequency images of different scales and directions; (4) based on the decomposed high and low frequency images, ST-hFFDNet network is trained by Adam algorithm; (5) all the samples of the training data set are carried out in steps (3) and ( 4), and the trained model is finally obtained. Simulation experiments and real seismic data denoising show that for low noise, the proposed method is slightly better than NSST, DnCNN and FFDNet, and that for high noise, it is superior to NSST, DnCNN and FFDNet.