DenoiseNet: Deep Generator and Discriminator Learning Network With Self-Attention Applied to Ocean Data

Abstract

Well-organized seismic signals play a significant role in subsequent seismic data processing. The multi-scale learning of characteristic signals of complex structures by deep convolutional neural networks has obvious benefits in reducing random noise in seismic data. However, deep convolutional neural networks also have shortcomings. It cannot discover effective features in seismic data structures or recover highquality seismic signals just using convolution. Therefore, the paper presents a Generative Adversarial Network (GAN) architecture in conjunction with the U-Net network. To produce the mapping connection between clean seismic signals and noisy seismic data, the U-Net network is employed as the G network of GAN. Incorporating a self-attention mechanism to strengthen the correlation between seismic data, with the goal of improving the network’s reconstruction capacity on the continuity of seismic signals. The intelligent denoising of seismic data enabled by DsGAN enhances labor efficiency when compared to traditional approaches. When compared to the optimal state of current models such as DnCNN, DnGAN, the peak signal-to-noise ratio (PSNR) is enhanced by 1.52db of the DsGAN model, according to experimental data from simulated and actual seismic data. Not only that, the network also has the ability to learn complex unknown noise with strong generalization and robustness.