A Global and Multiscale Denoising Method Based on Generative Adversarial Network for DAS VSP Data
Haitao Ma, J. Yu, Yibo Wang, Ning Wu, Yue Li
Abstract
Distributed acoustic sensing (DAS) has been gradually applied to vertical seismic profiling (VSP), where the generated DAS VSP seismic data contains types of complex noise. Therefore, data denoising plays an important role in collecting high-quality geological information. Generative adversarial network(GAN) has been widely used in seismic exploration data denoising these years, but problems such as insufficient optimization objectives, poor signal retention continuity, and insufficient accuracy still remains when processing DAS VSP data. To address these problems, this paper proposes DuGAN, a deep learning network for multi-scale feature extraction and global information discrimination, to better meet the requirements of high-precision in DAS VSP data denoising. Our method takes GAN as the basic architecture and chooses the multi-scale codec network U-net to explore the potential correlation of DAS data at different scales and a more robust feature representation of DAS signals. In addition, DuGAN is more inclined to emphasize the global role of discriminator so that the entire network ensures the integrity of effective signal structure from a global perspective. Also, for more accurate recovery of the DAS reflected signal, we adjust the loss function in adversarial training and tilt the target optimized space towards the discriminator. Experiments on synthetic and field DAS seismic data show that DuGAN has better denoising performance-not only the noise-covered signal can be recovered, but also the overall effective events are better preserved.