石油地球物理勘探2024，Vol.59Issue(4)：714-723,10.DOI:10.13810/j.cnki.issn.1000-7210.2024.04.008
应用生成对抗网络的地震数据重建和去噪一体化方法
An integrated m ethod of seism ic data reconstruction and denoising based on generative adversarial network
摘要
Abstract
During the actual acquisition process,due to terrain conditions and human factors,seismic data can suffer from spatial under sampling or irregular sampling,as well as being contaminated by random noise,which hinders subsequent processing and interpretation.Current seismic data processing methods typically separate re-construction and denoising into two stages,often introducing additional errors.The focus of the integrated re-construction and denoising method is to accurately extract the effective features of seismic data under mixed in-terference from missing traces and noise.This paper proposes an integrated method for seismic data reconstruc-tion and denoising based on conditional Wasserstein generative adversarial network(cWGAN).Firstly,a ge-nerator model is constructed with the U-Net model as the basic network structure,and the event features of seis-mic data are extracted.Conditional constraints are then introduced into the discriminator model to guide the gra-dient optimization direction of the generator.Secondly,an error description model for reconstruction and de-noising is established,and an integrated loss function is designed to address both tasks simultaneously.Finally,tests on synthetic and actual data demonstrate that the seismic data recovered by the proposed network model have a higher signal-to-noise ratio and good robustness.关键词
地震数据处理/重建与去噪一体化/深度学习/生成对抗网络/一体化损失函数Key words
seismic data processing/integrated method of reconstruction and denoising/deep learning,generative adversarial network/integrated loss function分类
天文与地球科学引用本文复制引用
张岩,张一鸣,董宏丽,宋利伟..应用生成对抗网络的地震数据重建和去噪一体化方法[J].石油地球物理勘探,2024,59(4):714-723,10.基金项目
本项研究受东北石油大学特色科研团队项目"智慧油田信息处理创新团队"(2023TSTD-04)资助. （2023TSTD-04）
