石油物探2024，Vol.63Issue(1)：104-115,128,13.DOI:10.12431/issn.1000-1441.2024.63.01.009
生成式对抗神经网络的改进及其在地震数据压噪中的应用
Seismic data denoising based on improved generative adversarial network
摘要
Abstract
Random noises in seismic data will deteriorate data quality and have a negative impact on interpretation.In random noise reduction,it is difficult to restore effective information in seismic data using a conventional generative adversarial network.Based on the U-net network,we develop a modified generative adversarial network with optimized batch normalization and pooling layers to improve effective information restoration.A multi-scale discriminator network is established to improve the performance of the network model.A set of multi-module loss functions are formulated with feature matching loss and structural information loss.Ow-ing to the new network structure,it is unnecessary to estimate noises in advance,and thus end-to-end blind denoising could be a-chieved.The model also features improved ability of generalization and data restoration.Field data tests in the northern South China Sea show improved performance of noise reduction and signal preservation compared with other denoising algorithms,leading to better imaging of boundaries.The improved generative adversarial network is a good method for seismic data denoising and could be applied to seismic data processing in additional prospects.关键词
生成式对抗神经网络/U-net神经网络/地震数据去噪/泛化能力/数据细节Key words
generative adversarial network/U-net network/seismic data denoising/generalization ability/data detail分类
地质学引用本文复制引用
彭海龙,李明,孙文钊,李列,周凡,鲁统祥,江凡..生成式对抗神经网络的改进及其在地震数据压噪中的应用[J].石油物探,2024,63(1):104-115,128,13.基金项目
中海油"十四五"重大专项课题(KJGG2022-0302)资助.This research is financially supported by the Major Project of CNOOC during 14th Five Year Plan Period(Grant No.KJGG2022-0302). （KJGG2022-0302）
