海洋地质前沿2025，Vol.41Issue(10)：28-37,10.DOI:10.16028/j.1009-2722.2024.195
基于深度残差U-Net网络的海上地震混采数据分离技术研究
Separation of ocean blended data based on deep learning residual U-Net network
摘要
Abstract
With the gradual increase of spatial sampling density of seismic data,blended source acquisition has gradually become one of the effective means to improve the acquisition efficiency,and the effective deblending of blended seismic data is an important part of seismic data processing.We proposed a smart deblending technology for marine dual-source alternating excitation blended data based on the residual U-Net network.The method first converts the blended data from the common shot gather to the common receiver gather to reduce the correlation of the non-primary source excitation signals,and then achieves the intelligent deblending of dual-source blended data based on the residual U-Net network.Compared to traditional U-Net network,our new network model increased the network depth and introduced convolutional residual modules during the downsampling process,which effect-ively avoided the problems of gradient disappearance and gradient explosion,enhanced the feature extraction cap-abilities especially in the processing of detail issues,and better protected the valid information.Through model calculations and actual data processing,the good performance of the network in marine data deblending was veri-fied.The experimental results show that the residual U-Net network could effectively deblend data without losing valid signals and significantly improve the signal-to-noise ratio of the deblending results.The research results provide a new idea for high-precision deblending of marine seismic data and lay the foundation for subsequent seismic data processing.关键词
混采分离/深度学习/残差U-Net网络/分离精度Key words
deblending/deep learning/residual U-Net network/deblending accuracy分类
海洋科学引用本文复制引用
梁兵,郭廷超,许冲,鲍伟,潘成磊..基于深度残差U-Net网络的海上地震混采数据分离技术研究[J].海洋地质前沿,2025,41(10):28-37,10.基金项目
中国石化科技部项目(P22162) （P22162）
