西南石油大学学报（自然科学版）2026，Vol.48Issue(3)：39-52,14.DOI:10.11885/j.issn.1674-5086.2025.08.16.01
基于混合优化与改进的U-Net震源分离方法
A Seismic Source Separation Method Based on Hybrid Optimization and Improved U-Net
摘要
Abstract
Traditional single-source seismic exploration has problems of low efficiency and insufficient anti-interference ability.Although multi-source technology improves the exploration efficiency,the data quality deteriorates due to the interference of aliasing noise.For this reason,this paper proposes two optimization methods to solve the source separation problem.Method 1:A dynamic weighted hybrid optimization algorithm(ALFT)is constructed by integrating the FISTA algorithm and the ALBM algorithm.This algorithm improves the convergence speed while ensuring accuracy.By combining the advantages of the filtering method and the inversion method,a process of"initial value pre-judgment-iterative correction"is formed.The experimental results show that,compared with the direct iteration method,this method can increase the signal-to-noise ratio by 10％～25％and reduce the iteration time by 33％.Method 2:A CSA-Unet deep learning network model is proposed.Based on the U-Net network architecture,this model introduces an attention local contrast(ALC)module to enhance the ability to capture the characteristics of effective signals,and combines a local entropy discrete point suppression mechanism to eliminate the interference of auxiliary sources.The validation results demonstrate that CSA-UNet achieves a significantly higher separation signal-to-noise ratio than ALFTa and U-Net on both the simulated dataset(Sigsbee2B)and the real dataset,while also effectively preserving the structure of the formation reflection signals.The methods proposed in this paper provide an efficient and high-precision solution for multi-source seismic exploration and are of great significance in imaging practices under complex geological conditions.关键词
多震源地震勘探/震源混叠噪声/主辅震源分离/U-NetKey words
multi-source seismic exploration/source aliasing noise/separation of primary and auxiliary sources/U-Net分类
天文与地球科学引用本文复制引用
李艳,吕晓雨,刘阳超,张全,彭博,唐书航..基于混合优化与改进的U-Net震源分离方法[J].西南石油大学学报（自然科学版）,2026,48(3):39-52,14.基金项目
中国石油-西南石油大学创新联合体支持交叉学科发展"揭榜挂帅"项目(2024CXJB09) （2024CXJB09）
新型油气勘探开发国家科技重大专项(2025ZD1408800) （2025ZD1408800）
