石油物探2026，Vol.65Issue(2)：207-223,17.DOI:10.12431/issn.1000-1441.2024.0317
基于Shearlet变换的自适应阈值迭代多震源地震数据分离方法及应用
A shearlet transform-based adaptive threshold iteration method for simultaneous-source data separation and its application
摘要
Abstract
Despite its significant advantages over conventional methods in terms of efficiency and cost,the simultaneous-source technique faces the challenge of wavefield aliasing that hinders its application.Effective signals and aliased noises in simultaneous-source data exhibit the differences in coherence on non-common shot gathers.These differences can be characterized by their respective sparsity in the shearlet transform domain.Therefore,this paper proposes an adaptive threshold iteration algorithm based on the shearlet transform for data separation,with the objective of ensuring the quality of subsequent seismic data processing and imaging.The algorithm begins by transforming the pseudo-separated simultaneous-source data on common receiver gathers into the shearlet domain for a sparse representation.Leveraging the distinct distribution characteristics of effective signals and aliased noises in the shearlet domain across different scales,aliased noises are separated by introducing an adaptive Bayesian threshold estimation to shrink the noise-related transformation coefficients.An iterative strategy is incorporated into the algorithm to gradually optimize the separation results.Synthetic and field data tests demonstrate the good performance of the proposed algorithm in simultaneous-source data separation.关键词
多震源地震数据分离/混叠噪声/Shearlet变换/自适应贝叶斯阈值估计Key words
simultaneous-source data separation/aliased noise/Shearlet transform/adaptive Bayesian threshold estimation分类
能源科技引用本文复制引用
郭琛,张闪闪,田坤,步长城,朱莉,秦宁,周炉峰..基于Shearlet变换的自适应阈值迭代多震源地震数据分离方法及应用[J].石油物探,2026,65(2):207-223,17.基金项目
中国石油化工股份有限公司课题"高密度立体地震采集处理关键技术研究"(P25060)、"纵横波地震采集处理试验探索研究"(P24184)和东营市自然科学基金"自适应稳定的黏弹介质逆时偏移成像方法研究"(2023ZR07)共同资助. This research is financially supported by Sinopec Corporation's Projects(Grant Nos.P25060,P24184)and Dongying Natural Science Foundation's Project(Grant No.2023ZR07). （P25060）
