石油物探2026，Vol.65Issue(2)：224-233,10.DOI:10.12431/issn.1000-1441.2024.0268
基于三维反褶积理论的Radon域稀疏增强算法
A Radon-domain sparsity enhancement algorithm based on 3D deconvolution
摘要
Abstract
The Radon transform serves as a critical tool for multiple suppression as well as high-precision imaging for seismic primary reflections,while its focusing capability directly affects the outcomes of seismic data processing.Conventional least-squares Radon transforms based on frequency-curvature domain L2-norm constraints tend to exhibit scissor-like trailing artifacts due to the limitation of finite aperture.Sparse Radon transforms have been developed to enhance Radon-domain focusing performance by incorporating sparsity constraints,which nevertheless face the limitation in achieving adequate convergence of energy clusters.To overcome this limitation,we propose a Radon-domain sparsity enhancement algorithm based on 3D deconvolution theory.The proposed algorithm integrates a seismic wavelet and a curvature-direction smoothing function to construct a deconvolution operator,which is then applied to 3D deconvolution of Radon-domain data within an iterative inversion framework using the alternating direction method of multipliers(ADMM).This approach compresses energy in the curvature dimension via the deblurring mechanism grounded in sparse inversion theory,thereby notably improving the concentration and resolution of energy clusters.Synthetic and field data tests demonstrate that,compared with conventional least-squares and time-domain sparse Radon transforms,the proposed deconvolution-based sparsity-enhanced method substantially improves the discriminability of Radon-domain energy clusters,leading to more accurate identification and suppression of multiple reflections.关键词
Radon变换/反褶积/多项式保幅/多次波压制/稀疏反演Key words
Radon transform/deconvolution/polynomial amplitude preservation/multiple suppression/sparse inversion分类
能源科技引用本文复制引用
郭梦欣,陈思远,时伟,王维红..基于三维反褶积理论的Radon域稀疏增强算法[J].石油物探,2026,65(2):224-233,10.基金项目
国家自然科学基金面上项目"物理和数据混合驱动的黏弹性介质纯P波最小二乘逆时偏移方法"(42274171)、国家自然科学基金项目"基于量子压缩感知的地震数据高精度重建算法研究"(42304113)和中国博士后面上资助项目"基于量子加速的地震数据重建方法研究初探"(2023MD744178)、黑龙江省自然科学基金优秀青年项目"基于物理引导神经网络的最小二乘偏移研究"(YQ2023D006)共同资助. This research is financially supported by the National Natural Science Foundation of China(Grant Nos.42274171,42304113),the General Program of China Postdoctoral Science Foundation(Grant No.2023MD744178)and the Heilongjiang Provincial Natural Science Foundation of China(Grant No.YQ2023D006). （42274171）
