基于三维稀疏反演的混合震源数据分离与一次波估计

doi:10.13278/j.cnki.jjuese.20190147

摘要/Abstract

摘要： 混合震源采集（下称混采）技术是当前地震勘探的潮流。但是由混采获得的数据中包含相互重叠的由多个震源激发产生的炮记录，会对后续的地震数据处理产生严重干扰。本文针对现有的基于混采数据的稀疏反演一次波估计（EPSI）方法，提出了一种改进的基于三维稀疏反演的混采数据分离与一次波估计方法。我们将混采EPSI方法的地下一次波响应估计过程转化为基于L1范数的双凸优化问题，并用基于L1范数的谱投影梯度（SPGL1）算法进行求解，确保取得全局极值，从而稳定反演过程。此外，我们还用二维曲波变换和一维小波变换组成三维联合稀疏变换对反演过程进行约束，能在确保求解精度的同时较以往的三维曲波稀疏约束大大提高计算速度。将本文方法应用于模拟混采数据和海上实际混采数据，将试算结果与传统混采数据EPSI方法对比，全面验证了本文所述方法的有效性和优越性。

关键词: 混采数据分离, 一次波估计, 三维稀疏反演

Abstract: The blended acquisition of seismic data is widely used in the industry area; however, the seismic data acquired by such a method contain overlapping shot records of multiple sources, which is not conducive to the subsequent seismic data processing. A modified separation and primary estimation method for blended data based on 3D sparse inversion is proposed in this paper. We introduce the L1 norm bi-convex optimization into the solution process of estimating primary impulse responses by conventional EPSI and SPGL1 algorithm to get the global minima, so that the inversion process is stable. Besides, 2D curvelet transform and 1D wavelet transform are combined into a 3D sparse constraint to improve the calculation speed while ensuring the inversion accuracy. Compared to the conventional EPSI for blended data in the standard industry workflow, the effectiveness and superiority of this proposed method is verified in the application in synthetic data and marine field data.

Key words: separation of blended data, primary estimation, 3D sparse inversion

中图分类号:

P631.4

王铁兴, 王德利, 孙婧, 胡斌, 刘思秀. 基于三维稀疏反演的混合震源数据分离与一次波估计[J]. 吉林大学学报(地球科学版), 2020, 50(3): 895-904.

Wang Tiexing, Wang Deli, Sun Jing, Hu bin, Liu Sixiu. Separation and Primary Estimation of Blended Data by 3D Sparse Inversion[J]. Journal of Jilin University(Earth Science Edition), 2020, 50(3): 895-904.

参考文献

[1] Berkhout A J. Changing the Mindset in Seismic Data Acquisition[J]. Leading Edge, 2008, 27(7):924-938.
[2] Tang Y, Biondi B. Least-Squares Migration/Inversion of Blended Data[C]//SEG Technical Program Expanded Abstracts 2009.[S.l.]:Society of Exploration Geophysicists, 2009:2859-2863.
[3] Verschuur D J, Berkhout A J. Seismic Migration of Blended Shot Records with Surface-Related Multiple Scattering[J]. Geophysics, 2011, 76(1):A7-A13.
[4] Anagaw A Y, Sacchi M D. Comparison of Multifrequency Selection Strategies for Simultaneous-Source Full-Waveform Inversion[J]. Geophysics, 2014, 79(5):R165-R181.
[5] Huo S, Luo Y, Kelamis P G. Simultaneous Sources Separation via Multidirectional Vector-Median Filtering[J]. Geophysics, 2012, 77(4):V123-V131.
[6] 张良,韩立国,李宇,等. 基于时间窗边线检测和多级中值滤波的混采数据分离[J]. 地球物理学进展, 2018, 33(6):2512-2521. Zhang Liang, Han Liguo, Li Yu, et al. Separation of Blended Data Based on Time Window Edge Line Detection and Multilevel Median Filter[J]. Progress in Geophysics, 2018, 33(6):2512-2521.
[7] Chen Y. Deblending Using a Space-Varying Median Filter[J]. Exploration Geophysics, 2015, 46(4):332-341.
[8] Mahdad A, Doulgeris P, Blacquiere G. Separation of Blended Data by Iterative Estimation and Subtraction of Blending Interference Noise[J]. Geophysics, 2011, 76(3):Q9-Q17.
[9] Lin T T Y, Herrmann F J. Designing Simultaneous Acquisitions with Compressive Sensing[C]//71st EAGE Conference and Exhibition Incorporating SPE EUROPEC.[S. l.]:EAGE, 2009.
[10] Chen Y, Fomel S, Hu J. Iterative Deblending of Simultaneous-Source Seismic Data Using Seislet-Domain Shaping Regularization[J]. Geophysics, 2014, 79(5):V179-V189.
[11] Cheng J, Sacchi M D. Separation and Reconstruction of Simultaneous Source Data via Iterative Rank Reduction[J]. Geophysics, 2015, 80(4):V57-V66.
[12] Cheng J, Sacchi M D. Fast Dual-Domain Reduced-Rank Algorithm for 3D Deblending via Randomized QR Decomposition[J]. Geophysics, 2016, 81(1):V89-V101.
[13] Zu S, Zhou H, Mao W, et al. Iterative Deblending of Simultaneous-Source Data Using a Coherency-Pass Shaping Operator[J]. Geophysical Journal International, 2017, 211(1):541-557.
[14] Verschuur D J, Berkhout A J, Wapenaar C P A. Adaptive Surface-Related Multiple Elimination[J]. Geophysics, 1992, 57(9):1166-1177.
[15] van Groenestijn G J, Verschuur D J. Estimating Primaries by Sparse Inversion and Application to Near-Offset Data Reconstruction[J]. Geophysics, 2009, 74(3):A23-A28.
[16] van Groenestijn G J A, Verschuur D J. Using Surface Multiples to Estimate Primaries by Sparse Inversion from Blended Data[J]. Geophysical Prospecting, 2011, 59(1):10-23.
[17] Berkhout A J, Blacquière G, Verschuur E. From Simultaneous Shooting to Blended Acquisition[C]//SEG Technical Program Expanded Abstracts 2008.[S. l.]:Society of Exploration Geophysicists, 2008:2831-2838.
[18] Lin T Y, Herrmann F J. Robust Signature Deconvolution and the Estimation of Primaries by Sparse Inversion[J]. Geophysics, 2013, 78(3):133-150.
[19] Hennenfent G, Berg E, Friedlander M P, et al. New Insights into One-Norm Solvers from the Pareto Curve[J]. Geophysics, 2008, 73(4):A23-A26.
[20] van den Berg E, Friedlander M P. Probing the Pareto Frontier for Basis Pursuit Solutions[J]. SIAM Journal on Scientific Computing, 2008, 31(2):890-912.
[21] van den Berg E, Friedlander M P. Sparse Optimization with Least-Squares Constraints[J]. SIAM Journal on Optimization, 2011, 21(4):1201-1229
[22] Feng Fei, Wang Deli, Zhu Heng,et al. Estimating Primaries by Sparse Inversion of the 3D Curvelet Transform and the L1-Norm Constraint[J]. Applied Geophysics, 2013,10(2):201-209.
[23] Wang Tiexing, Wang Deli, Sun Jing, et al. Closed-Loop SRME Based on the 3D L1-Norm Sparse Inversion[J]. Acta Geophysica, 2017, 65(6):1145-1152.
[24] 韩立,韩立国,李翔,等.二阶声波方程频域PML边界条件及频域变网格步长并行计算[J].吉林大学学报(地球科学版), 2011, 41(4):1226-1232. Han Li, Han Liguo, Li Xiang, et al. PML Boundary Conditions for Second-Order Acoustic Wave Equations and Variable Grid Parallel Computation in Frequency-Domain Modeling[J]. Journal of Jilin University (Earth Science Edition), 2011, 41(4):1226-1232.
[25] 田坤,黄建平,李振春,等.实现复频移完全匹配层吸收边界条件的递推卷积方法[J].吉林大学学报(地球科学版), 2013, 43(3):1022-1032. Tian Kun, Huang Jianping, Li Zhenchun, et al.Recursive Convolution Method for Implementing Complex Frequency-Shifted PML Absorbing Boundary Condition[J]. Journal of Jilin University (Earth Science Edition), 2013, 43(3):1022-1032.

相关文章 15

[1]	丁梦颜, 冯晅, 刘财. 裂缝对岩石非线性弹性特征的影响[J]. 吉林大学学报(地球科学版), 2026, 56(3): 1051-1061.
[2]	范佳奇, 吴秋莹, 王典, 李鹏. Radon变换的贪婪-快速迭代收缩阈值算法实现及多次波压制应用 [J]. 吉林大学学报(地球科学版), 2026, 56(2): 684-693.
[3]	赵天硕, 宋超, , 刘财, , , , 徐雨歆. 物理信息神经网络地震走时层析成像程函方程因式分解方法 [J]. 吉林大学学报(地球科学版), 2026, 56(2): 694-702.
[4]	谭晓淼, 周建波, 饶莹, 王海燕, 侯贺晟, 李明芮, 高锐. 中亚造山带中南部索伦缝合带岩石圈结构及其对古亚洲洋演化的制约[J]. 吉林大学学报(地球科学版), 2026, 56(1): 209-218.
[5]	毛子雄, 侯贺晟, 周建波, 符伟. 佳木斯地块—那丹哈达地体近地表速度结构[J]. 吉林大学学报(地球科学版), 2026, 56(1): 219-228.
[6]	韩复兴, 王源, 高正辉, 常志邈, 马飞, 秦昊, 尚浩. 基于NEWUOA的CRS叠加成像技术[J]. 吉林大学学报(地球科学版), 2026, 56(1): 386-396.
[7]	黄兴国, 翁央央, 韩丽. 基于广义递归卷积的孔隙黏弹地震波正演模拟[J]. 吉林大学学报(地球科学版), 2026, 56(1): 377-385.
[8]	韩复兴, 刘水源, 高正辉, 韩江涛, 张涛, 尚浩. 基于机器学习的微动HVSR数据干扰信号压制方法[J]. 吉林大学学报(地球科学版), 2025, 55(6): 2153-2163.
[9]	刘财, 张焱喆, 刘洋. 地震数据非平稳特征分析综述[J]. 吉林大学学报(地球科学版), 2025, 55(6): 2132-2152.
[10]	李丛, 张栋, 董果果, 袁青松, 许军, 朱德胜, 代磊, 李鹏飞, 焦通, 郑玉生, 魏俏巧, 刘家橘. 连续随机离散缝网表征技术在中牟凹陷裂缝发育区中的应用[J]. 吉林大学学报(地球科学版), 2025, 55(5): 1715-1727.
[11]	齐娇, 曹思远. 基于先验知识的深度学习表面相关多次波压制方法[J]. 吉林大学学报(地球科学版), 2025, 55(5): 1702-1714.
[12]	田广, 赵岩. 信噪比约束的可调节振幅补偿算子反Q滤波方法[J]. 吉林大学学报(地球科学版), 2025, 55(4): 1351-1360.
[13]	王志勇, 刘国昌, 王梓旭, 郭严粮, 秦晨. 基于振幅一致性残差卷积编码-解码器的不规则缺失数据重建 [J]. 吉林大学学报(地球科学版), 2025, 55(4): 1336-1350.
[14]	孙敬雯, 吕子强, 孔庆翰, 唐泽豪, 邱俊辉, 刘珈君. 基于背景噪声成像的沂沭断裂带及邻区波速变化[J]. 吉林大学学报(地球科学版), 2025, 55(4): 1361-1371.
[15]	丁乾龙, 沈金松, 陈双全, 冉尚, 龙刚. 频率域传播矩阵法地震AVA多参数反演[J]. 吉林大学学报(地球科学版), 2025, 55(3): 957-969.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed