吉林大学学报(地球科学版) ›› 2020, Vol. 50 ›› Issue (3): 895-904.doi: 10.13278/j.cnki.jjuese.20190147

• 地球探测与信息技术 • 上一篇    

基于三维稀疏反演的混合震源数据分离与一次波估计

王铁兴, 王德利, 孙婧, 胡斌, 刘思秀   

  1. 吉林大学地球探测科学与技术学院, 长春 130026
  • 收稿日期:2019-07-25 发布日期:2020-05-29
  • 作者简介:王铁兴(1992-),男,博士研究生,主要从事多次波压制方法研究,E-mail:845805051@qq.com
  • 基金资助:
    国家科技重大专项项目(2016ZX05026-002-003);国家自然科学基金项目(41374108)

Separation and Primary Estimation of Blended Data by 3D Sparse Inversion

Wang Tiexing, Wang Deli, Sun Jing, Hu bin, Liu Sixiu   

  1. College of GeoExploration Sicence and Technology, Jilin University, Changchun 130026, China
  • Received:2019-07-25 Published:2020-05-29
  • Supported by:
    Supported by National Nature Science Foundation of China(2016ZX05026-002-003)and National Natural Science Foundation of China(41374108)

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
[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.
[1] 邵广周, 赵凯鹏, 吴华. 基于MPI的面波有限差分正演模拟[J]. 吉林大学学报(地球科学版), 2020, 50(1): 294-303.
[2] 雷东宁, 乔岳强, 胡庆, 王秋良, 林松, 李雪. 丹江断裂东段第四纪活动性及地震地质涵义[J]. 吉林大学学报(地球科学版), 2019, 49(5): 1362-1375.
[3] 罗腾, 冯晅, 郭智奇, 刘财, 刘喜武. 基于模拟退火粒子群优化算法的裂缝型储层各向异性参数地震反演[J]. 吉林大学学报(地球科学版), 2019, 49(5): 1466-1476.
[4] 代丽艳, 董宏丽, 李学贵. 微地震数据去噪方法综述[J]. 吉林大学学报(地球科学版), 2019, 49(4): 1145-1159.
[5] 肖汉, 王德利. 基于快速匹配法的VTI介质走时计算[J]. 吉林大学学报(地球科学版), 2019, 49(4): 1160-1168.
[6] 孙章庆, 汪登科, 韩复兴. 复杂海底各种地震波的射线追踪与运动学特征[J]. 吉林大学学报(地球科学版), 2019, 49(4): 1169-1181.
[7] 薛林福, 祝铭, 李文庆, 刘文玉, 刘正宏, 刘泽宇. 岩浆泡破裂引发地震的模式——以吉林松原2013年地震群为例[J]. 吉林大学学报(地球科学版), 2018, 48(6): 1865-1875.
[8] 林松, 李媛, 程邈, 邓小虎, 王薇. 嘉鱼断裂西向延伸与第四系活动特征[J]. 吉林大学学报(地球科学版), 2018, 48(5): 1501-1511.
[9] 刘财, 裴思嘉, 郭智奇, 符伟, 张宇生, 刘喜武. 非均匀介质地震AVO响应模拟及分析[J]. 吉林大学学报(地球科学版), 2018, 48(5): 1512-1521.
[10] 邓馨卉, 刘财, 郭智奇, 刘喜武, 刘宇巍. 济阳坳陷罗家地区各向异性页岩储层全波场地震响应模拟及分析[J]. 吉林大学学报(地球科学版), 2018, 48(4): 1231-1243.
[11] 张冰, 郭智奇, 徐聪, 刘财, 刘喜武, 刘宇巍. 基于岩石物理模型的页岩储层裂缝属性及各向异性参数反演[J]. 吉林大学学报(地球科学版), 2018, 48(4): 1244-1252.
[12] 叶云飞, 孙建国, 张益明, 熊凯. 基于立体层析反演的低频模型构建在深水区储层反演中的应用:以南海深水W构造为例[J]. 吉林大学学报(地球科学版), 2018, 48(4): 1253-1259.
[13] 刘一, 刘财, 刘洋, 勾福岩, 李炳秀. 复杂地震波场的自适应流预测插值方法[J]. 吉林大学学报(地球科学版), 2018, 48(4): 1260-1267.
[14] 刘明忱, 孙建国, 韩复兴, 孙章庆, 孙辉, 刘志强. 基于自适应加权广义逆矢量方向滤波估计地震同相轴倾角[J]. 吉林大学学报(地球科学版), 2018, 48(3): 881-889.
[15] 孙建国, 苗贺. 基于Chebyshev走时逼近的三维多次反射射线计算[J]. 吉林大学学报(地球科学版), 2018, 48(3): 890-899.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!