一种联合SBL和DTW的叠前道集剩余时差校正方法

doi:10.12108/yxyqc.20190310

岩性油气藏 ›› 2019, Vol. 31 ›› Issue (3): 86–94.doi: 10.12108/yxyqc.20190310

一种联合SBL和DTW的叠前道集剩余时差校正方法

石战战^1,2, 夏艳晴¹, 周怀来², 王元君²

1. 成都理工大学工程技术学院, 四川乐山 614000;
2. 成都理工大学地球物理学院, 成都 610059

收稿日期:2018-10-22 修回日期:2018-12-08 出版日期:2019-05-21 发布日期:2019-05-06
第一作者:石战战(1985-),男,成都理工大学在读博士研究生,讲师,研究方向为储层预测。地址:(614000)四川省乐山市市中区肖坝路222号成都理工大学工程技术学院资源勘查与土木工程系。Email:shizhanzh@163.com。
基金资助:
国家重大科技专项课题子课题“双极子匹配追踪反演技术研究”（编号：2016ZX05026-001-005）和四川省教育厅项目“基于时频域波形分类的礁滩储层预测方法研究”（编号：16ZB0410）联合资助

Residual moveout correction of prestack seismic gathers based on SBL and DTW

SHI Zhanzhan^1,2, XIA Yanqing¹, ZHOU Huailai², WANG Yuanjun²

1. The Engineering & Technical College of Chengdu University of Technology, Leshan 614000, Sichuan, China;
2. College of Geophysics, Chengdu University of Technology, Chengdu 610059, China

Received:2018-10-22 Revised:2018-12-08 Online:2019-05-21 Published:2019-05-06

摘要/Abstract

摘要： 基于动态时间规整的叠前道集剩余时差校正方法存在动态时间规整算法对噪声敏感，准确计算规整路径困难；算法采用逐点搬家法，直接对地震道作剩余时差校正容易引起地震波形畸变的问题。提出一种联合稀疏贝叶斯学习（Sparse Bayesian Learning，SBL）和动态时间规整（Dynamic Time Warping，DTW）的叠前道集剩余时差校正方法，采用SBL对地震道集进行稀疏表示，再利用DTW对稀疏表示结果进行剩余时差校正，处理后重构地震记录。结果表明，SBL具有良好的噪声鲁棒性，较少的局部最小值，以及全局最优解同时也是最稀疏解，稀疏分解后得到地下地层单位冲击响应，消除了子波影响，再进行时差校正就能避免波形畸变，同时实现了高保真剩余时差校正和随机噪声压制。数值模拟和实际资料处理结果表明该方法具有良好的应用效果。

关键词: 叠前道集, 剩余时差, 稀疏表示, 稀疏贝叶斯学习, 动态时间规整

Abstract: The residual moveout correction method based on dynamic time warping is faced with two problems:the dynamic time warping algorithm is sensitive to noise, and it is difficult to calculate the warping path accurately; the algorithm adopts a point-by-point moving method, which corrects residual moveout of seismic trace directly, and may cause seismic waveform distortion. Aiming at these problems, a residual moveout correction method of prestack gather was proposed based on sparse Bayesian learning (SBL) and dynamic time warping (DTW). The implementation steps are as follows:sparse representation of seismic gathers was realized via sparse Bayesian learning, and then the residual moveout correction of the sparse representation results was conducted by dynamic time warping, and the seismic records were reconstructed after processing. This method utilizes sparse Bayesian learning with good noise robustness and few local minimums. The global optimal solution is also the sparsest one. After sparse decomposition, the unit impulse response of subsurface was obtained, and the wavelet effect was eliminated, and then the distortion of waveform caused by using dynamic time directly can be avoided. The highlight of this method is that high fidelity residual moveout correction and random noise suppression are simultaneously achieved. Numerical simulation and actual data processing results show that the proposed method has a good application effect.

Key words: prestack gather, residual moveout, sparse representation, sparse Bayesian learning, dynamic time warping

中图分类号:

P631.4

石战战, 夏艳晴, 周怀来, 王元君. 一种联合SBL和DTW的叠前道集剩余时差校正方法[J]. 岩性油气藏, 2019, 31(3): 86–94.

SHI Zhanzhan, XIA Yanqing, ZHOU Huailai, WANG Yuanjun. Residual moveout correction of prestack seismic gathers based on SBL and DTW[J]. Lithologic Reservoirs, 2019, 31(3): 86–94.

参考文献

[1] 刘振峰. 油气地震地质模型述评. 岩性油气藏, 2018, 30(1):19-29. LIU Z F. Review on oil and gas seismogeology models. Lithologic Reservoirs, 2018, 30(1):19-29.
[2] 陈可洋. 逆时成像技术在大庆探区复杂构造成像中的应用. 岩性油气藏, 2017, 29(6):91-100. CHEN K Y. Application of reverse-time migration technology to complex structural imaging in Daqing exploration area. Lithologic Reservoirs, 2017, 29(6):91-100.
[3] 曲寿利. AVO分析中的剩余时差校正. 石油地球物理勘探, 1991, 26(4):523-528. QU S L. Residual moveout correction in AVO analysis. Oil Geophysical Prospecting, 1991, 26(4):523-528.
[4] 周鹏, 刘志斌, 张益明, 等.动校剩余时差处理方法及应用. 地球物理学进展, 2015, 30(5):2349-2353. ZHOU P, LIU Z B, ZHANG Y M, et al. The processing method and application of the residual moveout NMO. Progress in Geophysics, 2015, 30(5):2349-2353.
[5] 石战战, 唐湘蓉, 庞溯, 等. 一种基于SC-DTW的叠前道集剩余时差校正方法. 岩性油气藏, 2017, 29(5):113-119. SHI Z Z, TANG X R, PANG S, et al. Prestack gather residual moveout correction based on shape context and dynamic time warping. Lithologic Reservoirs, 2017, 29(5):113-119.
[6] TIPPING M E. Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 2001, 1(3):211-244.
[7] MALLAT S G, ZHANG Z. Matching pursuits with time-frequency dictionaries. IEEE Trans on Signal Processing, 1993, 41(12):3397-3415.
[8] 陈胜, 欧阳永林, 曾庆才, 等.匹配追踪子波分解重构技术在气层检测中的应用. 岩性油气藏, 2014, 26(6):111-114. CHEN S, OUYANG Y L, ZENG Q C, et al. Application of matching pursuit wavelet decomposition and reconstruction technique to reservoir prediction and gas detection. Lithologic Reservoirs, 2014, 26(6):111-114.
[9] CHEN S S, DONOHO D L, SAUNDERS M A. Atomic decomposition by basis pursuit. Siam Review, 2001, 43(1):129-159.
[10] WIPF D P, RAO B D. Sparse Bayesian learning for basis selection. IEEE Transactions on Signal Processing, 2004, 52(8):2153-2164.
[11] WIPF D P, RAO B D. An empirical Bayesian strategy for solving the simultaneous sparse approximation problem. IEEE Transactions on Signal Processing, 2007, 55(7):3704-3716.
[12] ZHANG Z, RAO B D. Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning. IEEE Journal of Selected Topics in Signal Processing, 2011, 5(5):912-926.
[13] ZHANG Z, RAO B D. Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation. IEEE Transactions on Signal Processing, 2013, 61(8):2009-2015.
[14] ZHANG Z, RAO B D. Exploiting correlation in sparse signal recovery problems:Multiple measurement vectors, block sparsity, and time-varying sparsity. ICML 2011 Workshop on Structured Sparsity:Learning and Inference. Bellevue:International Machine Learning Society, 2011.
[15] RAO B D, ZHANG Z, JIN Y. Sparse signal recovery in the presence of intra-vector and inter-vector correlation. International Conference on Signal Processing and Communications. Bangalore:Indian Institute of Science, 2012.
[16] YI B K, JAGADISH H V, FALOUTSOS C. Efficient retrieval of similar time sequences under time warping. Fourteenth International Conference on Data Engineering. Orlando:IEEE Computer Society, 1998.
[17] KIM S W, PARK S, CHU W W. An index-based approach for similarity search supporting time warping in large sequence databases. Seventeenth International Conference on Data Engineering. Heidelberg:IEEE Computer Society, 2001.
[18] KEOGH E, RATANAMAHATANA C A. Exact indexing of dynamic time warping. Knowledge & Information Systems, 2005, 7(3):358-386.
[19] LEMIRE D. Faster retrieval with a two-pass dynamic-timewarping lower bound. Pattern Recognition, 2009, 42(9):2169-2180.
[20] AL-NAYMAT G, CHAWLA S, TAHERI J. Sparse DTW:a novel approach to speed up dynamic time warping. Eighth Australasian Data Mining Conference. Melbourne:Australian Computer Society, 2009.
[21] COMPTON S, HALE D. Estimating vp/vs ratios using smooth dynamic image warping. Geophysics, 2014, 79(6):1639-1643.
[22] CUI T. Improving seismic-to-well ties. Calgary:University of Calgary, 2015.
[23] ZHANG R, CASTAGNA J. Seismic sparse-layer reflectivity inversion using basis pursuit decomposition. Geophysics, 2011, 76(6):R147-R158.

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一种联合SBL和DTW的叠前道集剩余时差校正方法

Residual moveout correction of prestack seismic gathers based on SBL and DTW

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