3D seismic data reconstruction based on a fast structure dictionary learning method
LAN Nanying1, ZHANG Fanchang1, ZHANG Yiming2, QIN Guangsheng3, DING Jicai2
1. School of Geoscience, China University of Petroleum(East China), Qingdao, Shandong 266580, China; 2. CNOOC Research Institute Co. Ltd, Beijing 100028, China; 3. SINOPEC Zhongyuan Oilfield, Puyang, Henan 457099, China
Abstract：Currently,the 3D seismic data reconstruction methods based on dictionary learning usually reconstruct the data slice by slice.This strategy neglects the correlation between slices,and doesn't make full use of the continuity constraints in various directions of seismic data.To solve this problem,a 3D joint reconstruction method based on fast structure dictionary learning was proposed.Under the framework of compressive sensing theory,the method uses fast structure dictionary learning algorithm to train the training set in order to generate a 3D adaptive dictionary,and then reconstruct the data with high precision using 3D adaptive dictionary,observation matrix and regularized orthogonal matching pursuit algorithm.The reconstruction results of model data and real data demonstrated that the method can recover the detailed characteristics of seismic data with high precision and good performance on amplitude preservation.
Ma J.Three-dimensional irregular seismic data reconstruction via low-rank matrix completion[J].Geophysics,2013,78(5):V181-V192.
[6]
Chen Y,Zhang D,Jin Z,et al.Simultaneous denoising and reconstruction of 5-D seismic data via damped rank-reduction method[J].Geophysical Journal International,2016,206(3):1695-1717.
[7]
Xu S,Zhang Y,Pham D,et al.Antileakage Fourier transform for seismic data regularization[J].Geophysics,2005,70(4):V87-V95.
Choi H,Baraniuk R.Interpolation and denoising of nonuniformly sampled data using wavelet-domain processing[C].IEEE International Conference on Acoustics,1999,1645-1648.
[10]
Trad D O,Ulrych T J,Sacchi M D.Accurate interpolation with high-resolution time-variant Radon transforms[J].Geophysics,2002,67(2):25-26.
[11]
薛亚茹,王敏,陈小宏.基于SL0的高分辨率Radon变换及数据重建[J].石油地球物理勘探,2018,53(1):1-7.XUE Yaru,WANG Min,CHEN Xiaohong.High re-solution Radon transform based on SL0 and its application in data reconstruction[J].Oil Geophysical Prospecting,2018,53(1):1-7.
[12]
唐欢欢,毛伟建.3D高阶抛物Radon变换地震数据保幅重建[J].地球物理学报.2014,57(9):2918-2927.TANG Huanhuan,MAO Weijian.Amplitude preserved seismic data reconstruction by 3D high-order parabolic Radon transform[J].Chinese Journal of Geophysics,2014,57(9):2918-2927.
[13]
Herrmann F J,Hennenfent G.Non-parametric seismic data recovery with curvelet frames[J].Geophysical Journal of the Royal Astronomical Society,2010,173(1):233-248.
[14]
Zhang H,Chen X H,Zhang L Y.3D simultaneous seismic data reconstruction and noise suppression based on the curvelet transform[J].Applied Geophy-sics,2017,14(1):87-95.
[15]
张华,陈小宏.基于Jitter采样和曲波变换的三维地震数据重建[J].地球物理学报,2013,56(5):1637-1649.ZHANG Hua,CHEN Xiaohong.Seismic data reconstruction based on Jittered sampling and curvelet transform[J].Chinese Journal of Geophysics,2013,56(5):1637-1649.
[16]
王本锋,陈小宏,李景叶,等.POCS联合改进的Jitter采样理论曲波域地震数据重建[J].石油地球物理勘探,2015,50(1):20-28.WANG Benfeng,CHEN Xiaohong,LI Jingye,et al.Seismic data reconstruction based on POCS and improved Jittered sampling in the curvelet domain[J].Oil Geophysical Prospecting,2015,50(1):20-28.
[17]
曹静杰,王尚旭,李文斌.基于一种三维低冗余曲波变换和压缩感知理论的地震数据重建[J].中国石油大学学报(自然科学版),2017,41(5):61-68.CAO Jingjie,WANG Shangxu,LI Wenbin.Seismic reconstruction with 3D low-redundancy curvelet transform and compressed sensing theory[J].Journal of China University of Petroleum(Edition of Natural Science),2017,41(5):61-68.
[18]
刘财,李鹏,刘洋,等.基于Seislet变换的反假频迭代数据插值方法[J].地球物理学报,2013,56(5):1619-1627.LIU Cai,LI Peng,LIU Yang,et al.Iterative data interpolation beyond aliasing using seislet transform[J].Chinese Journal of Geophysics,2013,56(5):1619-1627.
[19]
张凯,张医奎,李振春,等.MCA框架下Shearlet和DCT字典组合地震数据重建[J].石油地球物理勘探,2019,54(5):1005-1013.ZHANG Kai,ZHANG Yikui,LI Zhenchun,et al.Seismic data reconstruction with the discrete cosine transform and Shearlet dictionaries under the morphological component analysis framework[J].Oil Geo-physical Prospecting,2019,54(5):1005-1013.
[20]
Cai J F,Ji H,Shen Z,et al.Data-driven tight frame construction and image denoising[J].Applied & Computational Harmonic Analysis,2014,37(1):89-105.
[21]
Michael E,Michal A.Image denoising via sparse and redundant representations over learned dictionaries[J].IEEE Transactions on Image Processing,2006,15(12):3736-3745.
[22]
张繁昌,兰南英,张珩.基于光滑L1/2正则化理论的地震数据重建[J].中国矿业大学学报,2019, 48(5):1054-1061.ZHANG Fanchang,LAN Nanying,ZHANG Heng.Seismic data reconstruction based on smoothed L1/2 regularization[J].Journal of China University of Mi-ning & Technology,2019,48(5):1054-1061.
[23]
Liang J,Ma J,Zhang X.Seismic data restoration via data-driven tight frame[J].Geophysics,2014,79(3):V65-V74.
[24]
周亚同,王丽莉,蒲青山.压缩感知框架下基于K-奇异值分解字典学习的地震数据重建[J].石油地球物理勘探,2014,49(4):652-660.ZHOU Yatong,WANG Lili,PU Qingshan.Seismic data reconstruction based on K-SVD dictionary lear-ning under compressive sensing framework[J].Oil Geophysical Prospecting,2014,49(4):652-660.
[25]
李慧,韩立国,张良,等.基于字典学习和ADMM的地震数据重建[J].石油物探,2019,58(3):419-426.LI Hui,HAN Liguo,ZHANG Liang,et al.Seismicdata reconstruction using dictionary learning and thealternating direction method of multipliers[J].Geophysical Prospecting for Petroleum,2019,58(3):419-426.
[26]
Li S,Fang L,Yin H.An efficient dictionary learning algorithm and its application to 3-D medical image denoising[J].IEEE Transactions on Biomedical Engineering,2012,59(2):417-427.
[27]
张繁昌,李传辉.基于正交时频原子的地震信号快速匹配追踪[J].地球物理学报,2012,55(1):277-283.ZHANG Fanchang,LI Chuanhui.Orthogonal time-frequency atom based fast matching pursuit for seismic signal[J].Chinese Journal of Geophysics,2012,55(1):277-283.
[28]
Bezdek J C,Hathaway R J.Convergence of alternating optimization[J].Neural Parallel & Scientific Computations,2003,11(4):351-368.
[29]
Maggioni M,Katkovnik V,Egiazarian K,et al.Nonlocal transform-domain filter for volumetric data denoising and reconstruction[J].IEEE Transactions on Image Processing,2012,22(1):119-133.
[30]
徐平,肖冲,张竞成,等.基于分组三维离散余弦变换字典的植物高光谱数据去噪方法[J].光学学报,2017,37(6):321-330.XU Ping,XIAO Chong,ZHANG Jingcheng,et al.Denoising method for plant hyperspectral data based on grouped 3D discrete cosine transform dictionary[J].Acta Optica Sinica,2017,37(6):321-330.
