Seismic data reconstruction based on segmented random sampling and MCA
WANG De-Ying1(), ZHANG Kai2, LI Zhen-Chun2, ZHANG Yi-Kui3, XU Xin1
1. Research Institute of Petroleum Exploration & Development-Northwest (NWGI),PetroChina,Lanzhou 730030,China 2. School of Geosciences,China University of Petroleum,Qingdao 266580,China 3. Wuhua Energy Technology Co.,Ltd,Xi'an 710067,China
Data reconstruction is a critical preliminary work in the processing of seismic data.Compressed sensing (CS) has been well applied in data reconstruction.The key to CS is random sampling,which converts the mutual coherent alias caused by regular under-sampling into lower-amplitude incoherent noise. But traditional sampling methods lack constraints on sampling points, resulting in excessive noise interference. The segmented random sampling (SRS) method can effectively control the distance between sampling points. Furthermore, a single mathematical transformation will lead to incomplete sparse representation and impact data reconstruction. The morphological component analysis (MCA) can decompose a signal into several components with outstanding morphological features to approximate the complex internal structure of data. A new dictionary combination (Shearlet+DCT) has been found under the MCA framework, and the block coordinate relaxation (BCR) algorithm has been used to get the optimal solution to obtain desired reconstruction results. Tests of real data have proven that the proposed method can produce good effects when used to reconstruct the SRS data.
王德英, 张凯, 李振春, 张医奎, 许鑫. 基于分段采样和MCA的地震数据重建[J]. 物探与化探, 2022, 46(5): 1214-1224.
WANG De-Ying, ZHANG Kai, LI Zhen-Chun, ZHANG Yi-Kui, XU Xin. Seismic data reconstruction based on segmented random sampling and MCA. Geophysical and Geochemical Exploration, 2022, 46(5): 1214-1224.
Zhang H, Chen X H. Seismic data reconstruction based on jittered sampling and curvelet transform[J]. Chinese J. Geophys., 2013, 56(5):1637-1649.
[2]
唐刚. 基于压缩感知和稀疏表示的地震数据重建与去噪[D]. 北京: 清华大学, 2010.
[2]
Tang G. Seismic data reconstruction and denoising based on compressive sensing and sparse representation[D]. Beijing: Tsinghua University, 2010.
[3]
Leneman O. Random sampling of random processes:Impulse response[J]. Information and Control, 1966, 9(2):347-363.
doi: 10.1016/S0019-9958(66)80002-5
[4]
Hennenfent G, Herrmann F J. Simply denoise:Wavefield Reconstruction via jittered undersampling[J]. Geophysics, 2008, 73(3): V19-V28.
doi: 10.1190/1.2841038
[5]
Herrmann F J, Wang D, Hennenfent G, et al. Curvelet-based seismic data processing: A multiscale and nonlinear approach[J]. Geophysics, 2008, 73(1): A1-A5.
doi: 10.1190/1.2799517
[6]
Mosher C C. Generalized windowed transforms for seismic processing and imaging[C]// 82nd Annual International Meeting Expanded Abstracts,SEG, 2012.
[7]
Yang P, Fomel S. Seislet-based morphological component analysis using scale-dependent exponential shrinkage[J]. Journal of Applied Geophysics, 2015, 118:66-74.
doi: 10.1016/j.jappgeo.2015.04.003
[8]
Men Z, Ning H X, Zhang M G, et al. A method and application of irregular geometry design based on compressive sensing[C]// SEG Technical Program Expanded Abstracts, 2019.
[9]
Sardy S, Bruce A G, Tseng P. Block coordinate relaxation methods for nonparametric wavelet denoising[J]. Journal of Computational and Graphical Statistics, 2000, 9(2):361-379.
Liu C M, Wang D L, Wang T, et al. Random seismic noise attenuation based on the Shearlet transform[J]. Acta Petrolei Sinica, 2014, 35(4): 692-699.
doi: 10.7623/syxb201404009
Zhou Y T, Liu Z F, Zhang Z W. Seismic signal reconstruction under the morphological component analysis framework combined with discrete cosine transform (DCT) and curvelet dictionary[J]. Geophysical Prospecting for Petroleum, 2015, 54(5):560-568.
Zhang L, Han L G, Xu D X, et al. Seismic data reconstruction with Shearlet transform based on compressed sensing technology[J]. Oil Geophysical Prospecting, 2017, 52(2): 220-225.
Xu W, Zhang H, Zhang L Y. A study of seismic data reconstruction based on complex-valued curvelet transform[J]. Geophysical and Geochemical Exploration, 2016, 40(4):750-756.
Shi Z Z, Xia Y Q, Zhou H L, et al. Seismic reflectivity inversion based on L1-L1-norm sparse representation[J]. Geophysical and Geochemical Exploration, 2019, 43(4): 851-858.
Kong X, Mi W T, Mo X, et al. Metallogenic prediction of gold deposits with weighting of evidence based on MRAS in Huaihua area,Hunan Province[J]. Geophysical and Geochemical Exploration, 2016, 40(3): 467-474.
[17]
Wu R S, Geng Y, Ye L. Preliminary study on Dreamlet based compressive sensing data recovery[C]// SEG Technical Program Expanded Abstracts, 2013.
Zhang K, Zhang Y K, Li Z C, et al. Seismic data reconstruction method combined with Discrete Cosine Transform and Shearlet dictionary under Morphological Component Analysis framework[J]. Oil Geophysical Prospecting, 2019, 54(5):12.
[20]
Li X, Guo M J, Li W H, et al. Sparsity promoting reconstruction with compressively acquired land data[C]// SEG Technical Program Expanded Abstracts, 2019.
[21]
Zwartjes P, Gisolf A. Fourier reconstruction with sparse inversion[J]. Geophysical Prospecting, 2007, 5(2):199-221.
Guo Q, Zeng Z F, Yu C X, et al. Seismic random noise suppression based on the high-precision dictionary learning algorithm[J]. Geophysical and Geochemical Exploration, 2017, 41(5):907-913.
[23]
Kumar R, Wason H, Herrmann F J. Source separation for simultaneous towed-streamer marine acquisition—A compressed sensing approach[J]. Geophysics, 2015, 80(6):WD73-WD88.
doi: 10.1190/geo2015-0108.1
[24]
Mosher C C, Kaplan S T, Janiszewski F D. Non-uniform optimal sampling for seismic survey design[C]// 74th EAGE Conference and Exhibition, 2012.
[25]
Neelamani R, Baumstein A, Gillard D, et al. Coherent and random noise attenuation using the curvelet transform[J]. The Leading Edge, 2008, 27:240-248.
doi: 10.1190/1.2840373
Luo Y, Mao H B, Yang X H, et al. Seismic random seismic noise attenuation method on basis of the double sparse representation[J]. Geophysical and Geochemical Exploration, 2018, 42(3):608-615.
