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《应用地球物理(英文版)》 2012-01
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Seismic data denoising based on learning-type overcomplete dictionaries

Tang Gang1,2, Ma Jian-Wei3, and Yang Hui-Zhu11. Institute of Seismic Exploration, School of Aerospace, Tsinghua University, Beijing 100084, China. 2. Geophysical Department, RIPED, PetroChina, Beijing 100083, China. 3. Institute of Applied Mathematics, Harbin Institute of Technology, Harbin 150001, China.  
The transform base function method is one of the most commonly used techniques for seismic denoising, which achieves the purpose of removing noise by utilizing the sparseness and separateness of seismic data in the transform base function domain. However, the effect is not satisfactory because it needs to pre-select a set of fixed transform-base functions and process the corresponding transform. In order to find a new approach, we introduce learning-type overcomplete dictionaries, i.e., optimally sparse data representation is achieved through learning and training driven by seismic modeling data, instead of using a single set of f ixed transform bases. In this paper, we combine dictionary learning with total variation (TV) minimization to suppress pseudo-Gibbs artifacts and describe the effects of non-uniform dictionary sub-block scale on removing noises. Taking the discrete cosine transform and random noise as an example, we made comparisons between a single transform base, non-learning-type, overcomplete dictionary and a learning-type overcomplete dictionary and also compare the results with uniform and nonuniform size dictionary atoms. The results show that, when seismic data is represented sparsely using the learning-type overcomplete dictionary, noise is also removed and visibility and signal to noise ratio is markedly increased. We also compare the results with uniform and nonuniform size dictionary atoms, which demonstrate that a nonuniform dictionary atom is more suitable for seismic denoising.
【Fund】: supported by The National 973 program (No. 2007 CB209505);; Basic Research Project of PetroChina's 12th Five Year Plan (No. 2011A-3601);; RIPED Youth Innovation Foundation (No. 2010-A-26-01)
【CateGory Index】: P631.4
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【Citations】
Chinese Journal Full-text Database 1 Hits
1 Xiao Quan1,Ding Xinghao1,Wang Shoujue1,2,Guo Donghui1,Liao Yinghao1(1 School of Information Science and Technology,Xiamen University,Xiamen 361005,China;2 Institute of Semiconductors,Chinese Academy of Sciences,Beijing 100083,China);Image denoising based on adaptive over-complete sparse representation[J];Chinese Journal of Scientific Instrument;2009-09
【Co-citations】
Chinese Journal Full-text Database 10 Hits
1 Li Pengfei Zhang Min Zhong Zifa(1.Laboratory 309,Electronic Engineering Institute,Hefei 230037,China;2.Anhui Key Laboratory of Electronic Restricting Technique,Hefei,230037,China);Wideband DOA estimation based on sparse representation[J];Journal of Electronic Measurement and Instrument;2011-08
2 FENG Liang1,2,WANG Ping1,XU Ting-fa1,2,SHI Ming-zhu1,2,ZHAO Feng3 (1.School of Optics and Electronics,Beijing Institute of Technology,Beijing 100081,China;2.Key Laboratory of Photoelectronic Imaging Technology and System of the Ministry of Education of China,Beijing Institute of Technology,Beijing 100081,China;3.Guilin University of Electronic Science and Technology,Guilin 541004,China);Dual dictionary sparse restoration of blurred images[J];Optics and Precision Engineering;2011-08
3 SHANG Li1,2,SU Pin-gang1,3(1.Department of Electronic Information Engineering,Suzhou Vocational University,Suzhou Jiangsu 215104,China; 2.Department of Automation,University of Science and Technology of China,Hefei Anhui 230026,China; 3.State Key Laboratory of Millimeter Wave(Southeast University),Nanjing Jiangsu 210098,China);Application of PDE model based on K-SVD in millimeter wave image restoration[J];Journal of Computer Applications;2012-03
4 SHANG Li1,2,SU Pin-gang1,3,CHEN Jie1(1.Department of Electronic Information Engineering,Suzhou Vocational University,Suzhou Jiangsu 215104,China; 2.Department of Automation,University of Science and Technology of China,Hefei Anhui 230026,China; 3.State Key Laboratory of Millimeter Wave(Southeast University),Nanjing Jiangsu 210096,China);Millimeter wave image restoration based on fuzzy radial basis function neural networks and sparse representation[J];Journal of Computer Applications;2012-07
5 GUO De-quan1,2,YANG Hong-yu1,3,LIU Dong-quan3,HE Wen-sen3 (1.Key Laboratory of Fundamental Synthetic Vision Graphics & Image Science for National Defense,Sichuan University,Chengdu 610065,China;2.Dept.of Machinery Engineering,Neijiang Vocational & Technical College,Neijiang Sichuan 641100,China;3.College of Computer Science,Sichuan University,Chengdu 610064,China);Overview on sparse image denoising[J];Application Research of Computers;2012-02
6 WANG Yu-feng,XIA Yuan-tao,WANG Xiao-chen(Faculty of Electrical and Control Engineering,Liaoning Technical University,Huludao 125105,China);Application on overcomplete ICA with noise in coal and rock identification of fully mechanized mining[J];Journal of China Coal Society;2011-S1
7 WANG Jing-jinga,ZHANG Xiao-ganga,CHEN Huab(a.College of Electrical and Information Engineering;b.College of Information Science and Engineering,Hunan University,Changsha 410082,China);DTCWT Fusion Detection for Flame Image Based on Sparse De-noising[J];Computer Engineering;2012-23
8 SHANG Li1,2*,SU Pin'gang1,ZHOU Yan1,3(1.School of Electronic Information Engineering,Suzhou Vocational University,Suzhou Jiangsu 215104,China; 2.Department of Automation,University of Science and Technology of China,Hefei Anhui 230026,China; 3.School of Electronic and Information,Soochow University,Suzhou Jiangsu 215006,China);Image feature extraction based on modified fast sparse coding algorithm[J];Journal of Computer Applications;2013-03
9 GUO JianZhong & QIN XiaoWei College of Physics &Information Technology, Shaanxi Normal University, Xi’an 710062, China;The performance of reconstruction ultrasound imaging based on compressed sensing by sparsity[J];Scientia Sinica(Informationis);2012-06
10 ZENG Jun-guo(Registrar's office,Chengdu Technological University,Chengdu 611730,China);Image super resolution based on couple dictionary learning[J];Computer Engineering and Design;2013-08
China Proceedings of conference Full-text Database 1 Hits
1 ZHANG QIONg~(1)),FU HUAI ZHENG~(2)),SHEN MIN FEN~(3)) 1) 2) 3)(Key Lab of Digital Signal and Image Processing of Guangdong Province,Shantou,515063) 1)(Dept.of Electronic Science and Technology,USTC, Hefei 230026);A color image super-resolution reconstruction algorithm based on sparse representation[A];[C];2010
【Secondary Citations】
Chinese Journal Full-text Database 2 Hits
1 SHAO Jun,YIN Zhong-ke,WANG Jian-ying,ZHANG Yue-fei(School of Information Science & Technology,Southwest Jiaotong University,Chengdu 610031,China);Set Partitioning of the Over-complete Dictionary in Signal Sparse Decomposition[J];Journal of the China Railway Society;2006-01
2 Lian Xueqiang, Ding Xinghao, Yan Jingwen(Institute of Information Science and Technology, Xiamen University, Xiamen 361005, China);SAR image despeckling using nonsubsampled Contourlet transform[J];Chinese Journal of Scientific Instrument;2008-03
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