[논문]Dictionary learning with convolutional structure for seismic data denoising and interpolation

Almadani, Murad; Waheed, Umair bin; Masood, Mudassir; Chen, Yangkang

doi:10.1190/geo2019-0689.1

Dictionary learning with convolutional structure for seismic data denoising and interpolation

Geophysics, v.86 no.5, 2021년, pp.V361 - V374

Almadani, Murad (King Fahd University of Petroleum and Minerals, Department of Electrical Engineering, Dhahran 31261, Saudi Arabia.(corresponding author)) , Waheed, Umair bin (.) , Masood, Mudassir (King Fahd University of Petroleum and Minerals, Department of Geosciences, Dhahran 31261, Saudi Arabia..) , Chen, Yangkang (King Fahd University of Petroleum and Minerals, Department of Electrical Engineering, Dhahran 31261, Saudi Arabia.(corresponding author))

Abstract ▼ AI-Helper

Seismic data inevitably suffer from random noise and missing traces in field acquisition. This limits the use of seismic data for subsequent imaging or inversion applications. Recently, dictionary learning has gained remarkable success in seismic data denoising and interpolation. Variants of the patch-based learning technique, such as the K-singular value decomposition (K-SVD) algorithm, have been shown to improve denoising and interpolation performance compared with the analytic transform-based methods. However, patch-based learning algorithms work on overlapping patches of data and do not take the full data into account during reconstruction. In contrast, the data patches (convolutional sparse coding [CSC]) model treats signals globally and, therefore, has shown superior performance over patch-based methods in several image processing applications. As a consequence, we test use of the CSC model for seismic data denoising and interpolation. In particular, we use the local block coordinate descent (LoBCoD) algorithm to reconstruct missing traces and clean seismic data from noisy input. The denoising and interpolation performance of the LoBCoD algorithm has been compared with that of K-SVD and orthogonal matching pursuit (OMP) algorithms using synthetic and field data examples. We have used three quality measures to test the denoising accuracy: the peak signal-to-noise ratio (PS/N), the relative L2-norm of the error (RLNE), and the structural similarity index (SSIM). We find that LoBCoD performs better than K-SVD and OMP for all test cases in improving PS/N and SSIM and in reducing RLNE. These observations suggest a huge potential of the CSC model in seismic data denoising and interpolation applications.

참고문헌 (52)

Abma, Ray, Claerbout, Jon. Lateral prediction for noise attenuation byt-xandf-xtechniques. Geophysics, vol.60, no.6, 1887-1896.

상세보기
Aharon, M., Elad, M., Bruckstein, A.. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, vol.54, no.11, 4311-4322.

상세보기
Beckouche, Simon, Ma, Jianwei. Simultaneous dictionary learning and denoising for seismic data. Geophysics, vol.79, no.3, A27-A31.

상세보기
10.3997/2214-4609-pdb.1.B035 Billette, F., and S. Brandsberg-Dahl, 2005, The 2004 BP velocity benchmark: 67th Annual International Conference and Exhibition, EAGE, Extended Abstracts, B305.
Bonar, David, Sacchi, Mauricio. Denoising seismic data using the nonlocal means algorithm. Geophysics, vol.77, no.1, A5-A8.

상세보기
Advances in Neural Information Processing Systems Bottou L. 1 2008
Wang, Zhou, Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.. Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.13, no.4, 600-612.

상세보기
Foundations and Trends® in Machine Learning Boyd S. 3 2011
10.1109/CVPR.2013.57 Bristow, H., A. Eriksson, and S. Lucey, 2013, Fast convolutional sparse coding: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 391-398.
10.1109/CVPR.2013.57 Bristow, H., and S. Lucey, 2014, Optimization methods for convolutional sparse coding: arXiv preprint arXiv:1406.2407.
Candès, Emmanuel J., Donoho, David L.. New tight frames of curvelets and optimal representations of objects with piecewise C² singularities. Communications on pure and applied mathematics, vol.57, no.2, 219-266.

상세보기
Chen, S., Billings, S. A., Luo, W.. Orthogonal least squares methods and their application to non-linear system identification. International journal of control, vol.50, no.5, 1873-1896.

상세보기
Chen, Scott Shaobing, Donoho, David L., Saunders, Michael A.. Atomic Decomposition by Basis Pursuit. SIAM review, vol.43, no.1, 129-159.

상세보기
Chen, Yangkang. Fast dictionary learning for noise attenuation of multidimensional seismic data. Geophysical journal international, vol.209, no.1, 21-31.

상세보기
Chen, Yangkang, Ma, Jianwei, Fomel, Sergey. Double-sparsity dictionary for seismic noise attenuation. Geophysics, vol.81, no.2, V103-V116.

상세보기
Do, M.N., Vetterli, M.. Framing pyramids. IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, vol.51, no.9, 2329-2342.

상세보기
Elad, Michael, Aharon, Michal. Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.15, no.12, 3736-3745.

상세보기
Engan, K., B. D. Rao, and K. Kreutz-Delgado, 1999, Frame design using FOCUSS with method of optimal directions (MOD): Proceedings of the NORSIG, 65-69.
Fadili, M.J., Starck, J.-L., Murtagh, F.. Inpainting and Zooming Using Sparse Representations. The Computer journal, vol.52, no.1, 64-79.

상세보기
Gan, S., Wang, S., Chen, Y., Chen, X., Huang, W., Chen, H.. Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on seislet transform. Journal of applied geophysics, vol.130, 194-208.

상세보기
10.1109/ICCV.2015.212 Gu, S., W. Zuo, Q. Xie, D. Meng, X. Feng, and L. Zhang, 2015, Convolutional sparse coding for image super-resolution: Proceedings of the IEEE International Conference on Computer Vision, 1823-1831.
Ibrahim, Amr, Sacchi, Mauricio D.. Simultaneous source separation using a robust Radon transform. Geophysics, vol.79, no.1, V1-V11.

상세보기
10.1190/1.3255551
Kong, B., and C. C. Fowlkes, 2014, Fast convolutional sparse coding (FCSC): Department of Computer Science, University of California, Technical Report 3.
Kong, Dehui, Peng, Zhenming. Seismic random noise attenuation using shearlet and total generalized variation. Journal of Geophysics and Engineering, vol.12, no.6, 1024-1035.

상세보기
Le Pennec, E., Mallat, S.. Sparse geometric image representations with bandelets. IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, vol.14, no.4, 423-438.

상세보기
Liu, Lina, Ma, Jianwei, Plonka, Gerlind. Sparse graph-regularized dictionary learning for suppressing random seismic noise. Geophysics, vol.83, no.3, V215-V231.

상세보기
Liu, Yu, Chen, Xun, Ward, Rabab K., Jane Wang, Z.. Image Fusion With Convolutional Sparse Representation. IEEE signal processing letters, vol.23, no.12, 1882-1886.

상세보기
10.1190/AIML2018-02.1
Mousavi, S. Mostafa, Langston, Charles A.. Hybrid Seismic Denoising Using Higher‐Order Statistics and Improved Wavelet Block Thresholding. Bulletin of the Seismological Society of America, vol.106, no.4, 1380-1393.

상세보기
Nazari Siahsar, Mohammad Amir, Gholtashi, Saman, Kahoo, Amin Roshandel, Chen, Wei, Chen, Yangkang. Data-driven multitask sparse dictionary learning for noise attenuation of 3D seismic data. Geophysics, vol.82, no.6, V385-V396.

상세보기
Neelamani, Ramesh, Baumstein, Anatoly I., Gillard, Dominique G., Hadidi, Mohamed T., Soroka, William L.. Coherent and random noise attenuation using the curvelet transform. The leading edge, vol.27, no.2, 240-248.

상세보기
Oren, Can, Nowack, Robert L.. An overview of reproducible 3D seismic data processing and imaging using Madagascar. Geophysics, vol.83, no.2, F9-F20.

상세보기
Oropeza, Vicente, Sacchi, Mauricio. Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis. Geophysics, vol.76, no.3, V25-V32.

상세보기
Papyan, Vardan, Sulam, Jeremias, Elad, Michael. Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding. IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, vol.65, no.21, 5687-5701.

상세보기
Pati, Y. C., R. Rezaiifar, and P. S. Krishnaprasad, 1993, Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition: Proceedings of the 27th Asilomar Conference on Signals, Systems and Computers, IEEE, 40-44.
Qu, X., Guo, D., Ning, B., Hou, Y., Lin, Y., Cai, S., Chen, Z.. Undersampled MRI reconstruction with patch-based directional wavelets. Magnetic resonance imaging, vol.30, no.7, 964-977.

상세보기
Qu, X., Hou, Y., Lam, F., Guo, D., Zhong, J., Chen, Z.. Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator. Medical image analysis, vol.18, no.6, 843-856.

상세보기
10.1109/ISBI.2016.7493321 Quan, T. M., and W.K. Jeong, 2016, Compressed sensing reconstruction of dynamic contrast enhanced MRI using GPU-accelerated convolutional sparse coding: 13th International Symposium on Biomedical Imaging (ISBI), IEEE, 518-521.
10.1109/ICASSP.2015.7178176 Romano, Y., and M. Elad, 2015, Patch-disagreement as away to improve K-SVD denoising: International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, 1280-1284.
Rubinstein, R., Zibulevsky, M., Elad, M.. Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation. IEEE transactions on signal processing : a publication of the IEEE Signal Processing Society, vol.58, no.3, 1553-1564.

상세보기
Shen, B., W. Hu, Y. Zhang, and Y.J. Zhang, 2009, Image inpainting via sparse representation:International Conference on Acoustics, Speech and Signal Processing, IEEE, 697-700.
Nazari Siahsar, Mohammad Amir, Gholtashi, Saman, Abolghasemi, Vahid, Chen, Yangkang. Simultaneous denoising and interpolation of 2D seismic data using data-driven non-negative dictionary learning. Signal processing : the official publication of the European Association for Signal Processing (EURASIP), vol.141, 309-321.

상세보기
IEEE Geoscience and Remote Sensing Letters Tang G. 8 2010
Turquais, Pierre, Asgedom, Endrias G., Söllner, Walter. A method of combining coherence-constrained sparse coding and dictionary learning for denoising. Geophysics, vol.82, no.3, V137-V148.

상세보기
Wang, Benfeng, Chen, Xiaohong, Li, Jingye, Cao, Jingjie. An Improved Weighted Projection Onto Convex Sets Method for Seismic Data Interpolation and Denoising. IEEE journal of selected topics in applied earth observations and remote sensing, vol.9, no.1, 228-235.

상세보기
Wang, Benfeng, Wu, Ru-Shan, Chen, Xiaohong, Li, Jingye. Simultaneous seismic data interpolation and denoising with a new adaptive method based on dreamlet transform. Geophysical journal international, vol.201, no.2, 1182-1194.

상세보기
Zhang, Chao, van der Baan, Mirko, Chen, Ting. Unsupervised Dictionary Learning for Signal‐to‐Noise Ratio Enhancement of Array Data. Seismological research letters, vol.90, no.a2, 573-580.

상세보기
Science in China Series F: Information Sciences Zhao R. 52 2009
Zhu, Lingchen, Liu, Entao, McClellan, James H.. Seismic data denoising through multiscale and sparsity-promoting dictionary learning. Geophysics, vol.80, no.6, WD45-WD57.

상세보기
10.1109/CVPR.2019.00840 Zisselman, E., J. Sulam, and M. Elad, 2018, A local block coordinate descent algorithm for the convolutional sparse coding model: arXiv preprint arXiv:1811.00312.
Zu, Shaohuan, Zhou, Hui, Wu, Rushan, Jiang, Maocai, Chen, Yangkang. Dictionary learning based on dip patch selection training for random noise attenuation. Geophysics, vol.84, no.3, V169-V183.

상세보기

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증

Dictionary learning with convolutional structure for seismic data denoising and interpolation

Abstract ▼ AI-Helper

참고문헌 (52)

이 논문을 인용한 문헌

관련 콘텐츠

원문 URL 링크

이 논문과 함께 이용한 콘텐츠

AI-Helper ※ AI-Helper는 오픈소스 모델을 사용합니다.

선택된 텍스트