2019 Vol. 62, No. 1
Article Contents

LI Yong, ZHANG YiMing, LEI Qin, NIU Cong, ZHOU YuBang, YE YunFei. 2019. Online dictionary learning seismic weak signal denoising method under model constraints. Chinese Journal of Geophysics (in Chinese), 62(1): 411-420, doi: 10.6038/cjg2019M0373
Citation: LI Yong, ZHANG YiMing, LEI Qin, NIU Cong, ZHOU YuBang, YE YunFei. 2019. Online dictionary learning seismic weak signal denoising method under model constraints. Chinese Journal of Geophysics (in Chinese), 62(1): 411-420, doi: 10.6038/cjg2019M0373

Online dictionary learning seismic weak signal denoising method under model constraints

  • Fund Project:

    国家科技重大专项(2016ZX05026001-004)资助

More Information
  • In this paper, the latest online dictionary learning denoising method is developed for the complexity of noise components and noise structures and the characteristics of weak signals. The online dictionary learning denoising is conducted by means of data-driven and iterative learning to obtain the sparse solution of the signal to realize the denoising of the signal. Based on this, an online dictionary learning denoising method under the combined constraints of data-driven and model-driven models is proposed. A better quality learning sample is obtained in a model driven process to build a dictionary and then to conduct denoising. Compared with the traditional wavelet transform for theoretical seismic synthesis recording, it is far superior to the traditional time-frequency domain denoising method in the case of low-SNR weak signals. The actual data denoising process shows that the online dictionary learning denoising method under model constraints is an effective denoising method. This joint denoising method can effectively extract weak signals against high noise and has broad application prospect.

  • 加载中
  •  

    Aharon M, Elad M, Bruckstein A. 2006. rmK-SVD:an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11):4311-4322. doi: 10.1109/TSP.2006.881199

     

    Beckouche S, Ma J M. 2014. Simultaneous dictionary learning and denoising for seismic data. Geophysics, 79(3):A27-A31. doi: 10.1190/geo2013-0382.1

     

    Cheng S J, Han L G, Yu J L, et al. 2018. Seismic data denoising based on improved K-SVD dictionary learning method. Global Geology (in Chinese), 37(2):627-635.

     

    Deka B, Baishnab D. 2012. Removal of random-valued impulse noise using overcomplete DCT dictionary.//Proceedings of the Cube International Information Technology Conference. New York, NY, USA: ACM: 42-46.

     

    Hennenfent G, Herrmann F J. 2006. Seismic denoising with nonuniformly sampled curvelets. Computing in Science & Engineering, 8(3):16-25.

     

    Jacques L, Duval L, Chaux C, et al. 2011. A panorama on multiscale geometric representations, intertwining spatial, directional and frequency selectivity. Signal Processing, 91(12):2699-2730. doi: 10.1016/j.sigpro.2011.04.025

     

    Jia R S, Zhao T B, Sun H M, et al. 2015. Microseismic signal denoising method based on empirical mode decomposition and independent component analysis. Chinese J.Geophys. (in Chinese).58(3):1013-1023.

     

    Kaplan S T, Sacchi M D, Ulrych T J. 2009. Sparse coding for data-driven coherent and incoherent noise attenuation.//79th Ann. Internat Mtg., Soc. Expi. Geophys.. Expanded Abstracts.

     

    Li L C. 2014. Research of seismic signal denoising based on sparse decomposition algorithm[Master's thesis] (in Chinese). Daqing: Northeast Petroleum University.

     

    Ma J W, Plonka G. 2011. The curvelet transform. IEEE Signal Processing Magazine, 27(2):118-133.

     

    Mairal J, Bach F, Ponce J, et al. 2009. Online dictionary learning for sparse coding.//Proceedings of the 26th Annual International Conference on Machine Learning. New York, NY, USA: ACM, 689-696.

     

    Olshausen B A, Millman K J. 1997. Learning sparse overcomplete image representations. Proceedings of the SPIE, 4119:445-452.

     

    Su J S, Luan X Q. 2008. Research of image denoising based on Gabor filter. Journal of Yili Normal University (Natural Science Edition) (in Chinese), (4):15-19.

     

    Tang G, Ma J W, Yang H Z. 2012. Seismic data denoising based onlearning-type overcomplete dictionaries. Applied Geophysics, 9(1):27-32. doi: 10.1007/s11770-012-0310-z

     

    Wang D, Liu C, Liu Y, et al. 2006. Study of the noise elimination methods in the reflection seismic exploration. Progress in Geophysics (in Chinese), 21(3):957-970.

     

    Xu D X. 2016. Research on Seismic Denoising Based on The Sparse Representation and Dictionary Learning[Master's thesis] (in Chinese). Changchun: Jilin University.

     

    程时俊, 韩立国, 于江龙等. 2018.基于改进K-SVD字典学习方法的地震数据去噪.世界地质, 37(2):627-635. doi: 10.3969/j.issn.1004-5589.2018.02.030

     

    李立超. 2014.基于稀疏分解算法的地震信号去噪研究[硕士论文].大庆: 东北石油大学.

     

    苏金善, 栾雪琴. 2008.基于Gabor滤波器的图像去噪研究.伊犁师范学院学报(自然科学版), (4):15-19. doi: 10.3969/j.issn.1673-999X.2008.04.004

     

    王典, 刘财, 刘洋等. 2006.反射法地震勘探噪声消除技术研究.地球物理学进展, 21(3):957-970. doi: 10.3969/j.issn.1004-2903.2006.03.040

     

    许德鑫. 2016.基于稀疏表示和字典学习的地震数据去噪研究[硕士论文].长春: 吉林大学.

     

    贾瑞生, 赵同彬, 孙红梅等. 2015.基于经验模态分解及独立成分分析的微震信号降噪方法.地球物理学报, 58(3):1013-1023.

通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Figures(13)

Tables(3)

Article Metrics

Article views(1440) PDF downloads(552) Cited by(0)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint