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Volume 21 Issue 5
Sep.  1999
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Wu Jianhua. SEISMIC DATA DENOISING WITH FAST GABOR REPRESENTATIONS[J]. Journal of Electronics & Information Technology, 1999, 21(5): 713-717.
Citation: Wu Jianhua. SEISMIC DATA DENOISING WITH FAST GABOR REPRESENTATIONS[J]. Journal of Electronics & Information Technology, 1999, 21(5): 713-717.

SEISMIC DATA DENOISING WITH FAST GABOR REPRESENTATIONS

  • Received Date: 1997-12-10
  • Rev Recd Date: 1998-09-19
  • Publish Date: 1999-09-19
  • Gabor representations are signal expansion using sets of functions that are localized and concentrated in time and frequency domain. This characteristic makes them be suitable for processing time-dependent or nonstationary signal. It is shown that Gabor representations formulated with frame theory can be used to remove noise from seismic data. The simulation shows that Gabor representations filtering techniques can outperform SVD eigenimage filtering techniques in the removal of noise.
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  • Shie Qian, Dapang Chen. Discrete Gabor transform. IEEE Trans. on SP, 1993, SP-41(7): 2429-2438.[2]Porat M, Zeevi Y Y. The generalized Gabor scheme of image representation in biological and machine vision. IEEE Trans. on PAMI, 1988, PAMI-10(4): 452-467.[3]刘贵忠, 邸双亮. 小波分析及其应用.西安西安电子科技大学出版社, 1992, 第八章, 103-158.[4]Daubechies I. The Wavelet Transform: A Method for Time-Frequency Localization, in Advances in Spectrum Analysis and Array Processing, S. Haykin Ed. Englewood Cliffs, NJ: Prentice Hall, 1990, Ch. 8, 366-417.[5]Daugman J G. Uncertainty relation for resolution in space, spatial frequency and orientation optimized by 2D visual cortical filters[J].J. Opt. Soc. Amer. (A.1985, 2:1160-1169[6]Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE Trans. on IT, 1990, IT-36(5): 961-1005.[7]Freire S L, Ulrych T J. Application of singular value decomposition to vertical seismic profiling[J].Geophysics.1988, 53(6):778-785[8]陈遵德, 段天友, 朱广生. SVD滤波方法的改进及应用石油地球物理勘探. 1994, 29(12): 783-792.
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