Structure-oriented edge-preserving smoothing based on accurate estimation of orientation and edges
Wang Jun1, Chen Yuhong2, Xu Dahua3, and Qiao Yulei4
1. China University of Petroleum ,Dong Ying, 257061, China.
2. Lanzhou Institute of Geology, Chinese Academy of Sciences, Lanzhou,730000, China.
3. Shengli Well Logging Company, Dong Ying, 257000, China.
4. Shengli Geophysics Academy, Dong Ying,257000, China.
Abstract：
In this paper, we present a new method for reducing seismic noise while preserving structural and stratigraphic discontinuities. Structure-oriented edge-preserving smoothing requires information such as the local orientation and edge of the reflections. The information is usually estimated from seismic data with full frequency bandwidth. When the data has a very low signal to noise ratio (SNR), the noise usually reduces the estimation accuracy. For seismic data with extremely low SNR, the dominant frequency has higher SNR than other frequencies, so it can provide orientation and edge information more reliably than other frequencies. Orientation and edge are usually described in terms of apparent reflection dips and coherence differences, respectively. When frequency changes, both dip and coherence difference change more slowly than the seismogram itself. For this reason, dip and coherence estimated from dominant frequency data can approximately represent those of other frequency data. Ricker wavelet are widely used in seismic modeling. The Marr wavelet has the same shape as Ricker wavelets in both time and frequency domains, so the Marr wavelet transform is selected to divide seismic data into several frequency bands. Reflection apparent dip as well as the edge information can be obtained by scanning the dominant frequency data. This information can be used to selectively smooth the frequency bands (dominant, low, and high frequencies) separately by structure-oriented edge-preserving smoothing technology. The ultimate noise-suppressed seismic data is the combination of the smoothed frequency band data. Application to synthetic and real data shows the method can effectively reduce noise, preserve edges, improve trackable reflection continuity, and maintain useful information in seismic data.
WANG Jun,CHEN Yu-Hong,XU Da-Hua et al. Structure-oriented edge-preserving smoothing based on accurate estimation of orientation and edges[J]. APPLIED GEOPHYSICS, 2009, 6(4): 337-346.
[1]
AlBinHassan, N. M., Luo, Y., and Al-Faraj, M. N., 2006, 3D edge-preserving smoothing and applications: Geophysics, 71(4), 5 - 11.
[2]
Al-Dossary, S., and Marfurt, K. J., 2004, Inter-azimuth coherence attribute for fracture detection: 74th Ann. Internat. Mtg., Eur. Assn. Expl. Geophys., Expanded Abstracts, 183 - 186.
[3]
Al-Dossary, S., and Marfurt, K. J., 2006, 3D volumetric multispectral estimates of reflector curvature and rotation: Geophysics, 71(5), 41 - 51.
[4]
Al-Dossary, S., and Marfurt, K. J., 2007, Lineament-preserving filtering: Geophysics, 72(1), 1 - 8.
[5]
Bahorich, M. S., and Farmer, S. L., 1995, 3-D seismic discontinuity for faults and stratigraphic features, The coherence cube: The Leading Edge, 16(4), 1053 - 1058.
[6]
Bakker,P., L. J. van Vliet, and P.W.Verbeek,1999, Edge-preserving orientation adaptive filtering: Proceedings of the IEEE-CS Conference on Computer Vision and Pattern Recognition, 535 - 540.
[7]
Boncelet, C., R. Hardie, and G. Arce, 1991, LUM filters for smoothing and sharpening, in Dougherty, E. R., Arce, G. R., and C. G. Boncelet Jr, Eds., Nonlinear image processing II: Proceedings of SPIE, 1451, 70 - 73.
[8]
Dalley, R. M., Gevers, E. E. A., Stampli, G. M., Davies, D. J., Gastaldi, C. N., Ruijetnberg, P.R., and Vermeer, G. J. D., 1989, Dip and azimuth displays for 3-D seismic interpretation: First Break, 7(1), 86 - 95.
[9]
Gersztenkorn, A., and Marfurt, K. J., 1999, Eigenstructure-based coherence computations as an aid to 3-D structural and stratigraphic mapping: Geophysics, 64(5), 1468 - 1479.
[10]
Gulunay, N., Holden, J., and Connor, J., 2007, Coherency enhancement on 3D seismic data by dip detection and dip selection: 77th Ann. Internat. Mtg., Eur. Assn. Expl. Geophys., Expanded Abstracts, 2625 - 2629.
[11]
Hocker, C., and Fehmers, G., 2002, Fast structure interpretation with structure-oriented filtering: The Leading Edge, 21(2), 238 - 243.
[12]
Kuwahara, M., Hachimura, K., Eiho, S., and Kinoshita, M., 1976, Processing of RI-angiocardiographic images: in Preston, K., and Onoe, M., Eds., Digital processing of biomedical images, Plenum Press, (New York) 187 - 202.
[13]
Lu, W., Li, Y., Zhang, S., Xiao, H., and Li, Y., 2005, Higher-order-statistics and supertrace-based coherence-estimation algorithm: Geophysics, 70(3), 13 - 18.
[14]
Luo,Y., Marhoon, M., Al-Dossary, S., and M. Alfaraj, 2002, Edge-preserving smoothing and applications: The Leading Edge, 21(2), 136 -158.
[15]
Luo, Y., Higgs, W. G., and Kowalik, W. S., 1996, Edge detection and stratigraphic analysis using 3D seismic data: 66th Annual International Meeting, SEG, Expanded Abstracts, 324 - 327.
[16]
Ma, J., and Plonka, G., 2007, Combined curvelet shrinkage and nonlinear anisotropic diffusion: IEEE Transactions on Image Processing, 16, 2198 - 2206.
Marfurt, K J., and Kirlin, R. L., 2000, 3-D broad-band estimates of reflector dip and amplitude: Geophysics, 65(1), 304 - 320.
[19]
Marfurt, K. J., 2006, Robust estimates of 3D reflector dip and azimuth: Geophysics, 71(4), 29 - 40.
[20]
Perez, G., and Marfurt, K. J., 2008, New azimuthal binning for improved delineation of faults and fractures: Geophysics, 73(1), S7 - S15.
[21]
Perona, P., and Malik, J., 1990, Scale-space and edge detection using anisotropic diffusion: IEEE Transactions on Pattern analysis and Machine Intelligence, 12, 629 - 639.
2009, Vol. 6 
