Cite this article
1
Download
46
Views
Journal Browser
Volume | Year
Issue
Search
News and Announcements
View All
ARTICLE

Self-adaptive edge-preserving smoothing and its applications in seismic impedance interpretation

RONG-HUO DAI1 YU-PEI ZHANG2 FAN-CHANG ZHANG3 CHENG YIN4
Show Less
1 School of Mathematics & Information, China West Normal University, Nanchong 637002, P.R. China,
3 School of Geosciences, China University of Petroleum (East China), Qingdao 266580, P.R. China,
4 School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, P.R. China,
JSE 2021, 30(4), 303–318;
Submitted: 8 August 2019 | Accepted: 8 May 2021 | Published: 1 August 2021
© 2021 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

Dai, R.-H., Zhang, Y.-P., Zhang, F.-C. and Yin, C., 2121. Self-adaptive edge-preserving smoothing and its applications in seismic impedance interpretation. Journal of Seismic Exploration, 30: 303-318. Seismic attributes, such as seismic impedance, AVO or AVA attributes, or other amplitude-like attributes, and so forth, increase the geological information interpretation ability of seismic data. However, in practical case, the calculation of seismic attributes is based on the mathematical formula, such as derivative or integration of seismic data. So, it also enhances random noise. Edge-preserving smoothing (EPS) method can suppress random noise along reflectors while preserving major stratigraphic or structural discontinuities features. These features are very important for seismic data’s geological interpretaion. However, the conventional EPS filter use fixed filter window size to perform in practice. Hence, the little geological features (e.g., channels, minor fault or thin layers) will be suppressed if their width are smaller than used filter window size. On the other hand, if the filter window size is too small, noise will not be removed sufficiently. In order to overcome this issue, we present a new EPS filter which uses a series of different window size and self-adaptively chooses the best one through filter window size scanning. The self-adpative EPS filter can strike a balance between noise remove and useful geological information protection. Applications on model tests and real data examples have shown the effectivity of the proposed method.

Keywords
self-adaptive edge-preserving smoothing
impedance interpretation
filter window size scanning
seismic attributes
References
  1. AlBinHassan, N.M., Luo, Y. and Al-Faraj M.N., 2006. 3D edge-preserving andapplications. Geophysics, 71(4): P5-P11.
  2. Al-Dossary, S., Marfurt, K.J. and Luo, Y., 2002. 3-D edge preserving smoothing forseismic edge detection. Expanded Abstr., 72nd Ann. Internat. SEG Mtg., Salt lakeCity: 524-527.
  3. Al-Dossary, S. and Marfurt, K.J., 2006. 3D volumetric multispectral estimates ofreflector curvature and rotation. Geophysics, 71(5): P41-P51.
  4. Al-Dossary, S. and Marfurt, K.J., 2007. Lineament-preserving filtering.Geophysics,72(1):PI-P8.
  5. Al-Dossary, S. and Wang, Y.E., 2011. Structure-preserving smoothing for 3D seismicattributes. Expanded Abstr., 81st Ann. Internat. SEG Mtg., San Antonio:1004-1008.
  6. Al-Shuhail, A. and Al-Dossary, S., 2020. Attenuation of Incoherent Seismic Noise.Springer Nature, Switzerland.
  7. Ba, J., Xu, W., Fu, L., Carcione, J.M. and Zhang, L., 2017. Rock anelasticity due topatchy-saturation and fabric heterogeneity: A double double-porosity model ofwave propagation. J. Geophys. Res., Solid Earth, 122: 1949-1976.
  8. Ba, J., Zhang, L., Wang, D., Yuan, Z., Cheng, W., Ma, R. and Wu, C., 2018.
  9. Experimental analysis on P-wave attenuation in carbonate rocks and reservoiridentification. J. Seismic Explor., 27: 371-402.
  10. Claerbout, J.F. and Muir, F., 1973. Robust modeling with erratic data. Geophysics, 38:826-844.
  11. Cooke, D.A. and Schneider, W.A., 1983. Generalized linear inversion of reflectionseismic data. Geophysics, 48: 665-676.
  12. Dai, R., Zhang, F., Liu, H. and Li, C., 2014a. Non-Linear Pre-Stack Seismic AVAnversion Based on Bayesian Theory Using Successive Iteration Method. J. JilinUniv., Earth Science Ed., 44: 2026-2033.
  13. Dai, R., Zhang, F., Liu, H., Wang, P. and Zhang Z., 2014b. Method of AVA inversionusing replacing parameters. Expanded Abstr., 84th Ann. Internat. SEG Mtg.,Denver: 559-563.
  14. Dai, R., Zhang, F. and Liu, H., 2015. AVA inversion using replacing parameters methodfor pre-stack seismic data. Prog. Geophys., 30: 261-266.
  15. Dai, R., Zhang, F. and Liu, H., 2016. Seismic inversion based on proximal objectivefunction optimization algorithm. Geophysics, 81(5): R237-R246.
  16. Fehmers, G.C. and Hocker, C.F.W., 2003. Fast structural interpretation withstructure-oriented filtering. Geophysics, 68: 1286-1293.
  17. Halpert, A.D., 2012. Edge-preserving smoothing for segmentation of seismic images.
  18. Expanded Abstr., 82nd Ann. Internat. SEG Mtg., Las Vegas: 1-5.
  19. Hocker, C. and Fehmers, G., 2002. Fast structural interpretation with structure-orientedfiltering. The Leading Edge, 21: 238-243.
  20. Liu, Y., Luo, Y. and Wang, Y., 2009a. Vector median filter and its applications ingeophysics. Expanded Abstr., 79th Ann. Internat. SEG Mtg., Houston: 3342-3345.
  21. Liu, Y., Liu, C. and Wang, D., 2009b. A 1D time-varying median filter for seismicrandom, spike-like noise elimination. Geophysics, 74(1): V17-V24.
  22. Liu, Y. and Luo, Y., 2012. Reducing random noise in vector field using vector medianfilter. Expanded Abstr., 82nd Ann. Internat. SEG Mtg., Las Vegas: 1-5.
  23. Luo, Y., Marhoon, M., Al-Dossary, S. and Alfaraj, M., 2002. Edge-preserving smoothingand applications. The Leading Edge, 21: 136-158.
  24. Marfurt, K.J., 2006. Robust estimates of 3D reflector dip and azimuth. Geophysics, 71(4):P29-P40.
  25. Misra, S. and Sacchi, M.D., 2008. Global optimization with model-space preconditioning:
  26. Application to AVO inversion. Geophysics, 73(5): P71-P82.
  27. Pang, M., Ba, J., Carcione, J.M., Picotti, S., Zhou, J. and Jiang, R., 2019. Estimation ofporosity and fluid saturation in carbonates from rock-physics templates based onseismic Q. Geophysics, 84 (6): M25-M36.
  28. Wang, Z., Yin, C., Fan, T. and Lei, X., 2012. Seismic geomorphology of a channelreservoir in lower Minghuazhen Formation, Laizhouwan subbasin, China.Geophysics, 77(4): B187-B195.
  29. Youzwishen, C.F., 2001. Non-linear sparse and blocky constraints for seismic inverseproblems. University of Alberta.
  30. Zhang, F., Dai, R. and Liu, H., 2014. Seismic inversion based on Ll-norm misfitfunction and total variation regularization. J. Appl. Geophys., 109: 111-118.
  31. Zhang, F., Li, D. and Dai, R., 2015a. Seismic inversion based on edge preserving smoothregularization. J. China Univ. Min. Tech., 44: 255-261.
  32. Zhang, F., Dai, R. and Liu, H., 2015b. High order approximation for scattering matrix inlayered elastic medium and its application in pre-stack seismic inversion. J. Petrol.Sci. Engineer., 131: 210-217.
  33. Zhang, F. and Dai, R., 2016. Nonlinear inversion of pre-stack seismic data using variablemetric method. J. Appl. Geophys., 129: 111-125.
Share
Back to top
Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing