Higher‐order correlative stacking for seismic data denoising based on the multiple‐domain combination

References

1. BonarD. and SacchiM.2012. Denoising seismic data using the nonlocal means algorithm. Geophysics77(1), A5–A8.
   [Google Scholar]
2. CandèsE.J. and DonohoD.L.2004. New tight frames of curvelets and optimal representations of objects with piecewise C2 singularities. Communications on Pure and Applied Mathematics57(2), 219–266.
   [Google Scholar]
3. CandèsE., DemanetL., DonohoD. and YingL.2006. Fast discrete curvelet transforms. Multiscale Modeling & Simulation5(3), 861–899.
   [Google Scholar]
4. ChenY., GanS., LiuT., YuanJ., ZhangY. and JinZ.2014a. Random noise attenuation by a selective hybrid approach using f‐x empirical mode decomposition. Journal of Geophysics and Engineering12(1), 12.
   [Google Scholar]
5. ChenY., FomelS. and HuJ.2014b. Iterative deblending of simultaneous‐source seismic data using seislet‐domain shaping regularization. Geophysics79(5), V179–V189.
   [Google Scholar]
6. ChenY. and MaJ.2014. Random noise attenuation by fx empirical‐mode decomposition predictive filtering. Geophysics79(3), V81–V91.
   [Google Scholar]
7. ChopraS. and MarfurtK.J.2015. Choice of mother wavelets in CWT spectral decomposition. 2015 SEG annual meeting. Society of Exploration Geophysicists.
   [Google Scholar]
8. DuarteL.T., DonnoD., LopesR.R. and RomanoJ.M.T.2014. Seismic signal processing: some recent advances. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2362–2366. Institute of Electrical and Electronics Engineers.
   [Google Scholar]
9. GórszczykA., AdamczykA. and MalinowskiM.2014. Application of curvelet denoising to 2D and 3D seismic data—practical considerations. Journal of Applied Geophysics105, 78–94.
   [Google Scholar]
10. HerrmannF.J., WangD., HennenfentG. and MoghaddamP.P.2007. Curvelet‐based seismic data processing: a multiscale and nonlinear approach. Geophysics73(1), A1–A5.
    [Google Scholar]
11. KreimerN. and SacchiM.D.2012. A tensor higher‐order singular value decomposition for prestack seismic data noise reduction and interpolation. Geophysics77(3), V113–V122.
    [Google Scholar]
12. LiJ.H., HeZ.X. and LiuQ.H.2013. Higher‐order statistics correlation stacking for DC electrical data in the wavelet domain. Journal of Applied Geophysics99, 51–59.
    [Google Scholar]
13. LiJ.H., ZhangY.J., QiR., LiuQ.H. and XiongB.2016. Wavelet‐based higher order correlative stacking for seismic data processing in curvelet domain. Manuscript in review.
    [Google Scholar]
14. LiuY., FomelS. and LiuC.2015. Signal and noise separation in prestack seismic data using velocity‐dependent seislet transform. Geophysics80(6), WD117–WD128.
    [Google Scholar]
15. LopezG.A. and VerschuurD.J.2015. Closed‐loop surface‐related multiple elimination and its application to simultaneous data reconstruction. Geophysics80(6), V189–V199.
    [Google Scholar]
16. MaJ. and PlonkaG.2010. A review of curvelets and recent applications. IEEE Signal Processing Magazine27(2), 118–133.
    [Google Scholar]
17. MauriG., Williams‐JonesG., SaraccoG. and ZurekJ.M.2012. A geo‐chemical and geophysical investigation of the hydrothermal complex of Masaya volcano, Nicaragua. Journal of Volcanology and Geothermal Research227, 15–31.
    [Google Scholar]
18. NeelamaniR., BaumsteinA.I., GillardD.G., HadidiM.T. and SorokaW.L.2008. Coherent and random noise attenuation using the curvelet transform. The Leading Edge27(2), 240–248.
    [Google Scholar]
19. NaghizadehM.2012. Seismic data interpolation and denoising in the frequency‐wavenumber domain. Geophysics77(2), V71–V80.
    [Google Scholar]
20. OliveiraM.S., HenriquesM.V.C., LeiteF.E.A., CorsoG. and LucenaL.S.2012. Seismic denoising using curvelet analysis. Physica A: Statistical Mechanics and Its Applications391(5), 2106–2110.
    [Google Scholar]
21. OropezaV. and SacchiM.2011. Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis. Geophysics76(3), V25–V32.
    [Google Scholar]
22. PlonkaG. and MaJ.2011. Curvelet‐wavelet regularized split Bregman iteration for compressed sensing. International Journal of Wavelets, Multiresolution and Information Processing9(1), 79–110.
    [Google Scholar]
23. PlonkaG., TenorthS. and RosxaD.2011. A new hybrid method for image approximation using the easy path wavelet transform. IEEE Transactions on Image Processing20(2), 372–381.
    [Google Scholar]
24. RobinsonJ.C.1970. Statistically optimal stacking of seismic data. Geophysics35(3), 436–446.
    [Google Scholar]
25. RoueffA., ChanussotJ. and MarsJ.I.2006. Estimation of polarization parameters using time‐frequency representations and its application to waves separation. Signal Processing86(12), 3714–3731.
    [Google Scholar]
26. RussoF.2007. An‐image enhancement system based on noise estimation. IEEE Transactions on Instrumentation and Measurement56, 1435–1442.
    [Google Scholar]
27. SaevarssonB.B., SveinssonJ.R. and BenediktssonJ.A.2004. Combined wavelet and curvelet denoising of SAR images. Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium (IGARSS'04)6, 4235–4238.
    [Google Scholar]
28. SaraccoG., MoreauF., MathéP.E., HermitteD. and MichelJ.M.2007. Multiscale tomography of buried magnetic structures: its use in the localization and characterization of archaeological structures. Geophysical Journal International171(1), 87–103.
    [Google Scholar]
29. ShanH., MaJ. and YangH.2009. Comparisons of wavelets, contourlets and curvelets in seismic denoising. Journal of Applied Geophysics69(2), 103–115.
    [Google Scholar]
30. StarckJ.L., NguyenM.K. and MurtaghF.2003. Wavelets and curvelets for image deconvolution: a combined approach. Signal Processing83(10), 2279–2283.
    [Google Scholar]
31. TangG. and MaJ.2011. Application of total‐variation‐based curvelet shrinkage for three‐dimensional seismic data denoising. IEEE Geoscience and Remote Sensing Letters8(1), 103–107.
    [Google Scholar]
32. TreebyB.E. and CoxB.T.2010. k‐Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields. Journal of Biomedical Optics15(2), 021314–021314‐12.
    [Google Scholar]
33. VentosaS., RabesonH. and DuvalL.2011. Coherent noise removal in seismic data with redundant multiscale directional filters. 19th European Signal Processing Conference, pp. 1150–1154. Institute of Electrical and Electronics Engineers.
    [Google Scholar]
34. VentosaS., Le RoyS., HuardI., PicaA., RabesonH., RicarteP. et al 2012. Adaptive multiple subtraction with wavelet‐based complex unary Wiener filters. Geophysics77(6), V183–V192.
    [Google Scholar]
35. VuongA.K., ZhangJ., GibsonR.L. and SagerW.W.2015Application of the two‐dimensional continuous wavelet transforms to imaging of the Shatsky Rise plateau using marine seismic data. Geological Society of America Special Papers511, SPE511–07.
    [Google Scholar]
36. WangD., SaabR., YilmazÖ. and HerrmannF.J.2008. Bayesian wave‐field separation by transform‐domain sparsity promotion. Geophysics73, A33–A38.
    [Google Scholar]
37. WardenS., GaramboisS., SailhacP., JouniauxL. and BanoM.2012. Curvelet‐based seismoelectric data processing. Geophysical Journal International190(3), 1533–1550.
    [Google Scholar]
38. XueY., CaoJ., TianR., DuH.K. and YaoY.2015. Wavelet‐based spectrum decomposition of seismic data and its application in hydrocarbon detection. Geophysical Prospecting.
    [Google Scholar]
39. YangP., GaoJ. and ChenW.2012. Curvelet‐based POCS interpolation of nonuniformly sampled seismic records. Journal of Applied Geophysics79, 90–99.
    [Google Scholar]
40. ZegadiA. and ZegadiK.K.2009. Coherent and random noise attenuation using the intrinsic time‐scale decomposition. 2009 SEG annual meeting. Society of Exploration Geophysicists.
    [Google Scholar]
41. ZengY.Q. and LiuQ.H.2004. A multidomain PSTD method for 3D elastic wave equations. Bulletin of the Seismological Society of America94(3), 1002–1015.
    [Google Scholar]
42. ZhangH., LiuT. and ZhangY.2009. Denoising of seismic data via multi‐scale ridgelet transform. Earthquake Science22(5), 493–498.
    [Google Scholar]
43. ZhangS.Z. and XuY.X.2006. Higher‐order correlation stacking for seismic data in the wavelet domain. Chinese Journal of Geophysics49(2), 554–560.
    [Google Scholar]
44. ZhangX.D.1996. Time Sequence Analysis‐Higher–Order Statistics Method. Beijing, China: Tsinghua University Press.
    [Google Scholar]
45. ZhangZ., ZhangX., YuH. and PanX.H.2010. Noise suppression based on a fast discrete curvelet transform. Journal of Geophysics and Engineering7(1), 105.
    [Google Scholar]
46. ZhengJ.J., YinX.Y., ZhangG.Z., WuG.H. and ZhangZ.S.2010. The surface wave suppression using the second generation curvelet transform. Applied Geophysics7(4), 325–335.
    [Google Scholar]