Spectral decomposition with f−x−y preconditioning

References

1. AskariR. and SiahkoohiH.2008. Ground roll attenuation using the s and x‐f‐k transforms. Geophysical Prospecting 56, 105–114.
   [Google Scholar]
2. BeckA. and TeboulleM.2009. A fast iterative shrinkage‐thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences 2, 183–202.
   [Google Scholar]
3. BonarD. and SacchiM.2010. Complex spectral decomposition via inversion strategies. SEG Annual Meeting 29, 1408–1412.
   [Google Scholar]
4. CanalesL.1984. Random noise reduction. SEG Expanded Abstracts, 525–527.
5. CastagnaJ., SunS. and SiegfriedR.2003. Instantaneous spectral analysis: Detection of low‐frequency shadows associated with hydrocarbons. The Leading Edge 22, 22.
   [Google Scholar]
6. ChakrabortyA. and OkayaD.1995. Frequency‐time decomposition of seismic data using wavelet‐based methods. Geophysics 60, 1906–1916.
   [Google Scholar]
7. ChaseM.1992. Random noise reduction by 3‐D spatial prediction filtering. SEG Expanded Abstracts, 1152–1153.
8. ChenS., DonohoD. and SaundersM.2001. Atomic decomposition by basis pursuit. SIAM Review 43, 129–159.
   [Google Scholar]
9. ClaerboutJ.1992. Earth Soundings Analysis: Processing Versus Inversion. Blackwell Scientific Publications. Stanford Exploration project.
   [Google Scholar]
10. CohenL.1993. Instantaneous ‘anything’. Proceedings of the IEEE 4, 105–108.
    [Google Scholar]
11. DaubechiesI., DefriseM. and MolC.2004. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Communication on Pure and Applied Mathematics57, 1413–1457.
    [Google Scholar]
12. DonohoD.2006. For most large underdetermined systems of linear equations, the minimal ell‐1 norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics 59, 797–829.
    [Google Scholar]
13. ElbothT., PresterudI. and HermansenD.2010. Time‐frequency seismic data de noising. Geophysical Prospecting 58, 441–453.
    [Google Scholar]
14. GaborD.1946. Theory of communication. J. IEEE 93, 429–457.
    [Google Scholar]
15. GardnerT. and MagnascoM.2006. Sparse time‐frequency representations. Proceedings of the National Academy of Sciences 103, 6094–6099.
    [Google Scholar]
16. GiroldiL. and AlegriaF.2005. Using spectral decomposition to identify and characterize glacial valleys and fluvial channels within the Carboniferous section in Bolivia. The Leading Edge 24, 1152–1159.
    [Google Scholar]
17. GulunayN.2000. Noncausal spatial prediction filtering for random noise reduction on 3‐D poststack data. Geophysics 65, 1641–1653.
    [Google Scholar]
18. GulunayN., SudhakarV., GerrardC. and MonkD.1993. Prediction filtering for 3‐D poststack data. SEG Expanded Abstracts, 1183–1186.
19. HardyH., BeierR. and GastonJ.2003. Frequency estimates of seismic traces. Geophysics 68, 370–380.
    [Google Scholar]
20. LiY. and ZhengX.2008. Spectral decomposition using Wigner‐Ville distribution with applications to carbonate reservoir characterization. The Leading Edge 27, 1050–1057.
    [Google Scholar]
21. LiY., ZhengX. and ZhangY.2011. High‐frequency anomalies in carbonate reservoir characterization using spectral decomposition. Geophysics 76, V47–V57.
    [Google Scholar]
22. LiuJ. and MarfurtK.2007. Instantaneous spectral attributes to detect channels. Geophysics 72, P23–P31.
    [Google Scholar]
23. MarfurtK. and KirlinR.2001. Narrow‐band spectral analysis and thin‐bed tuning. Geophysics 66, 1274–1283.
    [Google Scholar]
24. de MatosM., DavogusttoO., ZhangK. and MarfurtK.2011. Detecting stratigraphic discontinuities using time‐frequency seismic phase residues. Geophysics 76, P1–P10.
    [Google Scholar]
25. NaghizadehM. and InnanenK.2011. Seismic data interpolation using a fast generalized Fourier transform. Geophysics 76, V1–V10.
    [Google Scholar]
26. OdebeatuE., ZhangJ., ChapmanM., LiuE. and LiX.2006. Application of spectral decomposition to detection of dispersion anomalies associated with gas saturation. The Leading Edge 25, 206–210.
    [Google Scholar]
27. ParolaiS.2009. Denoising of seismograms using the S transform. Bulletin of the Seismological Society of America 99, 226–234.
    [Google Scholar]
28. PartykaG., GridleyJ. and LopezJ.1999. Interpretational applications of spectral decomposition in reservoir characterization. The Leading Edge 18, 353–360.
    [Google Scholar]
29. PinnegarC. and MansinhaL.2003. The S‐transform with windows of arbitrary and varying shape. Geophysics 68, 381–385.
    [Google Scholar]
30. PortniaguineO. and CastagnaJ.2004. Inverse spectral decomposition. SEG Expanded Abstracts.
31. RobinsonE. and TreitelS.1980. Geophysical Signal Analysis. Prentice‐Hall, Inc.
    [Google Scholar]
32. SinhaS., RouthP., AnnoP. and CastagnaJ.2005. Spectral decomposition of seismic data with contiuous‐wavelet transform. Geophysics 70, P19–P25.
    [Google Scholar]
33. SteeghsP., and DrijkoningenG.2001. Seismic sequence analysis and attribute extraction using quadratic time‐frequency representations. Geophysics 66, 1947–1959.
    [Google Scholar]
34. StockwellR., MansinhaL. and LoweR.1996. Localization of the complex spectrum: The s transform. IEEE Transactions on Signal Processing 44, 998–1001.
    [Google Scholar]
35. TanerM., KoehlerF. and SheriffR.1979. Complex seismic trace analysis. Geophysics 44, 1041–1063.
    [Google Scholar]
36. WangY.1999. Random noise attenuation using forward‐backward linear prediction. Journal of Seismic Exploration 8, 133–142.
    [Google Scholar]
37. WangY.2007. Seismic time‐frequency spectral decomposition by matching pursuit. Geophysics 72, V13–V20.
    [Google Scholar]
38. WangY.2010. Multichannel matching pursuit for seismic trace decomposition. Geophysics 75, V61–V66.
    [Google Scholar]
39. WangJ. and SacchiM.2009. Noise reduction by structure‐and‐amplitude preserving multi‐channel deconvolution. CSEG Recorder 34, 24–27.
    [Google Scholar]
40. YilmazÖ.2001. Seismic Data Analysis: Processing, Inversion, and Interpretation of Seismic Data, Second Edition. Society of Exploration Geophysicists.
    [Google Scholar]