Data‐driven time–frequency analysis of seismic data using non‐stationary Prony method

References

1. CastagnaJ., SunS. and SiegfriedR.W.2003. Instantaneous spectral analysis: detection of low‐frequency shadows associated with hydrocarbons. The Leading Edge22, 120–127.
   [Google Scholar]
2. ChenS.S., DonohoD.L. and SaundersM.A.1998. Atomic decomposition by basis pursuit. SIAM Review20(1), 33–61.
   [Google Scholar]
3. ChenY. and FomelS.2015. EMD‐seislet transform. 85th SEG annual international meeting, Expanded Abstracts, 4775–4778.
4. ChenY., LiuT., ChenX., LiJ. and WangE.2014. Time‐frequency analysis of seismic data using synchrosqueezing wavelet transform. Journal of Seismic Exploration23(4), 303–312.
   [Google Scholar]
5. CohenL.1989. Time‐frequency distributions—A review. Proceedings of the IEEE77(7), 941–981.
   [Google Scholar]
6. DaubechiesI., LuJ. and WuH.T.2011. Synchrosqueezed wavelet transforms: an empirical mode decomposition‐like tool. Applied and Computational Harmonic Analysis30(2), 243–261.
   [Google Scholar]
7. EbromD.2004. The low frequency gas shadows in seismic sections. The Leading Edge23, 772.
   [Google Scholar]
8. FomelS.2007. Shaping regularization in geophysical estimation problems. Geophysics72(2), R29–R36.
   [Google Scholar]
9. FomelS.2009. Adaptive multiple subtraction using regularized nonstationary regression. Geophysics74(1), V25–V33.
   [Google Scholar]
10. FomelS.2013. Seismic data decomposition into spectral components using regularized nonstationary autoregression. Geophysics78(6), O69–O76.
    [Google Scholar]
11. HanJ. and van der BaanM.2013. Empirical mode decomposition for seismic time‐frequency analysis. Geophysics78(2), O9–O19.
    [Google Scholar]
12. HouT.Y. and ShiZ.2013. Data‐driven time‐frequency analysis. Applied and Computational Harmonic Analysis35(2), 284–308.
    [Google Scholar]
13. HuangN.E., ShenZ., LongS.R., WuM.C., ShihH.H., ZhengQ.et al. 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non‐stationary time series analysis. Proceedings of the Royal Society of London Series A454, 903–995.
    [Google Scholar]
14. HuangZ., ZhangJ., ZhaoT. and SunY.2015. Synchrosqueezing S‐transform and its application in seismic spectral decomposition. IEEE Transactions on Geoscience and Remote Sensing54(2), 817–825.
    [Google Scholar]
15. KazemeiniS.H., JuhlinC., JorgensenK.Z. and NordenB.2009. Application of the continuous wavelet transform on seismic data for mapping of channel deposits and gas detection at the CO2SINK site, Ketzin, Germany. Geophysical Prospecting57(1), 111–123
    [Google Scholar]
16. LiuG., FomelS. and ChenX.2011. Time‐frequency analysis of seismic data using local attributes. Geophysics76(1), P23–P34.
    [Google Scholar]
17. LiuW., CaoS. and ChenY.2016. Seismic time‐frequency analysis via empirical wavelet transform. IEEE Geoscience and Remote Sensing Letters13(1), 28–32.
    [Google Scholar]
18. LobosT., RezmerJ. and SchegnerJ.2003. Parameter estimation of distorted signals using Prony method. Power Tech Conference Proceedings, 41–44.
19. MallatS.2009. A Wavelet Tour of Signal Processing: The Sparse Way. Academic Press.
    [Google Scholar]
20. MallatS. and ZhangZ.1993. Matching pursuit with time‐frequency dictionaries. IEEE Transactions on Signal Processing41(12), 3397–3415.
    [Google Scholar]
21. MallatS.G.1989. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence11(7), 674–693.
    [Google Scholar]
22. MitrofanovG. and PriimenkoV.2015. Prony filtering of seismic data. Acta Geophysica63(3), 652–678.
    [Google Scholar]
23. OberlinT., MeignenS. and PerrierV.2014. The Fourier‐based synchrosqueezing transform. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 315–319.
24. PeterT. and PlonkaG.2013. A generalized Prony method for reconstruction of sparse sums of eigenfunctions of linear operators. Inverse Problems29(2), 025001.
    [Google Scholar]
25. PronyR.1795. Essai expérimental et analytique. Annuaire de l'École Polytechnique1(2), 24.
    [Google Scholar]
26. ReineC., van der BaanM. and ClarkR.2009. The robustness of seismic attenuation measurements using fixed‐ and variable‐window time‐frequency transforms. Geophysics74(2), 123–135.
    [Google Scholar]
27. StockwellR.G., MansinhaL. and LoweR.P.1996. Localization of the complex spectrum. IEEE Transactions on Signal Processing44(4), 998–1001.
    [Google Scholar]
28. TaryJ.B., HerreraR.H., HanJ. and van der BaanM.2014. Spectral estimation—What is new? What is next? Reviews of Geophysics52(4), 723–749.
    [Google Scholar]
29. TikhonovA.N.1963. Solution of incorrectly formulated problems and the regularization method. Soviet Mathematics ‐ Doklady4, 1035–1038.
    [Google Scholar]
30. TohK. and TrefethenL.1994. Pseudozeros of polynomials and pseudospectra of companion matrices. Numerische Mathematik68(3), 403–425.
    [Google Scholar]
31. TorresM.E., ColominasM.A., SchlotthauerG. and FlandrinP.2011. A complete ensemble empirical mode decomposition with adaptive noise. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 4144–4147. IEEE.
32. WangY.H., YehC.H., YoungH.W.V., HuK. and LoM.T.2014. On the computational complexity of the empirical mode decomposition algorithm. Physica A: Statistical Mechanics and its Applications400(C), 159–167.
    [Google Scholar]
33. WuZ. and HuangN.E.2009. Ensemble empirical mode decomposition: a noise‐assisted data analysis method. Advances in Adaptive Data Analysis1(1), 1–41.
    [Google Scholar]