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Enhancing the resolution of time–frequency spectrum using directional multichannel matching pursuit

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Języki publikacji
EN
Abstrakty
EN
Matching pursuit is able to decompose signals adaptively into a series of wavelets and has been widely applied in signal processing of the geophysical felds. Single-channel matching pursuit could not take into account the lateral continuity of seismic traces, and the recent multichannel matching pursuit exploits the lateral coherence as a constraint, which helps to improve the stability of decomposition results. However, atoms searched by multichannel matching pursuit currently are just shared by lateral seismic traces at the same time slicers. The lack of directionality in multichannel search strategies leads to irrationality in dealing with large dip angle seismic traces. Considering that the waveforms of refection events are relatively continuous and similar, an improved multichannel matching pursuit is proposed to realize the directional decomposition of adjacent signals. Based on the principle of seismic refection events tracking and identifcation, directional multichan nel decomposition of seismic traces is realized. The seismic channel to be decomposed is correlated with the time shift of the optimal atom determined by the previous seismic channel. The time position of the maximum correlation indicates the center time of the optimal atom. Optimal atoms identifed by one iteration of multichannel decomposition have the same frequency and phase parameters, diferent center time and amplitude parameters. The center time of the optimal atoms is consistent with seismic refection events. Tests illustrate that the algorithm can successfully reconstruct 2D seismic data without reducing accuracy. Besides, the application of feld data is of great signifcance for reservoir exploration and hydro-carbon interpretation.
Czasopismo
Rocznik
Strony
1643--1652
Opis fizyczny
Bibliogr. 21 poz.
Twórcy
autor
  • School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong, China
  • Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong, China
autor
  • School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong, China
  • Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong, China
autor
  • School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong, China
  • Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong, China
Bibliografia
  • 1. Duijndam AJW, Schonewille MA (1999) Nonuniform fast fourier transform. Geophysics 64(2):539–551
  • 2. Durka PJ, Matysiak A, Montes EM, Sosa PV, Blinowska KJ (2005) Multichannel matching pursuit and EEG inverse solutions. J Neurosci Methods 148(1):49–59
  • 3. Gribonval R, Bacry E, Mallat S, Depalle P, Rodet X (1996) Analysis of sound signals with high resolution matching pursuit. In: IEEE-SP International Symposium on Time-frequency & Time-scale Analysis, IEEE. https://doi.org/10.1109/TFSA.1996.546702
  • 4. Hess-Nielsen N, Wickerhauser MV (1996) Wavelets and time-frequency analysis. Proc IEEE 84(4):523–540
  • 5. Jun W, Lijun W, Yanjiang W (2012) Seismic signal fast decomposition by multichannel matching pursuit with genetic algorithm. In: Presented at the 11th international conference on signal processing, IEEE
  • 6. Lin JL, Hwang WL, Pei SC (2006) Multiple blocks matching pursuit update algorithm for low bit rate video coding. IEEE Trans Circuits Syst Video Technol 16(3):331–337
  • 7. Liu J, Marfurt KJ (2005) Matching pursuit decomposition using Morlet wavelets. In: SEG technical program expanded abstracts, pp 786–789
  • 8. Liu J, Wu Y, Han D, Li X (2004) Time-frequency decomposition based on Ricker wavelet. In: SEG technical program expanded abstracts, pp 1937–1940
  • 9. Mallat S, Zhang Z (1993) Matching pursuit with time-frequency dictionaries. IEEE Trans Signal Process 41(12):3377–3415
  • 10. Masood M, Al-Naffouri TY (2013) Sparse reconstruction using distribution agnostic bayesian matching pursuit. IEEE Trans Signal Process 61(21):5298–5309
  • 11. Miandji E, Emadi M, Unger J, Afshari E (2017) On probability of support recovery for orthogonal matching pursuit using mutual coherence. IEEE Signal Process Lett 24(11):1646–1650
  • 12. Ning C, Wang Y (2014) An improved method for multichannel matching pursuit decomposition. In: 76th EAGE Conference and Exhibition 2014, p 1–5. https://doi.org/10.3997/2214-4609.20141566
  • 13. Qian S, Chen D (1999) Joint time-frequency analysis. IEEE Signal Process Mag 16(2):52–67
  • 14. Salomon BG, Ur H (2004) Sparse approximations with a high resolution greedy algorithm. In: IEEE International Conference on Electronics IEEE. https://doi.org/10.1109/ICECS.2004.1399685
  • 15. Sinha S, Routh PS, Anno PD, Castagna JP (2005) Spectral decomposition of seismic data with continuous wavelet transform. Geophysics 70(6):P19–P25
  • 16. Stefanoiu D, Lonescu F (2003) A genetic matching pursuit algorithm. In: International symposium on signal processing & its applications. IEEE
  • 17. Stockwell RG, Mansinha L, Lowe RP (2002) Localization of the complex spectrum: the s transform. IEEE Trans Signal Process 44(4):998–1001
  • 18. Tropp JA, Gilbert AC (2008) Signal recovery from random measurements via orthogonal matching pursuit: the gaussian case. IEEE Trans Inf Theory 53(12):4655–4666
  • 19. Wang Y (2010) Multichannel matching pursuit for seismic trace decomposition. Geophysics 75:V61–V66
  • 20. Yuan S, Wang S, Luo C, He Y (2015) Simultaneous multitrace impedance inversion with transform-domain sparsity promotion. Geophysics 80(2):R71–R80
  • 21. Yuan S, Ji Y, Shi P, Zeng J, Gao J, Wang S (2019) Sparse bayesian learning-based seismic high-resolution time-frequency analysis. IEEE Geosci Remote Sens Lett 16(4):623–627
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-0655e275-b338-4d25-9f7c-7f536422816d
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