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This study analyzed the uncertainty of inversion and the resolution limit in the presence of noise by means of statistical experiments. The exhaustive method is adopted to obtain the global optimal solution in each experiment. We found that even with small level of noise, solutions fluctuate in a large range for the thin bed. The distribution of solutions in the presence of noise is closely related to the spread of the cost function in the absence of noise. As a result, the area of a certain neighborhood around the true solution on the spread of the cost function in the absence of noise is used to evaluate the uncertainty of inversion and the resolution limit in the presence of noise. In the case that the SNR (signal-to-noise ratio) is 5 in this study, solutions focus around the true solution with a very small uncertainty only when the bed thickness is greater than the reciprocal of the double predominant frequency of the convoluting wavelet.
Słowa kluczowe
Wydawca
Czasopismo
Rocznik
Tom
Strony
1011--1018
Opis fizyczny
Bibliogr. 15 poz.
Twórcy
autor
- .China University of Petroleum Beijing, State Key Laboratory of Petroleum Resources and Prospecting CNPC Key Laboratory of Geophysical Prospecting Beijing China
autor
- .China University of Petroleum Beijing, State Key Laboratory of Petroleum Resources and Prospecting CNPC Key Laboratory of Geophysical Prospecting Beijing China
autor
- .China University of Petroleum Beijing, State Key Laboratory of Petroleum Resources and Prospecting CNPC Key Laboratory of Geophysical Prospecting Beijing China
Bibliografia
- 1. Chung H, Lawton DC (1995) Frequency characteristics of seismic re- flections from thin beds. Can J Explor Geophys 31(1–2):32–37
- 2. Jenkins FA, White HE (1957) Fundamentals of optics. McGraw Hill publishing Co., New York
- 3. Kallweit RS, Wood LC (1982) The limits of resolution of zero-phase wavelets. Geophysics 47(7):1035–1046
- 4. Luo CM, Wang SX, Yuan SY (2014) Effect of inaccurate wavelet phase on prestack waveform inversion. Appl Geophys 11(4):479–488
- 5. Puryear CI, Castagna JP (2008) Layer-thickness determination and stratigraphic interpretation using spectral inversion: theory and application. Geophysics 73(2):R37–R48
- 6. Ricker N (1953) Wavelet contraction, wavelet expansion and the control of seismic resolution. Geophysics 18(4):769–792
- 7. Tirado S (2004) Sand thickness estimation using spectral decomposition: MS. University of Oklahoma, Thesis
- 8. Widess MB (1973) How thin is a thin bed? Geophysics 38(6):1176–1180
- 9. Yuan SY, Wang SX, Ma M, Ji YZ, Deng L (2017) Sparse Bayesian learning-based time-variant deconvolution. IEEE Trans Geosci Remote Sens 55(11):6182–6194
- 10. Yuan SY, Wang SX, Luo CM, Wang TY (2018a) Inversion-based 3-D seismic denoising for exploring spatial edges and spatio-temporal signal redundancy. IEEE Geosci Remote Sens Lett. https://doi.org/10.1109/lgrs.2018.2854929
- 11. Yuan SY, Wang SX, Yuan FF, Liu Y (2018b) The influence of errors in the source wavelet on inversion-based surface-related multiple attenuation. Geophys Prospect 66:55–73
- 12. Zeng H (2009) How thin is a thin bed? An alternative perspective. Lead Edge 28(10):1192–1197
- 13. Zhang R, Castagna JP (2011) Seismic sparse-layer reflectivity inversion using basis pursuit decomposition. Geophysics 77(5):V143–V167
- 14. Zong Z, Yin X, Wu G (2015a) Complex pre-stack amplitude inversion for P-wave and S-wave quality factors. Geophys J Int 202(1):564–577
- 15. Zong Z, Yin X, Wu G (2015b) Geofluid discrimination incorporating poroelasticity and seismic reflection inversion. Surv Geophys 36(5):659–681
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-1b55c7d9-1b61-4a72-b7e4-aacc06da5615