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Acta Geophysica

, Volume 67, Issue 2, pp 577–587 | Cite as

The influence of the frequency-dependent spherical-wave effect on the near-surface attenuation estimation

  • Yongzhen JiEmail author
  • Shangxu Wang
  • Sanyi Yuan
  • Binpeng Yan
Research Article - Applied Geophysics
  • 103 Downloads

Abstract

The knowledge of Q is desirable for improving seismic resolution, facilitating amplitude analysis and seismic interpretation. The most commonly used methods for Q estimation are the frequency-spectrum-based methods. Generally, these methods are based on the plane wave theory assuming that the transmission/reflection loss is frequency independent. This assumption is reasonable in the far-field situation and makes the transmission/reflection coefficient irrelevant with the Q estimation result. However, in the near-surface context, this assumption is invalid because the seismic wave propagates in the form of spherical wave in the real seismic surveys and the spherical-wave transmission/reflection coefficient is frequency dependent. As a result, deviation will exist. In this paper, the influence of the spherical-wave effect on the Q estimation in the near-surface context was proved in both synthetic data and field data for the first time, and it was found that the deviation due to the spherical-wave effect is of order comparable to the intrinsic attenuation. The compensation method based on the forward modeling is then proposed to correct this deviation, and the effectiveness of the proposed method is proved by the reasonable estimated results of both synthetic data and field data example. These results raise caution for the interpretation of the extracted Q in the near-surface context if they do not account for the spherical-wave effect and point to the necessity of incorporating a frequency-dependent term in the frequency-spectrum-based method when applied to the Q estimation in the near surface.

Keywords

Spherical-wave effect Attenuation estimation Near-field Frequency-spectrum-based method 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (41674127 and 41304108), the National Key Basic Research Development Program (2013CB228600), the Major Scientific Research Program of Petrochina Science and Technology Management Department “Comprehensive Seismic Prediction Software Development and Applications of Natural Gas” (2016B-0603).

References

  1. Aki KT, Richards PG (1980) Quantitative seismology: theory and methods. W. H. Freeman, New YorkGoogle Scholar
  2. Alulaiw B, Gurevich B (2013) Analytic wavefront curvature correction to plane-wave reflection coefficient for a weak contrast interface. Geophys Prospect 61(1):53–63CrossRefGoogle Scholar
  3. Beckwith J, Clark R, Hodgson L (2016) Estimating frequency-dependent attenuation quality factor values from prestack surface seismic data. Geophysics 82(1):O11–O22CrossRefGoogle Scholar
  4. Bendeck CDC, Clark RA, Booth AD, Wills W (2017) Constant and frequency-dependent attenuation from vertical seismic profiles in fractured granite and thinly layered sediment. In: 79th EAGE conference and exhibitionGoogle Scholar
  5. Dasgupta R, Clark RA (1998) Estimation of Q from surface seismic reflection data. Geophysics 63(6):2120–2128CrossRefGoogle Scholar
  6. Gurevich B, Pevzner R (2015) How frequency dependency of Q affects spectral ratio estimates. Geophysics 80(2):A39–A44CrossRefGoogle Scholar
  7. Haase AB, Stewart RR (2010) Near-field seismic effects in a homogeneous medium and their removal in vertical seismic profile attenuation estimates. Geophys Prospect 58:1023–1032Google Scholar
  8. Hao Y, Wen X, Zhang B, He Z, Zhang R, Zhang J (2016) Q estimation of seismic data using the generalized S-transform. J Appl Geophys 135:122–134CrossRefGoogle Scholar
  9. Li F, Zhou H, Jiang N, Bi J, Marfurt KJ (2015) Q estimation from reflection seismic data for hydrocarbon detection using a modified frequency shift method. J Geophys Eng 12(4):577–586CrossRefGoogle Scholar
  10. Li F, Zhou H, Zhao T, Marfurt KJ (2016a) Unconventional reservoir characterization based on spectrally corrected seismic attenuation estimation. J Seism Explor 25(5):447–461Google Scholar
  11. Li G, Sacchi MD, Zheng H (2016b) situ evidence for frequency dependence of near-surface Q. Geophys J Int 204(2):1308–1315CrossRefGoogle Scholar
  12. Li JN, Wang SX, Dong CH, Yuan SY, Wang JB (2016c) Study on frequency-dependent characteristics of spherical-wave PP reflection coefficient. Chin J Geophys 59(10):3810–3819Google Scholar
  13. Li JN, Wang SX, Wang JB, Dong CH, Yuan SY (2017) Frequency-dependent spherical-wave reflection in acoustic media: analysis and inversion. Pure appl Geophys 174(4):1759–1778CrossRefGoogle Scholar
  14. Mangriotis MD, Rector JW III, Herkenhoff EF (2011) Case History Effects of the near-field on shallow seismic studies. Geophysics 76(1):B9–B18CrossRefGoogle Scholar
  15. Matsushima J (2006) Seismic wave attenuation in methane hydrate-bearing sediments: vertical seismic profiling data from the Nankai Trough exploratory well, offshore Tokai, central Japan. J Geophys Res Solid Earth.  https://doi.org/10.1029/2005JB004031 Google Scholar
  16. Matsushima J, Ali MY, Bouchaala F (2015) Seismic attenuation estimation from zero-offset VSP data using seismic interferometry. Geophys J Int 204(2):1288–1307CrossRefGoogle Scholar
  17. Montano M, Lawton DC, Margrave G (2015) Near-surface Q estimation: an approach using the up-going wave-field in vertical seismic profile data. In: SEG technical program expanded abstracts, pp 5600–5604Google Scholar
  18. Quan Y, Harris JM (1997) Seismic attenuation tomography using the frequency shift method. Geophysics 62(3):895–905CrossRefGoogle Scholar
  19. Robinson EA (1984) Seismic inversion and deconvolution. Geophysical Press, MarbellaGoogle Scholar
  20. Sommerfeld A (1909) Über die Ausbreitung der Wellen in der drahtlosen Telegraphie. Ann Phys 333(4):665–736CrossRefGoogle Scholar
  21. Tao YH, Wang SX, Li JN, Dong CH, Yuan SY, Chen G (2016) The effect of frequency-dependent reflection coefficient on seismic attenuation estimation. In: 78th EAGE conference and exhibition 2016Google Scholar
  22. Tonn R (1991) The determination of the seismic quality factor Q from VSP data: a comparison of different computational methods. Geophys Prospect 39(1):1–27CrossRefGoogle Scholar
  23. Wehner D, Landrø M, Amundsen L, Westerdahl H (2018) Frequency-depth-dependent spherical reflection response from the sea surface—a transmission experiment. Geophys J Int 214(2):1206–1217CrossRefGoogle Scholar
  24. Weyl H (1919) Ausbreitung elektromagnetischer Wellen ueber einem ebenen Leiter. Ann Phys 365(21):481–500CrossRefGoogle Scholar
  25. 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):6182–6194CrossRefGoogle Scholar
  26. Yuan SY, Liu JW, Wang SX, Wang TY, Shi PD (2018) Seismic waveform classification and first-break picking using convolution neural networks. IEEE Geosci Remote Sens Lett 15(2):272–276CrossRefGoogle Scholar
  27. Yuan SY, Liu Y, Zhang Z, Luo CM, Wang SX (2019) Prestack stochastic frequency-dependent velocity inversion with rock-physics constraints and statistical associated hydrocarbon attributes. IEEE Geosci Remote Sens Lett 16(1):140–144CrossRefGoogle Scholar
  28. Zhang C, Ulrych TJ (2002) Estimation of quality factors from CMP records. Geophysics 67(5):1542–1547CrossRefGoogle Scholar

Copyright information

© Institute of Geophysics, Polish Academy of Sciences & Polish Academy of Sciences 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Petroleum Resources and Prospecting, CNPC Key Laboratory of Geophysical ExplorationChina University of Petroleum (Beijing)BeijingChina
  2. 2.Sinopec Geophysical Research InstituteNanjingChina

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