1887
Volume 17, Issue 5
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

When the depth of the shallow three‐dimensional seismic exploration is less than 100 m, one often encounters very low velocities for the target and high frequencies in the data. Following Nyquist–Shannon sampling theorem, the permissible maximum receiver interval can be smaller in this case compared to relatively deeper seismic exploration. This suggests that there are still issues to be addressed in the design of geometry and in data processing in shallow three‐dimensional seismic exploration. This paper addresses these problems by applying the theory of compressed sensing for signal processing to shallow‐seismic geometry designing and data processing. Theoretical research shows that random sampling of data can better reconstruct the wavefield than undersampled data. Random sampled data can transform the coherent aliasing to non‐coherent noise, which turns the seismic data interpretation problem into a data denoising problem. The jittered random sampling method avoids the situation when the spatial data points of a randomly sampled dataset are too concentrated or too sparse. Our proposed approach was tested on simulated and real seismic data. The results show that if the jittered random undersampling method is used in shallow three‐dimensional seismic data acquisition, then a wider range of observation with fewer receivers in the layout is possible. This greatly improves the data collection efficiency in the field. In addition, the random sampling method has more flexibility in the field environment. When using the regular sampling method, an open survey area without large obstacles is needed. However, the random sampling method can be adapted to rugged terrains. When obstacles are encountered, the receiver spacing can be increased appropriately. In open areas, the receiver spacing can be decreased to compensate for the reduced data.

Loading

Article metrics loading...

/content/journals/10.1002/nsg.12063
2019-09-04
2024-04-16
Loading full text...

Full text loading...

References

  1. AbmaR. and KabirN.2006. 3D interpolation of irregular data with a POCS algorithm. Geophysics71, E91–E97.
    [Google Scholar]
  2. DonohoL.L.2006. Compressed sensing. IEEE Transactions on Information Theory52, 1289–1306.
    [Google Scholar]
  3. GuoD.L., ZhangT.J. and DaiX.H.2012. Method of signal frequency, amplitude and phase measurement based on non‐uniform sampling. Systems Engineering and Electronics34, 662–665.
    [Google Scholar]
  4. HennenfentG. and HerrmannF.J.2008. Simply denoise: wavefield reconstruction via jittered undersampling. Geophysics73, V19–V28.
    [Google Scholar]
  5. HerrmannF.J.2009. Sub‐Nyquist sampling and sparsity: How to get more information from fewer samples. SEG Technical Program Expanded Abstracts, 3410–3415.
  6. LiP., GuH.M., MaS.M. and DongX.2015. Marine towed streamer random sampling of spectral analysis. Geological Science and Technology Information34, 179–184.
    [Google Scholar]
  7. LustigM., DonohoD.L. and PaulyJ.M.2007. Sparse MRI: The application of compressed sensing for rapid MR imaging. Magnetic Resonance in Medicine58, 1182–1195.
    [Google Scholar]
  8. MoldoveanuN.2010. Random sampling: a new strategy for marine acquisition. SEG Technical Program Expanded Abstracts 2010, 51–55.
  9. ShiZ.J., TianG., ZhaoW.K. and WangZ.H.2013. Application on ultra‐shallow 3D seismic exploration technology. Journal of Zhejiang University (Engineering Science)47, 912–917.
    [Google Scholar]
  10. WangA.M., WangS. and ChenM.X.2005. A novel spectrum analysis method for nonuniform periodically sampling. Signal Processing21, 240–243.
    [Google Scholar]
  11. XiongY.H., ZhangJ.Q. and LiuR.Z.2013. A study of the application of 3D shallow seismic exploration to taocha. Chinese Journal of Engineering Geophysics10, 227–235.
    [Google Scholar]
  12. ZhouC.X. and SchusterG.T.1995. Quasi‐random migration of 3‐D field data. SEG Technical Program Expanded Abstracts 1995, 1145–1148.
  13. ZhaoY.J.2012. Study on Compressive Sampling and Recovery Algorithm of Sparse Analog Signal. University of Electronic Science and Technology of China.
    [Google Scholar]
  14. ZhangH. and ChenX.H.2013. Seismic data reconstruction based on jittered sampling and curvelet transform. Chinese Journal of Geophysics56, 1637–1649.
    [Google Scholar]
  15. ZwartjesP.M. and SacchiM.D.2007. Fourier reconstruction of nonuniformly sampled, aliased data. Geophysics72, V21–V32.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1002/nsg.12063
Loading
/content/journals/10.1002/nsg.12063
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): 3D; Data processing; Migration; Near surface; Seismic

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error