1887
  1. Home
  2. A-Z Publications
  3. Geophysical Prospecting
  4. Volume 63, Issue 1
  5. Article
Volume 63 Number 1
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

In this paper, we discuss high‐resolution coherence functions for the estimation of the stacking parameters in seismic signal processing. We focus on the Multiple Signal Classification which uses the eigendecomposition of the seismic data to measure the coherence along stacking curves. This algorithm can outperform the traditional semblance in cases of close or interfering reflections, generating a sharper velocity spectrum. Our main contribution is to propose complexity‐reducing strategies for its implementation to make it a feasible alternative to semblance. First, we show how to compute the multiple signal classification spectrum based on the eigendecomposition of the temporal correlation matrix of the seismic data. This matrix has a lower order than the spatial correlation used by other methods, so computing its eigendecomposition is simpler. Then we show how to compute its coherence measure in terms of the signal subspace of seismic data. This further reduces the computational cost as we now have to compute fewer eigenvectors than those required by the noise subspace currently used in the literature. Furthermore, we show how these eigenvectors can be computed with the low‐complexity power method. As a result of these simplifications, we show that the complexity of computing the multiple signal classification velocity spectrum is only about three times greater than semblance. Also, we propose a new normalization function to deal with the high dynamic range of the velocity spectrum. Numerical examples with synthetic and real seismic data indicate that the proposed approach provides stacking parameters with better resolution than conventional semblance, at an affordable computational cost.

Copyright © 2015 European Association of Geoscientists & Engineers © 2014 European Association of Geoscientists & Engineers
Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12164
2014-11-03
2024-04-19

  • From This Site

    /content/journals/10.1111/1365-2478.12164
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Abbad, B. and Ursin, B.2012. High‐resolution bootstrapped differential semblance. Geophysics77, U39–U47.
     [Google Scholar]
  2. Asgedom, E.G., Gelius, L.G. and Tygel, M.2011. Higher resolution determination of zero‐offset common‐reflexion‐surface stack parameters. International Journal of Geophysics.
     [Google Scholar]
  3. Biondi, B. and Kostov, C.1989. High‐resolution velocity spectra using eigenstructure methods. Geophysics54, 832–842.
     [Google Scholar]
  4. Dix, C.H.1955. Seismic velocities from surface measurements. Geophysics20, 68–86.
     [Google Scholar]
  5. Golub, B.H. and Van Loan, C.F.1996. Matrix Computations, 3rd ed. The Johns Hopkins University Press.
     [Google Scholar]
  6. Jakobsson, A., Swindlehurst, A.L. and Stoica, P.1998. Subspace‐based estimation of time delays and Doppler shifts. IEEE Transactions on Signal Processing46, 2472–2483.
     [Google Scholar]
  7. Kirlin, R.L.1992. The relationship between semblance and eigenstructure velocity estimators. Geophysics57, 1027–1033.
     [Google Scholar]
  8. Kirlin, R.L. and Done, W.D.1999. Covariance Analysis for Seismic Signal Processing, 1st ed. Society of Exploration Geophysicists.
     [Google Scholar]
  9. Mayne, W.H.1962. Common reflection point horizontal data stacking techniques. Geophysics27, 927–938.
     [Google Scholar]
  10. Neidell N. and Taner M. 1971. Semblance and other coherency measures for multichannel data. Geophysics 36, 482–497.
  11. Schmidt, R.O.1986. Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas and Propagation34, 276–280.
     [Google Scholar]
  12. Shan, T.J., Wax, M. and Kailath, T.1985. On spatial smoothing for direction‐of‐arrival estimation of coherent signals. IEEE Transactions on Acoustics, Speech and Signal Processing33, 806–811.
     [Google Scholar]
  13. Taner, M. and Koehler, F.1969. Velocity spectra digital computer derivation and applications of velocity functions. Geophysics34, 859–881.
     [Google Scholar]
  14. Wang, Y.Y., Chen, J.D. and Fang, W.H.2001. TST‐MUSIC for joint DOA‐delay estimation. IEEE Transactions on Signal Processing49, 721–729.
     [Google Scholar]
  15. Willians, R.T., Prasad, S., Mahalanabis, A.K. and Sibul, L.H.1988. An improved spatial smoothing technique for bearing estimation in a multipath environment. IEEE Transactions on Acoustics, Speech and Signal Processing36, 425–432.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12164
Loading
Implementation aspects of eigendecomposition‐based high‐resolution velocity spectra
Geophysical Prospecting 63, 99 (2015); https://doi.org/10.1111/1365-2478.12164
/content/journals/10.1111/1365-2478.12164
/content/journals/10.1111/1365-2478.12164
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Coherence; Signal processing; Velocity analysis

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