1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. 79th EAGE Conference and Exhibition 2017
  5. Conference paper

Abstract

Summary

We present a recursive-iterative Singular Spectrum Analysis (RI-SSA) algorithm which explores the time-correlation between reflected events. The RI-SSA algorithm depends on the first eigenimage of the SVD of the data matrix only. It is formed by letting each column be the data vector shifted one place down. The first eigenimage is related to the part of the signal with most strong correlation along the time variable and may be transformed to a time signal, which mainly consists of the low-frequency part of the input signal. We show that this corresponds to filtering the data with a symmetric zero-phase filter, which is the autocorrelation of the first eigenvector associated to the time variable. The computational implementation may be done using the power-method in a recursive scheme, increasing the order of the data matrix, by increasing the number of shifted traces. This improves the separation of the input data in a low-frequency and high-frequency component. This separation may be further improved by adding iterations. The output of the RI-SSA algorithm is the low and high frequency part of the signal. We illustrate the effectiveness of this new approach to the prediction and subtraction of the ground-roll.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201701182
2017-06-12
2024-04-19

  • From This Site

    /content/papers/10.3997/2214-4609.201701182
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Bekara, M. and Baan, M.V.
    [2007] Local singular value decomposition for signal enhancement of seismic data. Geophysics72, 59–65.
     [Google Scholar]
  2. Cheng, J. and Sacchi, M.
    [2016] Fast and memory-efficient singular spectrum analysis for seismic data reconstruction and denoising. SEG Technical Program Expanded Abstracts 2016: pp. 4064–4068. doi: 10.1190/segam2016–13955076.1
     https://doi.org/10.1190/segam2016–13955076.1 [Google Scholar]
  3. Freire, S.L.M. and Ulrych, T.J.
    [1988] Application of singular value decomposition to vertical seismic profiling. Geophysics53, 778–785.
     [Google Scholar]
  4. Golub, G., and Van Loan, C.F.
    [1996] Matrix computations, 3rd ed.: The Johns Hopkins University Press.
     [Google Scholar]
  5. Golyandina, N. and Zhigljavsky, A.
    [2013] Single spectrum analysis for time series. Springer.
     [Google Scholar]
  6. Liu, X.
    , [1999] Ground roll suppression using the Karhunen-Loeve transform. Geophysics64, 564–566.
     [Google Scholar]
  7. Manenti, R. and Porsani, M.J.
    , [2016] Spectral whitening using single-trace singular value decom-position applied to vibroseis data, SEG, 86th Annual Meeting, Dallas, Expanded Abstract, 4825–4829.
     [Google Scholar]
  8. Oropeza, V., and Sacchi, M.
    [2011] Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis: Geophysics, 76, no. 3, V25–V32, http://dx.doi.org/10.1190/L3552706.
     [Google Scholar]
  9. Porsani, M.J., Ursin, B., Silva, M.G., and Melo, P.E.M.
    [2013] Dip-adaptive singular-value decomposition filtering for seismic reflection enhancement: Geophysical Prospecting, 61, 42–52, doi: 10.1111/j.1365–2478.2012.01059.x
     https://doi.org/10.1111/j.1365–2478.2012.01059.x [Google Scholar]
  10. Silva, M.G., Porsani, M.J., and Ursin, B.
    , [2016] A single-trace singular value decomposition method with application to the ground-roll removal, SEG, 86th Annual Meeting, Dallas, Expanded Abstract, 4659–4663.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201701182
Loading
Recursive-Iterative Zero-Phase Filtering Via Singular Spectrum Analysis
Conference Proceedings 2017, 1 (2017); https://doi.org/10.3997/2214-4609.201701182
/content/papers/10.3997/2214-4609.201701182
/content/papers/10.3997/2214-4609.201701182
Loading

Data & Media loading...

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