1887

Abstract

Summary

This paper presents the theory and application behind Wavelet Assisted Constrained Least Squares Spectral Analysis, which is a hybrid mixed-model algorithmic approach for spectral decomposition. It combines a wavelet-based and a fixed-window constrained inversion Fourier based approach to estimate more accurate and reassigned time-frequency coefficients. The results from WACLSSA are compared with its constituent techniques and demonstrate that the spectrum obtained is more compact in terms of time and frequency standard deviations. They also show absence of ringing and tailed spectra dominant in the parent techniques. The results on real data also display improvements in vertical and horizontal resolution of seismic data, with the ability to isolate zones of interest better than its predecessors. WACLSSA also illuminates and highlights a 32ms thick layer at 22Hz frequency. The frequency is selected on the basis of the dominant frequency around the spectra corresponding to the seismic signal at the well location.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.2023101456
2023-06-05
2026-03-16
Loading full text...

Full text loading...

References

  1. Castagna, J.P., and Sun, S., [2006]. Comparison of spectral decomposition methods. EAGE First Break, 2006, v. 24, 75–79.
    [Google Scholar]
  2. Fomel, S., [2006]. Local Seismic attributes In: SEG New Orleans Annual Meeting. 1228–1232.
    [Google Scholar]
  3. Kirsch, A., [2011]. An Introduction to the Mathematical Theory of Inverse problems. Springer. pp.314
    [Google Scholar]
  4. Mahdavi, A., Kahoo, A.R., Radad, M. and Monfared, M.S., [2021]. Application of the local maximum synchrosqueezing transform for seismic data. Digital Signal Processing, 110, p.102934
    [Google Scholar]
  5. PantA., GhosalD. and Puryear, C.I., [2022]. Imaging a possible isolated carbonate build-up at anomalous low frequencies. Marine Geophysical Research. 43(4):1–14 DOI: 10.1007/s11001‑022‑09466‑0
    https://doi.org/10.1007/s11001-022-09466-0 [Google Scholar]
  6. PantA., GhosalD., PuryearC.I., [2020]. Comparative Study of Wavelet-based Recent Spectral Decomposition Algorithms for Seismic Signals. In: 82nd EAGE Annual Conference and Exhibition. pp. 1–4
    [Google Scholar]
  7. PantA., GhosalD., PuryearC.I., [2019]. Improved Reservoir Delineation in Complex Geologic Settings using CLSSA: A Case Study from offshore Nova Scotia. In: 81st EAGE Annual Conference and Exhibition. pp. 1–4 DOI: 10.3997/2214‑4609.201900687
    https://doi.org/10.3997/2214-4609.201900687 [Google Scholar]
  8. Puryear, C., Portniaguine, O., Cobos, C., Castagna, J.P., [2012]. Constrained Least Squares Spectral Analysis: Application to Seismic Data, Geophysics, v. 75(5), V143–167
    [Google Scholar]
  9. Sinha, S., Routh, P.S., Anno, P.D., Castagna, J.P., [2005]. Spectral decomposition of seismic data with continuous-wavelet transform. Geophysics, v. 70(6), P19–P25.
    [Google Scholar]
  10. Tary, J.B., Herrera, R.H., Han, J., van der Baan, M., [2014]. Spectral Estimation- What is new? What is next?. AGU Reviews of Geophysics, v. 52, 723–749.
    [Google Scholar]
  11. TorrenceC., Compo, G.P., 1998. A Practical Guide to Wavelet Analysis. Bulletin of the American Meteorological Society, pp. 61–78.
    [Google Scholar]
  12. Rayner, J.N., [2001]. Spectral Analysis. International Journal of the Social and Behavioral Sciences, 2001, ISBN:0-08-043076-7, 14861–14864.
    [Google Scholar]
  13. Wu, L., Castagna, J.P., Oyem, A., [2017]. Quantitative Resolution Analysis for Spectral Decomposition Using Regularized Inversion. In: SEG International Exposition and 87th Annual Meeting, pp. 3123–3127.
    [Google Scholar]
/content/papers/10.3997/2214-4609.2023101456
Loading
/content/papers/10.3997/2214-4609.2023101456
Loading

Data & Media loading...

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