1887
Volume 16, Issue 3
  • ISSN: 1569-4445
  • E-ISSN: 1873-0604

Abstract

ABSTRACT

High‐resolution reflection seismics is a powerful tool that can provide the required resolution for subsurface imaging and monitoring in urban settings. Shallow seismic reflection data acquired in soil‐covered sites are often contaminated by source‐coherent surface waves and other linear moveout noises (LMON) that might be caused by, e.g., anthropogenic sources or harmonic distortion in vibroseis data. In the case of shear‐wave seismic reflection data, such noises are particularly problematic as they overlap the useful shallow reflections. We have developed new schemes for suppressing such surface‐wave noise and LMON while still preserving shallow reflections, which are of great interest to high‐resolution near‐surface imaging. We do this by making use of two techniques. First, we make use of seismic interferometry to retrieve predominantly source‐coherent surface waves and LMON. We then adaptively subtract these dominant source‐coherent surface waves and LMON from the seismic data in a separate step. We illustrate our proposed method using synthetic and field data. We compare results from our method with results from frequency–wave‐number (f‐k) filtering. Using synthetic data, we show that our schemes are robust in separating shallow reflections from source‐coherent surface waves and LMON even when they share very similar velocity and frequency contents, whereas f‐k filtering might cause undesirable artefacts. Using a field shear‐wave reflection dataset characterised by overwhelming LMON, we show that the reflectors at a very shallow depth can be imaged because of significant suppression of the LMON due to the application of the scheme that we have developed.

Loading

Article metrics loading...

/content/journals/10.3997/1873-0604.2018013
2018-04-01
2021-07-23
Loading full text...

Full text loading...

References

  1. BekaraM. and van der BaanM.2007. Local singular value decomposition for signal enhancement of seismic data. Geophysics72, V59–V65.
    [Google Scholar]
  2. BrouwerJ., GhoseR., HelbigK. and NijhofV.1997. The improvement of geotechnical subsurface models through the application of S‐wave reflection seismic exploration. Proceedings of the 3rd European Meeting of Environmental and Engineering Geophysics, pp. 103–106.
    [Google Scholar]
  3. DongS., HeR. and SchusterG.2006. Interferometric predcition and least squares subtraction of surface waves. SEG Technical Program Expanded Abstracts, 2783–2786.
    [Google Scholar]
  4. DraganovD., HellerK. and GhoseR.2012. Monitoring CO2 storage using ghost reflections retrieved from seismic interferometry. International Journal of Greenhouse Gas Control11, S35–S46.
    [Google Scholar]
  5. DraganovD., GhoseR., HellerK. and RuigrokE.2013. Monitoring of changes in velocity and Q in reservoirs using non‐physical arrivals in seismic interferometry. Geophysical Journal International192, 699–709.
    [Google Scholar]
  6. EckartC. and YoungG.1936. The approximation of one matrix by another of lower rank. Psychometrika1, 211–218.
    [Google Scholar]
  7. GhoseR., BrouwerJ. and NijhofV.1996. A portable S‐wave vibrator for high‐resolution imaging of the shallow subsurface. 58th EAGE Conference and Exhibition, Amsterdam, The Netherlands, June 3–7, 1996.
    [Google Scholar]
  8. GhoseR., NijhofV., BrouwerJ., MatsubaraY., KaidaY. and TakahashiT.1998. Shallow to very shallow, high‐resolution reflection seismic using a portable vibrator system. Geophysics63, 1295–1309.
    [Google Scholar]
  9. GhoseR.2002. High‐frequency shear wave reflections from shallow subsoil layers using a vibrator source; sweep cross‐correlation versus deconvolution with groundforce derivative. 72nd SEG annual international meeting, Expanded Abstracts, 1408–1411.
    [Google Scholar]
  10. GhoseR. and GoudswaardJ.2004. Integrating S‐wave seismic‐reflection data and cone penetration test data using a multiangle multiscale approach. Geophysics69, 440–459.
    [Google Scholar]
  11. GhoseR.2012. A microelectromechanical system digital 3C array seismic cone penetrometer. Geophysics77, WA99–WA107.
    [Google Scholar]
  12. GolubG. and van LoanC.1996. Matrix Computations. Baltimore, MD: The Johns Hopkins University Press.
    [Google Scholar]
  13. GuittonA. and VerschuurD.J.2004. Adaptive subtraction of multiples using the L1‐norm. Geophysical Prospecting52, 27–38.
    [Google Scholar]
  14. HallidayD., CurtisA., RobertssonJ. and van ManenD.2007. Interferometric surface‐wave isolation and removal. Geophysics72, A69–A73.
    [Google Scholar]
  15. HasbrouckW.P.1991. Four shallow‐depth, shear‐wave feasibility studeis. Geophysics56, 1875–1885.
    [Google Scholar]
  16. HaworthR.J.2003. The shaping of Sydney by its urban geology. Quaternary International103, 41–55.
    [Google Scholar]
  17. KingS. and CurtisA.2012. Suppressing nonphysical reflections in Green’s function estimates using source‐receiver interferometry. Geophysics77, Q15–Q25.
    [Google Scholar]
  18. KonstantakiL.A., GhoseR., DraganovD., DiaferiaG. and HeimovaaraT.2014. Characterization of a heterogeneous landfill using seismic and electrical resistivity data. Geophysics80, EN13–EN25.
    [Google Scholar]
  19. KonstantakiL.A., DraganovD., GhoseR. and HeimovaaraT.2015. Seismic interferometry as a tool for improved imaging of the heterogeneities in the body of a landfill. Journal of Applied Geophysics122, 28–39.
    [Google Scholar]
  20. KrawczykC., PolomU. and BeileckeT.2013. Shear‐wave reflection seismics as a valuable tool for near‐surface urban applications. The Leading Edge32, 256–263.
    [Google Scholar]
  21. MeloG., MalcolmA., MikesellT.D. and van WijkK.2013. Using SVD for improved interferometric Green’s function retrieval. Geophysical Journal International194(3), 1596–1612.
    [Google Scholar]
  22. MikesellD., van WijkK., CalvertA. and HaneyM.2009. Virtual refraction: useful spurious energy in seismic interferometry. Geophysics74, A13–A17.
    [Google Scholar]
  23. PuginA., LarsonT., SargentS., McBrideJ. and BexfieldC.2004. Near‐surface mapping using SH‐wave and P‐wave seismic land‐streamer data acquisition in Illinois, U.S. The Leading Edge23, 677–682.
    [Google Scholar]
  24. PullanS.E., HunterJ.A. and NeaveK.G.1990. Shallow shear‐wave reflection tests. 60th SEG annual international meeting, San Francisco, USA, Expanded Abstracts, 380–382.
    [Google Scholar]
  25. SinsakulS.2000. Late Quaternary geology of the Lower Central Plain, Thailand. Journal of Asian Earth Sciences18, 415–426.
    [Google Scholar]
  26. SniederR.2004. Extracting the Green’s function from the correlation of coda waves: a derivation based on stationary phase. Physical Review E69, 46610.
    [Google Scholar]
  27. ThorbeckeJ. and DraganovD.2011. Finite‐difference modeling experiments for seismic interferometry. Geophysics76, H1–H18.
    [Google Scholar]
  28. WapenaarK. and FokkemaJ.2006. Green’s function representations for seismic interferometry. Geophysics71, SI33–SI46.
    [Google Scholar]
  29. YilmazÖ.2001. Seismic data analysis: processing, inversion and interpretation of seismic data. SEG, USA.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.3997/1873-0604.2018013
Loading
/content/journals/10.3997/1873-0604.2018013
Loading

Data & Media loading...

  • Article Type: Research Article
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