1887
Volume 63 Number 4
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Seismic data from crystalline or hardrock environments usually exhibit a poor signal‐to‐noise ratio due to low impedance contrasts in the subsurface. Moreover, instead of continuous reflections, we observe a lot of steeply dipping events resembling parts of diffractions. The conventional seismic processing (common midpoint stack and dip moveout) is not ideally suited for imaging such type of data. Common‐reflection‐surface stack processing considers more traces during the stack than common midpoint processing, and the resulting image displays a better signal‐to‐noise ratio. In the last decade, the common‐reflection‐surface stack method was established as a powerful tool to provide improved images, especially for low‐fold or noise‐contaminated data. The common‐reflection‐surface stack and all attributes linked to it are obtained using a coherence‐based automatic data‐driven optimization procedure. In this work we applied the common‐reflection‐surface stack workflow to 3D crystalline rock seismic data, which were acquired near Schneeberg, Germany, for geothermal exploration. The common‐reflection‐surface stack itself provided an image of good signal‐to‐noise ratio. However, for data from environments with low acoustic impedance and poor velocity information, coherence, which is automatically obtained in the optimization procedure, provides an alternative way to image the subsurface. Despite the reduced resolution, for these data, the coherence image provided the best results for an initial analysis. Utilized as a weight, the coherence attribute can be used to further improve the quality of the stack. By combining the benefits of a decreased noise level with the high‐resolution and high‐interference properties of waveforms, we argue that these results may provide the best images in an entirely data‐driven processing workflow for the Schneeberg data.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12282
2015-06-15
2024-04-16
Loading full text...

Full text loading...

References

  1. BaykulovM., DümmongS. and GajewskiD.2011. From time to depth with CRS attributes. Geophysics76, S151–S155.
    [Google Scholar]
  2. BaykulovM. and GajewskiD.2009. Prestack seismic data enhancement with partial common‐reflection‐surface (CRS) stack. Geophysics74, V49–V58.
    [Google Scholar]
  3. BergerH.‐J., FelixM., GörneS., KochE., KrentzO., FörsterA.et al. 2011. Tiefengeothermie Sachsen . Landesamt für Umwelt, Landwirtschaft und Geologie. Schriftenreihe, No. 9.
  4. BerglerS., HubralP., MarchettiP., CristiniA. and CardoneG.2002. 3D common‐reflection‐surface stack and kinematic wavefield attributes. The Leading Edge21, 1010–1015.
    [Google Scholar]
  5. DellS. and GajewskiD.2011. Common‐reflection‐surface‐based workflow for diffraction imaging. Geophysics76, S187–S195.
    [Google Scholar]
  6. DellS., GajewskiD. and VanelleC.2012. Prestack time migration by common‐migrated‐reflector‐element stacking. Geophysics77, S73–S82.
    [Google Scholar]
  7. DümmongS. and GajewskiD.2008. A multiple suppression method via CRS attributes. 2008 SEG Expanded Abstracts2531–2535.
  8. DuveneckE.2004. Velocity model estimation with data‐derived wavefront attributes. Geophysics69, 265–274.
    [Google Scholar]
  9. EmmermannR. and WohlenbergJ.1988. German Continental Deep Drilling Program (KTB). Springer.
    [Google Scholar]
  10. HertweckT., SchleicherJ. and MannJ.2007. Data stacking beyond CMP. The Leading Edge26, 818–827.
    [Google Scholar]
  11. HubralP.1983. Computing true amplitude reflections in a laterally inhomogeneous earth. Geophysics48, 1051–1062.
    [Google Scholar]
  12. LüschenE., GörneS., von HartmannH., ThomasR. and SchulzR.2015. 3D seismic survey for geothermal exploration in crystalline rocks in Saxony, Germany. Geophysical Prospecting63(4), 984–998.
    [Google Scholar]
  13. MalehmirA., DurrheimR., BellefleurG., UrosevicM., JuhlinC., WhiteD.J.et al. 2012. Seismic methods in mineral exploration and mine planning: a general overview of past and present case histories and a look into the future. Geophysics77, WC173–WC190.
    [Google Scholar]
  14. MannJ., JägerR., MüllerT. and HubralP.1999. Common‐reflection‐surface‐stack ‐ a real data example. Journal for Applied Geophysics42, 283–300.
    [Google Scholar]
  15. MilkereitB., BerrerE.K., KingA.R., WattsA.H., RobertsB., AdamE.et al. 2000. Development of 3‐D seismic exploration technology for deep nickel‐copper deposits‐A case history from the Sudbury basin, Canada. Geophysics65, 1890–1899.
    [Google Scholar]
  16. MüllerN.A.2003. The 3D common‐reflection‐surface stack — Theory and application. Master's thesis, University of Karlsruhe.
  17. SchwarzB., VanelleC., GajewskiD. and KashtanB.2014. Curvatures and inhomogeneities: an improved common‐reflection‐surface approach. Geophysics79, S231–S240.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12282
Loading
/content/journals/10.1111/1365-2478.12282
Loading

Data & Media loading...

  • Article Type: Research Article

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