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Volume 69, Issue 7
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

During ocean bottom seismic acquisition, seafloor multicomponent geophones located in rugose and sloping water bottom can be affected by skewed energy distribution, such as leaked shear energy on the vertical geophones and leaked compressional energy on the horizontal geophones. To correct for the tilted energy distribution, which is one of most effective preprocessing steps, a geophone reorientation step is applied. This is a simple and straightforward process that applies a 3‐dimensional rotation matrix with respect to the orientation angles. Since the reorientation process highly affects the outcome of the entire data processing workflow, it has to be accompanied by a careful quality control process to verify its validity for the whole survey area. In this study, we propose a quality control workflow for the geophone reorientation by using unsupervised machine learning. A correlation analysis is employed to compare numerical versus analytical solutions of both the azimuth and the incidence angles for the direct arrivals. A comparison of both solutions aims to generate correlation coefficients that are indicative of the accuracy of geophone orientation. The correlation coefficients are subsequently investigated by the ‐means clustering algorithm to differentiate and identify normally and abnormally deployed/reoriented geophones. Numerical experiments on a field ocean bottom seismic data set confirm that the proposed workflow effectively provides reliable labels for normally and abnormally deployed/reoriented geophones. The labelling assigned by the proposed quality control workflow is a suitable indicator for abnormalities in the geophone reorientation step and will be helpful for further investigation, such as re‐correction or removal of abnormally reoriented geophones.

© 2021 European Association of Geoscientists & Engineers © 2021 European Association of Geoscientists & Engineers
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/content/journals/10.1111/1365-2478.13127
2021-08-09
2024-04-26

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References

  1. Amini, A., Peng, H., Zhang, Z., Huang, R. and Yang, J. (2016) Joint inversion of water velocity and node position for ocean‐bottom node data. SEG Technical Program Expanded Abstracts, 5490–5494.
     [Google Scholar]
  2. Amunsen, L., Ikelle, L.T. and Martin, J. (2000) Multiple attenuation and P/S splitting of multicomponent OBC data at a heterogeneous sea floor. Wave Motion, 32, 67–78.
     [Google Scholar]
  3. Ankerst, M., Breunig, M.M., Kriegel, H.P. and Sander, J. (1999) OPTICS: Ordering points to identify the clustering structure. ACM SIGMOD international conference on Management of data, 49–60.
     [Google Scholar]
  4. Bagaini, C. and Petterson, F. (2017) Data‐driven multicomponent sensor orientation in the presence of acquisition gaps. 79th EAGE Annual International Meeting Extended Abstracts, 1–5.
     [Google Scholar]
  5. Barnes, E. and Laughlin, K.J. (2002) Investigation of methods for unsupervised classification of seismic data. SEG Technical Program Expanded Abstracts, 2221–2224.
     [Google Scholar]
  6. Barr, F.J. and Sanders, J.L. (1989) Attenuation of water‐column reverberation using pressure and velocity detectors in a water‐bottom cable. SEG Technical Program Expanded Abstracts, 653–656.
     [Google Scholar]
  7. Celik, T. (2009) Unsupervised change detection in satellite images using principal component analysis and k‐means clustering. IEEE Geoscience and Remote Sensing Letters, 6, 772–776.
     [Google Scholar]
  8. Dellinger, J., Clarke, R. and Gutowski, P. (2001) Horizontal vector infidelity correction by general linear transform. SEG Technical Program Expanded Abstracts, 865–868.
     [Google Scholar]
  9. Di, H., Shafiq, M. and AlRegib, G. (2018) Multi‐attribute k‐means clustering for salt‐boundary delineation from three‐dimensional seismic data. Geophysical Journal International, 215, 1999–2007.
     [Google Scholar]
  10. Ester, M., Kriegel, H.P., Sander, J. and Xu, X. (1996) A density‐based algorithm for discovering clusters in large spatial databases with noise. Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining, 226–231.
     [Google Scholar]
  11. Fjellanger, J.P., Boen, F., Ronning, K.J., Sabo, A. and Haaland, A.N. (2006) Seismic mapping and monitoring for well optimization. Offshore Technology Conference, OTC–18236–MS.
     [Google Scholar]
  12. Gaiser, J. (2003) Vector‐fidelity differences between P‐wave first breaks and PS‐wave reflections: implications for compensation of full‐azimuth data. 2003. SEG Technical Program Expanded Abstracts, 822–825.
     [Google Scholar]
  13. Galvis, I.S., Villa, Y., Duarte, C., Sieera, D. and Agudelo, W. (2017) Seismic attribute selection and clustering to detect and classify surface waves in multicomponent seismic data by using k‐mean algorithm. The Leading Edge, 36, 239–248.
     [Google Scholar]
  14. Huang, W. (2019) Seismic signal recognition by unsupervised machine learning. Geophysical Journal International, 219, 1163–1180.
     [Google Scholar]
  15. Jenkinson, T., Bansal, R., Martinez, A., Matheney, M., Khare, V. and Conrnaglia, V. (2010) Joint PP‐PS angle stack analysis and AVA inversion in Grane Field, offshore Norway. The Leading Edge, 29, 1228–1239.
     [Google Scholar]
  16. Jurkevics, A. (1988) Polarization analysis of three‐component array data. Bulletin of the Seismological Society of America, 78, 1725–1743.
     [Google Scholar]
  17. Li, J., Jin, S. and Ronen, S. (2004) Data‐driven tilt‐angle estimation of multicomponent receivers. SEG Technical Program Expanded Abstract, 921–924.
     [Google Scholar]
  18. Li, X.Y. and Yuan, J. (1999) Geophone orientation and coupling in three‐ component sea‐floor data: a case study. Geophysical Prospecting, 47, 995–1013.
     [Google Scholar]
  19. Liu, G., Li, C., Liu, X., Ge, Q. and Chen, X. (2018) Automatic stacking‐velocity estimation using similarity‐weighted clustering. Geophysical Prospecting, 66, 649–663.
     [Google Scholar]
  20. MacQueen, J.B. (1967) Some methods for classification and analysis of multivariate observations. Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
     [Google Scholar]
  21. Marroquín, I.D., Brault, J.‐J. and Hart, B.S. (2009a) A visual data‐mining methodology for seismic facies analysis: Part 1 — Testing and comparison with other unsupervised clustering methods. Geophysics, 74, P1–P11.
     [Google Scholar]
  22. Marroquín, I.D., Brault, J.‐J. and Hart, B.S. (2009b) A visual data‐mining methodology for seismic facies analysis: Part 2 — Application to 3D seismic data. Geophysics, 74, P13–P23.
     [Google Scholar]
  23. Olofsson, B., Massacand, C. and Millington, M. (2007) Determining 3C geophone orientation from a single seismic shot. SEG Technical Program Expanded Abstract, 970–974.
     [Google Scholar]
  24. Osen, A., Amunsen, L. and Reiten, A. (1999) Removal of water‐layer multiples from multicomponent sea‐bottom data. Geophysics, 64, 838–851.
     [Google Scholar]
  25. Oshida, A., Kubota, R., Nishiyama, E., Ando, J., Kasahara, J., Nishizawa, A., et al. (2008) A new method for determining OBS positions for crustal structure studies, using airgun shots and precise bathymetric data. Exploration Geophysics, 39, 15–25.
     [Google Scholar]
  26. Soubaras, R. (1996) Ocean bottom hydrophone and geophone processing. SEG Technical Program Expanded Abstract, 24–27.
     [Google Scholar]
  27. Soudani, M.T.A., Boelle, J.L., Hugonnet, P. and Grandi, A. (2006) 3D methodology for OBC pre‐processing. 68th EAGE Annual International Meeting Extended Abstracts, B044.
     [Google Scholar]
  28. Vaxelaire, D., Kravik, K., Bertini, F. and Mougenot, J.M. (2007) Wide azimuth 3D 4C OBC – a key breakthrough to lead to the development of Hild field. 69th EAGE Annual International Meeting Extended Abstracts, C009.
     [Google Scholar]
  29. Waheed, U.B., Alzahrani, S. and Hanafy, S.M. (2019) Machine learning algorithms for automatic velocity picking: K‐means vs DBSCAN. SEG Technical Program Expanded Abstracts, 5110–5114.
     [Google Scholar]
  30. Wang, Y., Bale, R., Grion, S. and Holden, J. (2010) The ups and downs of ocean‐bottom seismic processing: Applications of wavefield separation and up‐down deconvolution. The Leading Edge, 29, 1258–1265.
     [Google Scholar]
  31. Wrona, T., Pan, I., Gawthorpe, R.L. and Fossen, H. (2018) Seismic facies analysis using machine learning. Geophysics, 83, O83–O95.
     [Google Scholar]
  32. Xia, G., Clarke, R., Etgen, J., Kabir, N., Matson, K. and Michell, S. (2006) OBS multiple attenuation with application to the deepwater GOM Atlantis OBS nodes data. SEG Technical Program Expanded Abstract, 2654–2658.
     [Google Scholar]
  33. Xia, K., Hilterman, F. and Hu, H. (2018) Unsupervised machine learning algorithm for detecting and outlining surface waves on seismic shot gathers. Journal of Applied Geophysics, 157, 73–86.
     [Google Scholar]
  34. Zhang, P. and Lu, W. (2016) Automatic time‐domain velocity estimation based on an accelerated clustering method. Geophysics, 81, U13–U23.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.13127
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Quality control for the geophone reorientation of ocean bottom seismic data using k‐means clustering
Geophysical Prospecting 69, 1487 (2021); https://doi.org/10.1111/1365-2478.13127
/content/journals/10.1111/1365-2478.13127
/content/journals/10.1111/1365-2478.13127
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  • Article Type: Research Article
Keyword(s): Geophone reorientation; K‐means clustering; Ocean bottom node; Quality control; Seismic data processing

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