1887
  1. Home
  2. A-Z Publications
  3. Geophysical Prospecting
  4. Volume 72, Issue 1
  5. Article
Volume 72 Number 1
  • E-ISSN: 1365-2478
PDF

Abstract

Abstract

Ghost reflections from the free surface distort the source signature and generate notches in the seismic amplitude spectrum. For this reason, removing ghost reflections is essential to improve the bandwidth and signal‐to‐noise ratio of seismic data. We have developed a novel approach that involves training a convolutional neural network to remove source and receiver ghosts from marine dual‐component data. High‐quality training data is essential for the network to produce accurate predictions on real data. We have used the demigration of a stacked depth‐migrated image to create training shot gathers. Demigrated pressure and vertical velocity data are used to train the network. We apply the trained network on real pressure and vertical velocity data with ghosts. The network's output may be either source deghosting and receiver deghosting, or both. We test our method on synthetic Marmousi and real North Sea data with dual‐component streamers. The method is compared with conventional dual‐component deghosting using the summation of pressure and vertical velocity. Results show that the method can accurately remove the ghosts with only minor errors in synthetic data. Based on a decimation test, the method is less affected by spatially aliased data than a conventional method, which could benefit data with high frequencies and/or large receiver or cable separations. On real data, the results show consistency with conventional deghosting, both within and outside the training area. This indicates that the method is a viable alternative to conventional methods on real data.

© 2024 European Association of Geoscientists & Engineers. © 2023 CGG Services Norway AS. Geophysical Prospecting published by John Wiley & Sons Ltd on behalf of European Association of Geoscientists & Engineers.
Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.13407
2023-12-18
2025-04-26

  • From This Site

    /content/journals/10.1111/1365-2478.13407
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/gpr/72/1/gpr13407.html?itemId=/content/journals/10.1111/1365-2478.13407&mimeType=html&fmt=ahah

References

  1. Almuteri, K. & Sava, P. (2021) A convolutional neural network approach for ghost removal Seismic deghosting using CNNs. In: First international meeting for applied geoscience & energy expanded abstracts. SEG. pp. 2550–2554.
  2. Amundsen, L., Zhou, H., Reitan, A. & Weglein, A.B. (2013) On seismic deghosting by spatial deconvolution. Geophysics, 78(6), V267–V271. https://doi.org/10.1190/GEO2013‐0198.1
     [Google Scholar]
  3. Amundsen, L., Weglein, A.B. & Reitan, A. (2013) On seismic deghosting using integral representation for the wave equation: use of Green's functions with Neumann or Dirichlet boundary conditions. Geophysics, 78(4), T89–T98. https://doi.org/10.1190/GEO2012‐0305.1
     [Google Scholar]
  4. Amundsen, L. & Zhou, H. (2013) Low‐frequency seismic deghosting. Geophysics, 78(2), WA15–WA20. https://doi.org/10.1190/GEO2012‐0276.1
     [Google Scholar]
  5. Aytun, K. (1999) The footsteps of the receiver ghost in the f‐k domain. Geophysics, 64(5), 1618–1626. https://doi.org/10.1190/1.1444666
     [Google Scholar]
  6. Bearnth, R.E. & Moore, N.A. (1989) Air gun‐slant cable seismic results in the Gulf of Mexico. In: SEG technical program expanded abstracts. SEG. pp. 649–652. https://doi.org/10.1190/1.1889556
  7. Bleistein, N. (1987) On the imaging of reflectors in the earth. Geophysics, 52(7), 931–942. https://doi.org/10.1190/1.1442363
     [Google Scholar]
  8. Bleistein, N., Stockwell, J. & Cohen, J.K. (2001) Mathematics of multidimensional seismic imaging, migration, and inversion. New York, NY: Springer New York. https://doi.org/10.1007/978‐1‐4613‐0001‐4
     [Google Scholar]
  9. Brittan, J., Martin, T., Bekara, M. & Koch, K. (2011) 3D shallow water demultiple – extending the concept. First Break, 29(9), 97–101. https://doi.org/10.3997/1365‐2397.29.9.53730
     [Google Scholar]
  10. Carlson, D., Long, A., Söllner, W., Tabti, H., Tenghamn, R. & Lunde, N. (2007) Increased resolution and penetration from a towed dual‐sensor streamer. First Break, 25(12), 71–77. https://doi.org/10.3997/1365‐2397.25.12.27722
     [Google Scholar]
  11. CGG . (2022) NVG 3D seismic. CGG. Available at: https://www.cgg.com/earth‐data/multi‐client‐seismic/northern‐viking‐graben
  12. Chang, H., VanDyke, J.P., Solano, M., McMechan, G.A. & Epili, D. (1996) 3‐D prestack Kirchhoff depth migration: from prototype to production in a MPP environment. Geophysics, 63(2), 546–556. https://doi.org/10.1190/1.1826254
     [Google Scholar]
  13. Dumoulin, V. & Visin, F. (2018) A guide to convolution arithmetic for deep learning. ArXiv preprint. arXiv: 1603.07285v2. Available at: https://arxiv.org/abs/1603.07285v2
  14. de Jonge, T., Vinje, V., Poole, G., Song, H. & Iversen, E. (2022) De‐bubbling seismic data using a generalized neural network. Geophysics, 87(1), V1–V14. https://doi.org/10.1190/geo2021‐0053.1
     [Google Scholar]
  15. de Jonge, T., Vinje, V., Zhao, P., Poole, G. & Iversen, E. (2022) Source and receiver deghosting by demigration‐based supervised learning. Geophysical Prospecting, 2022, 1–26. https://doi.org/10.1111/1365‐2478.13253
     [Google Scholar]
  16. Greiner, T.L., Kolbjørnsen, O., Lie, J.E., Nilsen, E.H., Evensen, A.K. & Gelius, L. (2019) Cross‐streamer wavefield interpolation using deep convolutional neural network. In: SEG technical program expanded abstracts. SEG. pp. 2207–2211. https://doi.org/10.1190/segam2019‐3214009.1
  17. Gulunay, N. (2003) Seismic trace interpolation in the Fourier transform domain. Geophysics, 68(1), 355–369. https://doi.org/10.1190/1.1543221
     [Google Scholar]
  18. Hammond, J.W. (1962) Ghost elimination from reflection records. Geophysics, 27(1), 48–60. https://doi.org/10.1190/1.1438977
     [Google Scholar]
  19. Hill, D., Combee, L. & Bacon, J. (2006) Over/under acquisition and data processing: the next quantum leap in seismic technology?First Break, 24(6), 81–95. https://doi.org/10.3997/1365‐2397.24.1096.26991
     [Google Scholar]
  20. Hlebnikov, V., Greiner, T.L., Vinje, V., Lie, J.E., Poole, G. (2022) De‐migration‐based supervised learning for interpolation and regularization of 3D offset classes. Geophysical Prospecting, 2022, 1–17. https://doi.org/10.1111/1365‐2478.13206
     [Google Scholar]
  21. Iversen, E., Tygel, M., Ursin, B. & de Hoop, V. (2012) Kinematic time migration and demigration of reflections in pre‐stack seismic data. Geophysical Journal International, 189(3), 1635–1666. https://doi.org/10.1111/j.1365‐246X.2012.05435.x
     [Google Scholar]
  22. Jovanovich, D.B., Sumner, R.D. & Akins‐Easterlin, S.L. (1983) Ghosting and marine signature deconvolution: a prerequisite for detailed seismic interpretation. Geophysics, 48(11), 1468–1485. https://doi.org/10.1190/1.1441431
     [Google Scholar]
  23. King, S. & Poole, G. (2015) Hydrophone‐only receiver deghosting using a variable sea surface datum. In: SEG technical program expanded abstracts. SEG. pp. 4610–4614. https://doi.org/10.1190/segam2015‐5891123.1
  24. Lucas, A., Iliadis, M., Molina, R. & Katsaggelos, A.K. (2018) Using deep neural networks for inverse problems in imaging: beyond analytical methods. IEEE Signal Processing Magazine, 35(1), 20–36. https://doi.org/10.1109/MSP.2017.2760358
     [Google Scholar]
  25. Martin, G.S., Wiley, R. & Marfurt, K.J. (2006) Marmousi2: an elastic upgrade for Marmousi. Leading Edge, 25(2), 156–166. https://doi.org/10.1190/1.2172306
     [Google Scholar]
  26. Martin, T., Brittan, J., Bekara, M. & Koch, K. (2011) 3D shallow water demultiple – extending the concept. In: 73rd EAGE Conference and Exhibition 2011: unconventional resources and the role of technology. Incorporating SPE EUROPEC 2011. EAGE. pp. 2040–2045. https://doi.org/10.3997/2214‐4609.20149267
  27. Masoomzadeh, H. & Woodburn, N. (2013) Broadband processing of conventional streamer data – optimized de‐ghosting in the tau‐P domain. In: 75th EAGE annual conference and exhibition EAGE. pp. 1–5. https://doi.org/10.3997/2214‐4609.20130093
  28. Mellier, G. & Tellier, N. (2018) Considerations about multi‐sensor solid streamer design. Oslo, Norway: EAGE Marine Acquisition Workshop.
     [Google Scholar]
  29. Peng, C., Jin, H. & Wang, P. (2014) Noise attenuation for multi‐sensor streamer data via cooperative de‐noising. In: SEG technical program expanded abstracts. SEG. pp. 1878–1882. https://doi.org/10.1190/segam2014‐0300.1
  30. Peng, H., Messud, J., Salaun, N., Hammoud, I., Jeunesse, P., Lesieur, T. et al. (2021) Proposal of the DUnet neural network architecture: deghosting example and theoretical analysis. In: 82nd EAGE annual conference & exhibition. EAGE. pp. 1–5. https://doi.org/10.3997/2214‐4609.202112820
  31. Poole, G. (2013) Pre‐migration receiver de‐ghosting and re‐datuming for variable depth streamer data. In: SEG technical program expanded abstracts. SEG. pp. 4216–4220. https://doi.org/10.1190/segam2013‐0541.1
  32. Poole, G. & Cooper, J. (2018) Multi‐sensor receiver deghosting using data domain sparseness weights. In: 80th EAGE conference and exhibition: opportunities presented by the energy transition. EAGE. https://doi.org/10.3997/2214‐4609.201801003
  33. Rickett, J.E., van Manen, D.J., Loganathan, P., Seymour, N. (2014) Slanted‐streamer data‐adaptive deghosting with local plane waves. In: 76th EAGE conference & exhibition. EAGE. pp. 1–5. https://doi.org/10.3997/2214‐4609.20141453
  34. Ronneberger, O., Fischer, P. & Brox, T. (2015) U‐net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer—assisted intervention. Springer. pp. 234–241. https://doi.org/10.1007/978‐3‐319‐24574‐4_28
  35. Santos, L.T., Schleicher, J. & Tygel, M. (2000a) Modeling, migration, and demigration. Leading Edge, 19(7), 712–715. https://doi.org/10.1190/1.1438696
     [Google Scholar]
  36. Santos, L.T., Schleicher, J., Tygel, M. & Hubral, P. (2000b) Seismic modeling by demigration. Geophysics, 65(4), 1281–1289. https://doi.org/10.1190/1.1444819
     [Google Scholar]
  37. Schleicher, J., Tygel, M. & Hubral, P. (2007) Seismic true‐amplitude imaging. Society of Exploration Geophysicists. https://doi.org/10.1190/1.9781560801672
     [Google Scholar]
  38. Schneider, W.A., Larner, K.L., Burg, J.P. & Backus, M. (1964) Data‐processing arrivals on technique reflection for the elimination. Geophysics, 29(5), 783–805. https://doi.org/10.1190/1.1439419
     [Google Scholar]
  39. Song, J.G., Gong, Y.L. & Li, S. (2015) High‐resolution frequency‐domain Radon transform and variable‐depth streamer data deghosting. Applied Geophysics, 12(4), 564–572. https://doi.org/10.1007/s11770‐015‐0525‐x
     [Google Scholar]
  40. Soubaras, R. (2010) Deghosting by joint deconvolution of a migration and a mirror migration. In: SEG technical program expanded abstracts. SEG. pp. 3406–3410. https://doi.org/10.1190/1.3513556
  41. Soubaras, R. & Dowle, R. (2010) Variable‐depth streamer – a broadband marine solution. First Break, 28(12), 89–96. https://doi.org/10.3997/1365‐2397.28.12.44692
     [Google Scholar]
  42. Sun, J., Slang, S., Elboth, T., Greiner, T.L., McDonald, S. & Gelius, L. (2019) Attenuation of marine seismic interference noise employing a customized U‐Net. Geophysical Prospecting, 68(3), 845–871. https://doi.org/10.1111/1365‐2478.12893
     [Google Scholar]
  43. Sun, J. & Hou, S. (2022) Improving signal fidelity for deep learning‐based seismic interference noise attenuation. Geophysical Prospecting, 2022. https://doi.org/10.1111/1365‐2478.13268
     [Google Scholar]
  44. Telling, R. & Grion, S. (2022) Multicomponent de‐ghosting using a hybrid operator in frequency and space. In: 83rd EAGE annual conference & exhibition. EAGE. pp. 1–5. https://doi.org/10.3997/2214‐4609.202210526
  45. Tenghamn, R. & Dhelie, P.E. (2009) GeoStreamer – increasing the signal‐to‐noise ratio using a dual‐sensor towed streamer. First Break, 27(10), 45–51. https://doi.org/10.3997/1365‐2397.2009017
     [Google Scholar]
  46. Tygel, M., Schleichert, J. & Hubral, P. (1996) A unified approach to 3‐D seismic reflection imaging, Part II: Theory. Geophysics, 61(3), 759–775. https://doi.org/10.1190/1.1444002
     [Google Scholar]
  47. Vrolijk, J.W. & Blacquière, G. (2021) Source deghosting of coarsely sampled common‐receiver data using a convolutional neural network. Geophysics, 86(3), V185–V196. https://doi.org/10.1190/geo2020‐0186.1
     [Google Scholar]
  48. Vrolijk, J.W. & Blacquiere, G. (2020) Source deghosting of coarsely sampled common‐receiver data using machine learning. In: SEG technical program expanded abstracts. SEG. pp. 3294–3298. https://doi.org/10.1190/segam2020‐3413112.1
  49. Wang, B., Zhang, N., Lu, W. & Wang, J. (2019) Deep‐learning‐based seismic data interpolation: a preliminary result. Geophysics, 84(1), V11–V20. https://doi.org/10.1190/geo2017‐0495.1
     [Google Scholar]
  50. Zhang, Z., Masoomzadeh, H. & Wang, B. (2018) Evolution of deghosting process for single‐sensor streamer data from 2D to 3D. Geophysical Prospecting, 66(5), 975–986. https://doi.org/10.1111/1365‐2478.12614
     [Google Scholar]
  51. Zhao, X., Lu, P., Zhang, Y., Chen, J. & Li, X. (2019) Swell‐noise attenuation: a deep learning approach. Leading Edge, 38(12), 934–942. https://doi.org/10.1190/tle38120934.1
     [Google Scholar]
/content/journals/10.1111/1365-2478.13407
Loading
Deghosting dual‐component streamer data using demigration‐based supervised learning
Geophysical Prospecting 72, 68 (2023); https://doi.org/10.1111/1365-2478.13407
/content/journals/10.1111/1365-2478.13407
/content/journals/10.1111/1365-2478.13407
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): data processing; modelling; multi‐component; noise; seismics; signal processing

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