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

Abstract

Abstract

Seismic records from reflection seismic exploration in complex surface areas often contain scattered direct waves, a type of scattered waves generated by direct waves propagating on a rugged surface. For imaging reflections, scattered direct waves are regarded as noise, which lowers the signal‐to‐noise ratio of the reflected waves and deteriorates the quality of the seismic profile. Importantly, it is very challenging to separate scattered direct waves with strong energy and complex wave field characteristics owing to the rugged surface. In recent years, the rapid development of machine‐learning technology has broadened and advanced the application of deep learning in denoising seismic data. In this context, we propose an approach to using the convolutional autoencoder, a deep learning network, to intelligently separate scattered direct waves from seismic records on a rugged surface. The spectral element method is applied to simulate the elastic wave seismic records and scattered direct waves on a rugged surface. The synthetic shot gathers on the rugged ground are employed as the input for training the convolutional autoencoder network, while the simulated scattered direct waves on the uniform half‐space with the same rugged ground are employed as the label data for training. After the network training is completed, the trained convolutional autoencoder network can be applied to predict scattered direct waves from seismic records. The numerical experiments demonstrate that the proposed approach has good potential for suppressing complex scattered direct waves generated by direct waves propagating on a rugged surface.

© 2024 European Association of Geoscientists & Engineers. © 2022 European Association of Geoscientists & Engineers.
Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.13250
2023-12-18
2025-04-20

  • From This Site

    /content/journals/10.1111/1365-2478.13250
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Abid, M., Niu, L., Ma, J. & Geng, J. (2021) Unconventional reservoir characterization and sensitive attributes determination: a case study of the Eastern Sembar Formation, Lower Indus Basin, Pakistan. Geophysics, 86, B1–B14.
     [Google Scholar]
  2. Abma, R. & Claerbout, J. (1995) Lateral prediction for noise attenuation by t‐x and f‐x techniques. Geophysics, 60, 1887–1896.
     [Google Scholar]
  3. Alain, G. & Bengio, Y. (2014) What regularized auto‐encoders learn from the data generating distribution. Journal of Machine Learning Research, 15, 3563–3593.
     [Google Scholar]
  4. Almuhaidib, A.M. & Toksöz, M.N. (2016) Suppression of near‐surface scattered body‐to‐surface waves: a steerable and nonlinear filtering approach. Geophysical Prospecting, 64, 392–405.
     [Google Scholar]
  5. Burroughs, L. & Trickett, S. (2009) Prestack rank‐reducing noise suppression: practice. SEG Technical Program Expanded Abstracts, 28, 3337–3341.
     [Google Scholar]
  6. Cadzow, J.A. (1988) Signal enhancement: a composite property mapping algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing, 36, 49–62.
     [Google Scholar]
  7. Canales, L.L. (1984) Random noise reduction. SEG Technical Program Expanded Abstracts, 525–527.
     [Google Scholar]
  8. Cary, P.W. & Zhang, C. (2009) Ground roll attenuation with adaptive eigenimage filtering. SEG Technical Program Expanded Abstracts, 28, 3302–3306.
     [Google Scholar]
  9. Chen, Y. & Saygin, E. (2021) Seismic inversion by hybrid machine learning. Journal of Geophysical Research: Solid Earth, 126, 1–23.
     [Google Scholar]
  10. Chen, Y. & Schuster, G.T. (2020) Seismic inversion by Newtonian machine learning. Geophysics, 85, WA185–WA200.
     [Google Scholar]
  11. Chiu, S.K. (2013) Coherent and random noise attenuation via multichannel singular spectrum analysis in the randomized domain. Geophysical Prospecting, 61, 1–9.
     [Google Scholar]
  12. Gao, W. & Sacchi, M.D. (2020) Random noise attenuation via the randomized canonical polyadic decomposition. Geophysical Prospecting, 68, 872–891.
     [Google Scholar]
  13. Gao, Y., Zhao, P., Li, G. & Li, H. (2021) Seismic noise attenuation by signal reconstruction: an unsupervised machine learning approach. Geophysical Prospecting, 69, 984–1002.
     [Google Scholar]
  14. Gemechu, D., Ma, J. & Yong, X. (2018) A compound method for random noise attenuation. Geophysical Prospecting, 66, 1548–1567.
     [Google Scholar]
  15. Glorot, X. & Bengio, Y. (2010) Understanding the difficulty of training deep feedforward neural networks. Journal of Machine Learning Research, 9, 249–256.
     [Google Scholar]
  16. Halliday, D., Bilsby, P., West, L., Kragh, E. & Quigley, J. (2015) Scattered ground‐roll attenuation using model‐driven interferometry. Geophysical Prospecting, 63, 116–132.
     [Google Scholar]
  17. Hennenfent, G. & Herrmann, F.J. (2006) Seismic denoising with nonuniformly sampled curvelets. Computing in Science & Engineering, 8, 16–25.
     [Google Scholar]
  18. Herman, G.C. & Perkins, C. (2006), Predictive removal of scattered noise. Geophysics, 71, V41–V49.
     [Google Scholar]
  19. Huang, L., Dong, X. & Clee, T.E. (2017) A scalable deep learning platform for identifying geologic features from seismic attributes. The Leading Edge, 36, 249–256.
     [Google Scholar]
  20. Ibrahim, A. & Sacchi, M.D. (2014) Simultaneous source separation using a robust Radon transform. Geophysics, 79, V1–V11.
     [Google Scholar]
  21. Kaur, H., Fomel, S. & Pham, N. (2020) Seismic ground‐roll noise attenuation using deep learning. Geophysical Prospecting, 68, 2064–2077.
     [Google Scholar]
  22. Kingma, D.P. & Ba, J.L. (2015) Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR), 7–9 May 2015, San Diego.
     [Google Scholar]
  23. Krizhevsky, A., Sutskever, I. & Hinton, G.E. (2012) ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 1, 1097–1105.
     [Google Scholar]
  24. LeCun, Y., Bengio, Y. & Hinton, G. (2015) Deep learning. Nature, 521, 436–444.
     [Google Scholar]
  25. LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. (1998) Gradient‐based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324.
     [Google Scholar]
  26. Levin, A. & Nadler, B. (2011) Natural image denoising: optimality and inherent bounds. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2833–2840.
     [Google Scholar]
  27. Li, K., Liu, Z., She, B., Cai, H. & Wang, Y. (2021) Two‐dimensional dictionary learning for suppressing random seismic noise. Geophysical Prospecting, 69, 85–100.
     [Google Scholar]
  28. Liu, D., Wang, W., Wang, X., Wang, C., Pei, J. & Chen, W. (2020) Poststack seismic data denoising based on 3‐D convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 58, 1598–1629.
     [Google Scholar]
  29. Martin, G.S., Wiley, R. & Marfurt, K.J. (2006) Marmousi2: an elastic upgrade for Marmousi. The Leading Edge, 25, 156–166.
     [Google Scholar]
  30. Masci, J., Meier, U., Ciresan, D. & Schmidhuber, J. (2011) Stacked convolutional auto‐encoders for hierarchical feature extraction. International Conference on Artificial Neural Networks, 52–59.
     [Google Scholar]
  31. Neelamani, R., Baumstein, A.I., Gillard, D.G., Hadidi, M.T. & Soroka, W.L. (2008) Coherent and random noise attenuation using the curvelet transform. The Leading Edge, 27, 240–248.
     [Google Scholar]
  32. Oropeza, V. & Sacchi, M. (2011) Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis. Geophysics, 76, V25–V32.
     [Google Scholar]
  33. Othman, A., Iqbal, N., Hanafy, S.M. & Waheed, U. (2022) Automated event detection and denoising method for passive seismic data using residual deep convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–11.
     [Google Scholar]
  34. Pang, B., Nijkamp, E. & Wu, Y.N. (2020) Deep learning with TensorFlow: a review. Journal of Educational and Behavioral Statistics, 45, 227–248.
     [Google Scholar]
  35. Ross, C.P. & Cole, D.M. (2017) A comparison of popular neural network facies‐classification schemes. The Leading Edge, 36, 340–349.
     [Google Scholar]
  36. Russell, B., Hampson, D. & Chun, J. (1990) Noise elimination and the Radon transform, Part 2. The Leading Edge, 9, 31–37.
     [Google Scholar]
  37. Shaheen, A., Waheed, U., Fehler, M., Sokol, L. & Hanafy, S. (2021) GroningenNet: Deep learning for low‐magnitude earthquake detection on a multi‐level sensor network. Sensors, 21, 8080.
     [Google Scholar]
  38. Sun, J., Slang, S., Elboth, T., Greiner, T.L., Mcdonald, S. & Gelius, L. (2020) Attenuation of marine seismic interference noise employing a customized U‐Net. Geophysical prospecting, 68, 845–871.
     [Google Scholar]
  39. Tromp, J., Komatitsch, D. & Liu, Q. (2008) Spectral‐element and adjoint methods in seismology. Communications in Computational Physics, 3, 1–32.
     [Google Scholar]
  40. Tsai, C.J. (1984) An analysis leading to the reduction of scattered noise on deep marine seismic records. The Leading Edge, 49, 17–26.
     [Google Scholar]
  41. Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y. & Manzagol, P.‐A. (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11, 3371–3408.
     [Google Scholar]
  42. Waheed, U., Haghighat, E., Alkhalifah, T., Song, C. & Hao, Q. (2021) PINNeik: Eikonal solution using physics‐informed neural networks. Computers and Geosciences, 155, 1–13.
     [Google Scholar]
  43. Wang, F. & Chen, S. (2019) Residual learning of deep convolutional neural network for seismic random noise attenuation. IEEE Geoscience and Remote Sensing Letters, 16, 1314–1318.
     [Google Scholar]
  44. Wang, Y., Wang, B., Tu, N. & Geng, J. (2020) Seismic trace interpolation for irregularly spatial sampled data using convolutional autoencoder. Geophysics, 85, 1–12.
     [Google Scholar]
  45. Wu, H., Zhang, B., Lin, T., Li, F. & Liu, N. (2019) White noise attenuation of seismic trace by integrating variational mode decomposition with convolutional neural network. Geophysics, 84, V307–V317.
     [Google Scholar]
  46. Xie, P., Boelle, J.L. & Puntous, H. (2018) Generative‐adversarial network‐based fast‐noise removal on land‐seismic data. SEG Technical Program Expanded Abstracts, 2171–2175.
     [Google Scholar]
  47. Xu, Y., Yin, C., Zou, X., Ni, Y., Pan, Y. & Xu, L. (2020) A high accurate automated firs‐break picking method for seismic records from high‐density acquisition in areas with a complex surface. Geophysical Prospecting, 68, 1228–1252.
     [Google Scholar]
  48. Yang, F. & Ma, J. (2019) Deep‐learning inversion: a next generation seismic velocity‐model building method. Geophysics, 84, R583–R599.
     [Google Scholar]
  49. Yilmaz, Ö. (2001) Seismic data analysis. Tulsa: Society of Exploration Geophysicists.
     [Google Scholar]
  50. You, J., Xue, Y., Cao, J. & Li, C. (2020) Attenuation of seismic swell noise using convolutional neural networks in frequency domain and transfer learning. Interpretaion, 8, T941–T952.
     [Google Scholar]
  51. Yu, S. & Ma, J. (2021) Deep learning for geophysics: current and future trends. Reviews of Geophysics, 59, 1–36.
     [Google Scholar]
  52. Yu, S., Ma, J. & Wang, W. (2019) Deep learning for denoising. Geophysics, 84, V333–V350.
     [Google Scholar]
  53. Yuan, S., Liu, J., Wang, S., Wang, T. & Shi, P. (2018) Seismic waveform classification and first‐break picking using convolution neural networks. IEEE Geoscience and Remote Sensing Letters, 15, 272–276.
     [Google Scholar]
  54. Yuan, Y., Si, X. & Zheng, Y. (2020) Ground‐roll attenuation using generative adversarial networks. Geophysics, 85, WA255–WA267.
     [Google Scholar]
  55. Zhang, Y., Lin, H., Li, Y. & Ma, H. (2019) A patch‐based denoising method using deep convolutional neural network for seismic image. IEEE Access, 7, 156883–156894.
     [Google Scholar]
  56. Zhao, X., Lu, P., Zhang, Y., Chen, J. & Li, X. (2019a) Swell‐noise attenuation: a deep learning approach. The Leading Edge, 38, 934–942.
     [Google Scholar]
  57. Zhao, Y., Li, Y., Dong, X. & Yang, B. (2019b) Low‐frequency noise suppression method based on improved DnCNN in desert seismic data. IEEE Geoscience and Remote Sensing Letters, 16, 811–815.
     [Google Scholar]
/content/journals/10.1111/1365-2478.13250
Loading
A deep learning approach to separating scattered direct waves from reflection seismic records on rugged surface: Synthetic data examples
Geophysical Prospecting 72, 20 (2023); https://doi.org/10.1111/1365-2478.13250
/content/journals/10.1111/1365-2478.13250
/content/journals/10.1111/1365-2478.13250
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): neural network; noise rejection; scattering; seismic modelling; 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