1887
Volume 72, Issue 5
  • E-ISSN: 1365-2478

Abstract

Abstract

Picking stack velocity from seismic velocity spectra is a fundamental method in seismic stack velocity analysis. With the increase in the scale of seismic data acquisition, manual picking cannot achieve the required efficiency. Therefore, an automatic picking algorithm is urgently needed now. Despite some supervised deep learning–based picking approaches that have been proposed, they heavily rely on sufficient training samples and lack interpretability. In contrast, utilizing physical knowledge to develop semi‐data‐driven methods has the potential to efficiently solve this problem. Thus, we propose a semi‐supervised ensemble learning method to reduce the reliance on manually labelled data and improve interpretability by incorporating the interval velocity constraint. Semi‐supervised ensemble learning fuses the information of the estimated spectrum, nearby velocity spectra and few‐shot manual picking to recognize the velocity picking. Test results of both the synthetic and field datasets indicate that semi‐supervised ensemble learning achieves more reliable and precise picking than traditional clustering‐based techniques and the currently popular convolutional neural network method.

© 2024 European Association of Geoscientists & Engineers. © 2024 European Association of Geoscientists & Engineers.
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2024-05-21
2026-02-08

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References

  1. Bishop, C.M. & Nasrabadi, N.M. (2006) Pattern recognition and machine learning. Cham: Springer.
     [Google Scholar]
  2. Biswas, R., Vassiliou, A., Stromberg, R. & Sen, M.K. (2019) Estimating normal moveout velocity using the recurrent neural network. Interpretation, 7(4), T819–T827.
     [Google Scholar]
  3. Carlos, C., Sen, M.K. & Stoffa, P.L. (1998) Automatic NMO correction and velocity estimation by a feedforward neural network. Geophysics, 63(5), 1696–1707.
     [Google Scholar]
  4. Chen, Y., Liu, T. & Chen, X. (2015) Velocity analysis using similarity‐weighted semblance. Geophysics, 80(4), A75–A82.
     [Google Scholar]
  5. Dix, C.H. (1955) Seismic velocities from surface measurements. Geophysics, 20(1), 68–86.
     [Google Scholar]
  6. Ferreira, R.S., Oliveira, D.A., Semin, D.G. & Zaytsev, S. (2020) Automatic velocity analysis using a hybrid regression approach with convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 59(5), 4464–4470.
     [Google Scholar]
  7. Fish, B.C. & Kusuma, T. (1994) A neural network approach to automate velocity picking. In SEG technical program expanded abstracts. Tulsa, OK: Society of Exploration Geophysicists, pp. 185–188.
     [Google Scholar]
  8. Fomel, S. (2009) Velocity analysis using AB semblance. Geophysical Prospecting, 57(3), 311–321.
     [Google Scholar]
  9. Härdle, W., Müller, M., Sperlich, S., Werwatz, A., et al. (2004) Nonparametric and semiparametric models. Cham: Springer.
     [Google Scholar]
  10. Huang, K.Y., Chen, K.J. & Yang, J.R. (2013) Genetic algorithm for seismic velocity picking. In The 2013 International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ: IEEE, pp. 1–8.
     [Google Scholar]
  11. Huang, K.Y. & Yang, J.R. (2016) Seismic velocity picking by Hopfield neural network. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Piscataway, NJ: IEEE, pp. 3190–3193.
     [Google Scholar]
  12. Leung, Y., Zhang, J.S. & Xu, Z.B. (2000) Clustering by scale‐space filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1396–1410.
     [Google Scholar]
  13. Luo, S. & Hale, D. (2012) Velocity analysis using weighted semblance. Geophysics, 77(2), U15–U22.
     [Google Scholar]
  14. Ma, H. (2021) A velocity spectrum picking method based on detection fine tuning depth recognition technology. In 2021 IEEE 4th International Conference on Computer and Communication Engineering Technology (CCET). Piscataway, NJ: IEEE, pp. 69–74.
     [Google Scholar]
  15. Ma, Y., Ji, X., Fei, T.W. & Luo, Y. (2018) Automatic velocity picking with convolutional neural networks. In SEG technical program expanded abstracts. Houston, TX: Society of Exploration Geophysicists, pp. 2066–2070.
     [Google Scholar]
  16. Neidell, N.S. & Taner, M.T. (1971) Semblance and other coherency measures for multichannel data. Geophysics, 36(3), 482–497.
     [Google Scholar]
  17. Park, M.J. & Sacchi, M.D. (2020) Automatic velocity analysis using convolutional neural network and transfer learning. Geophysics, 85(1), V33–V43.
     [Google Scholar]
  18. Qiu, C., Wu, B., Meng, D., Zhu, X., Li, M. & Qin, N. (2021) Attention neural network semblance velocity auto picking with reference velocity curve data augmentation. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Piscataway, NJ: IEEE, pp. 4596–4599.
     [Google Scholar]
  19. Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. (2016) You only look once: Unified, real‐time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ: IEEE, pp. 779–788.
     [Google Scholar]
  20. 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. Berlin: Springer, pp. 234–241.
     [Google Scholar]
  21. Schmidt, J. & Hadsell, F.A. (1992) Neural network stacking velocity picking. In SEG technical program expanded abstracts. Tulsa, OK: Society of Exploration Geophysicists, pp. 18–21.
     [Google Scholar]
  22. Taner, M.T. & Koehler, F. (1969) Velocity spectra‐digital computer derivation applications of velocity functions. Geophysics, 34(6), 859–881.
     [Google Scholar]
  23. Umair, b.W., Al‐Zahrani, S. & Hanafy, S.M. (2019) Machine learning algorithms for automatic velocity picking: K‐means vs. DBSCAn. In SEG technical program expanded abstracts. Houston, TX: Society of Exploration Geophysicists, pp. 5110–5114.
     [Google Scholar]
  24. Wang, H., Zhang, J., Zhao, Z., Zhang, C., Long, L., Yang, Z. & Geng, W. (2022) Automatic velocity picking using a multi‐information fusion deep semantic segmentation network. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–10.
     [Google Scholar]
  25. Wang, W., McMechan, G.A., Ma, J. & Xie, F. (2021) Automatic velocity picking from semblances with a new deep‐learning regression strategy: Comparison with a classification approach. Geophysics, 86(2), U1–U13.
     [Google Scholar]
  26. Yilmaz, Ö. (2001) Seismic data analysis: Processing, inversion, and interpretation of seismic data. Houston, TX: Society of Exploration Geophysicists.
     [Google Scholar]
  27. Zaremba, W., Sutskever, I. & Vinyals, O. (2014) Recurrent neural network regularization [preprint]. arXiv. arXiv:1409.2329.
  28. Zhang, H., Zhu, P., Gu, Y. & Li, X. (2019) Automatic velocity picking based on deep learning. In SEG technical program expanded abstracts 2019. Houston, TX: Society of Exploration Geophysicists, pp. 2604–2608.
     [Google Scholar]
  29. Zhang, P. & Lu, W. (2016) Automatic time‐domain velocity estimation based on an accelerated clustering method. Geophysics, 81(4), U13–U23.
     [Google Scholar]
/content/journals/10.1111/1365-2478.13492
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Automatic stack velocity picking using a semi‐supervised ensemble learning method
Geophysical Prospecting 72, 1816 (2024); https://doi.org/10.1111/1365-2478.13492
/content/journals/10.1111/1365-2478.13492
/content/journals/10.1111/1365-2478.13492
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  • Article Type: Research Article
Keyword(s): clustering; ensemble learning; semi‐supervised learning; velocity analysis; velocity spectrum

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