1887
Volume 53, Issue 3
  • ISSN: 0812-3985
  • E-ISSN: 1834-7533

Abstract

Seismic data interpolation techniques are extremely economical for removing negative effects resulting from insufficient spatial sampling. In recent years, self-learning mechanism-based methods (relatively intelligent methods), such as machine learning (ML) and deep learning, have increasingly attracted the attention of many scholars. Support vector regression machine (SVR), a kind of ML method, can obtain good reconstructed performance for seismic data interpolation. However, the performance is influenced directly by the kernel function, which controls the map from the input space to the feature space. In other words, a good and suitable kernel function can contribute to the extraction of good features and improve the self-learning ability. In this paper, Ricker kernel function suitable for seismic data, rather than the traditional Gaussian kernel function, will be introduced and applied in the new SVR-based interpolation method. In detail, the Ricker wavelet is widely used to simulate seismic data. And the Ricker kernel function can be used specially for the seismic data process. Under the guarantee of the special kernel function, the input time-space vector series are trained as a better model for future prediction of missing seismic data. Numerical experiments on synthetic and real field data show better reconstruction ability than that of SVR based on the traditional kernel function. Furthermore, we discuss the difference between the relatively shallow ML method (SVR) and the deep learning neural networks method. Except for the computer hardware, deep neural learning methods will impose stringent requirements on the training data, not only with respect to quantity but also including data quality. From this perspective, SVR shows more flexibility in the self-learning process.

© 2021 Australian Society of Exploration Geophysicists
Loading

Article metrics loading...

/content/journals/10.1080/08123985.2021.1931107
2022-05-04
2026-01-17

  • From This Site

    /content/journals/10.1080/08123985.2021.1931107
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Amari, S.-i., and S.Wu. 1999. Improving support vector machine classifiers by modifying kernel functions. Neural Networks12: 783–9.
     [Google Scholar]
  2. An, P.2006. Application of multi-wavelet seismic trace decomposition and reconstruction to seismic data interpretation and reservoir characterization. SEG technical program expanded abstracts 2006, 973–7.
  3. An-na, W., Z.Yue, H.Yun-tao, and L.Yun-lu. 2010. A novel construction of SVM compound kernel function. In 2010 International conference on logistics systems and intelligent management (ICLSIM), 1462–5. IEEE.
  4. Araya-Polo, M., T.Dahlke, C.Frogner, C.Zhang, T.Poggio, and D.Hohl. 2017. Automated fault detection without seismic processing. The Leading Edge36: 208–14.
     [Google Scholar]
  5. Beckouche, S., and J.Ma. 2014. Simultaneous dictionary learning and denoising for seismic data. Geophysics79: A27–31.
     [Google Scholar]
  6. Chai, X., G.Tang, S.Wang, R.Peng, W.Chen, and J.Li. 2020. Deep learning for regularly missing data reconstruction. IEEE Transactions on Geoscience and Remote Sensing58: 4406–23.
     [Google Scholar]
  7. Chang, C.-C., and C.-J.Lin. 2011. Libsvm: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology2: Article number 27.
     [Google Scholar]
  8. Chang, D., W.Yang, X.Yong, G.Zhang, W.Wang, H.Li, and Y.Wang. 2020. Seismic data interpolation using dual-domain conditional generative adversarial networks. IEEE Geoscience and Remote Sensing Letters. doi:10.1109/LGRS.2020.3008478.
     https://doi.org/10.1109/LGRS.2020.3008478 [Google Scholar]
  9. Chen, Y., M.Bai, Z.Guan, Q.Zhang, M.Zhang, and H.Wang. 2020. Five-dimensional seismic data reconstruction using the optimally damped rank-reduction method. Geophysical Journal International222: 1824–45.
     [Google Scholar]
  10. Deng, X., D.Yang, and J.Xie. 2009. Noise reduction by support vector regression with a ricker wavelet kernel. Journal of Geophysics and Engineering6: 177–88.
     [Google Scholar]
  11. Elangovan, M., V.Sugumaran, K.Ramachandran, and S.Ravikumar. 2011. Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool. Expert Systems with Applications38: 15202–7.
     [Google Scholar]
  12. Fomel, S.2003. Seismic reflection data interpolation with differential offset and shot continuation. Geophysics68 (2): 733–44.
     [Google Scholar]
  13. Gan, S., S.Wang, Y.Chen, X.Chen, W.Huang, and H.Chen. 2016. Compressive sensing for seismic data reconstruction via fast projection onto convex sets based on Seislet transform. Journal of Applied Geophysics130: 194–208.
     [Google Scholar]
  14. Gan, S., S.Wang, Y.Chen, Y.Zhang, and Z.Jin. 2015. Dealiased seismic data interpolation using Seislet transform with low-frequency constraint. IEEE Geoscience and Remote Sensing Letters12: 2150–54.
     [Google Scholar]
  15. García-Nieto, P.J., E.Garcia-Gonzalo, F.S.Lasheras, J.A.Fernández, and C.D.Muñiz. 2020. A hybrid de optimized wavelet kernel SVR-based technique for algal atypical proliferation forecast in La Barca reservoir: A case study. Journal of Computational and Applied Mathematics366: Article number 112417.
     [Google Scholar]
  16. Guo, Z.2017. Seismic data interpolation: The arbitrarily sampled fourier transform method. Master's thesis, Graduate Studies.
  17. Huang, W., D.Feng, and Y.Chen. 2020. De-aliased and de-noise Cadzow filtering for seismic data reconstruction. Geophysical Prospecting68: 553–71.
     [Google Scholar]
  18. Huang, W., R.-S.Wu, and R.Wang. 2018. Damped dreamlet representation for exploration seismic data interpolation and denoising. IEEE Transactions on Geoscience and Remote Sensing56: 3159–72.
     [Google Scholar]
  19. Jia, Y., and J.Ma. 2017. What can machine learning do for seismic data processing? An interpolation application. Geophysics82 (3): V163–77.
     [Google Scholar]
  20. Jia, Y., S.Yu, and J.Ma. 2018. Intelligent interpolation by Monte Carlo machine learning. Geophysics83: V83–97.
     [Google Scholar]
  21. Kaur, H., N.Pham, and S.Fomel. 2019. Seismic data interpolation using cyclegan. In SEG technical program expanded abstracts 2019: Society of exploration geophysicists, 2202–6.
  22. Kong, F., F.Picetti, V.Lipari, P.Bestagini, and S.Tubaro. 2020. Deep prior-based seismic data interpolation via multi-res u-net. In SEG technical program expanded abstracts 2020: Society of exploration geophysicists, 3159–63.
  23. Lewis, W., and D.Vigh. 2017. Deep learning prior models from seismic images for full-waveform inversion. In SEG technical program expanded abstracts 2017: Society of exploration geophysicists, 1512–7.
  24. Liu, J., Y.Wu, D.Han, and X.Li. 2004. Time-frequency decomposition based on ricker wavelet. In SEG technical program expanded abstracts 2004: Society of exploration geophysicists, 1937–40.
  25. Ma, J.2013. Three-dimensional irregular seismic data reconstruction via low-rank matrix completion. Geophysics78 (5): V181–92.
     [Google Scholar]
  26. Ma, J., and S.Yu. 2016. Seismic data interpolation with polar fourier transform. In SPG/SEG 2016 international geophysical conference, Beijing, China, 20–22 April 2016, Society of exploration geophysicists and society of petroleum geophysicists, 480–81.
  27. Mousavi, S.M., and C.A.Langston. 2017. Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data. Geophysics82: V211–27.
     [Google Scholar]
  28. Naghizadeh, M.. 2012. Seismic data interpolation and denoising in the frequency-wavenumber domain. Geophysics77: V71–80.
     [Google Scholar]
  29. Naghizadeh, M., and K.A.Innanen. 2011. Seismic data interpolation using a fast generalized fourier transform. Geophysics76: V1–10.
     [Google Scholar]
  30. Naghizadeh, M., and M.D.Sacchi. 2007. Multistep autoregressive reconstruction of seismic records. Geophysics72 (6): V111–8.
     [Google Scholar]
  31. Richardson, A.2018. Seismic full-waveform inversion using deep learning tools and techniques, preprint arXiv:1801.07232.
  32. Siahsar, M.A.N., S.Gholtashi, V.Abolghasemi, and Y.Chen. 2017. Simultaneous denoising and interpolation of 2D seismic data using data-driven non-negative dictionary learning. Signal Processing141: 309–21.
     [Google Scholar]
  33. Smola, A.J., B.Schölkopf, and K.-R.Müller. 1998. The connection between regularization operators and support vector kernels. Neural Networks11: 637–49.
     [Google Scholar]
  34. Song, H., Z.Ding, C.Guo, Z.Li, and H.Xia. 2008. Research on combination kernel function of support vector machine. In 2008 International conference on computer science and software engineering, 838–41. IEEE.
  35. Spitz, S.1991. Seismic trace interpolation in the FX domain. Geophysics56 (6): 785–94.
     [Google Scholar]
  36. Wang, B., R.-S.Wu, X.Chen, and J.Li. 2015. Simultaneous seismic data interpolation and denoising with a new adaptive method based on dreamlet transform. Geophysical Journal International201: 1182–94.
     [Google Scholar]
  37. Wang, B., N.Zhang, W.Lu, and J.Wang. 2019. Deep-learning-based seismic data interpolation: A preliminary result. Geophysics84: V11–20.
     [Google Scholar]
  38. Wang, H., W.Chen, Q.Zhang, X.Liu, S.Zu, and Y.Chen. 2020. Fast dictionary learning for high-dimensional seismic reconstruction. IEEE Transactions on Geoscience and Remote Sensing. doi:10.1109/TGRS.2020.3030740.
     https://doi.org/10.1109/TGRS.2020.3030740 [Google Scholar]
  39. Wu, J., M.Bai, D.Zhang, H.Wang, G.Huang, and Y.Chen. 2020. Fast and robust low-rank approximation for five-dimensional seismic data reconstruction. IEEE Access8: 175501–12.
     [Google Scholar]
  40. Yu, S., J.Ma, X.Zhang, and M.D.Sacchi. 2015. Interpolation and denoising of high-dimensional seismic data by learning a tight frame. Geophysics80: V119–32.
     [Google Scholar]
  41. Zhang, W., H.Chen, B.Zhao, and J.Du. 2016. 5D seismic data interpolation using low-rank radial function interpolation. Presented at the 2016 SEG international exposition and annual meeting, society of exploration geophysicists.
  42. Zhu, L., E.Liu, and J.H.McClellan. 2017. Joint seismic data denoising and interpolation with double-sparsity dictionary learning. Journal of Geophysics and Engineering14: 802–10.
     [Google Scholar]
/content/journals/10.1080/08123985.2021.1931107
Loading
Self-learning regression interpolation based on Ricker kernel function for seismic data
Exploration Geophysics 53, 289 (2022); https://doi.org/10.1080/08123985.2021.1931107
/content/journals/10.1080/08123985.2021.1931107
/content/journals/10.1080/08123985.2021.1931107
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): machine learning; Seismic data interpolation; support vector regression machine

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