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Volume 72, Issue 3
  • E-ISSN: 1365-2478

Abstract

Abstract

Seismic random noise is one of the main factors that degrade the quality of seismic data. Therefore, seismic random noise attenuation should be performed appropriately through several stages during seismic data processing, and this requires sufficient experience and knowledge because the proper hyperparameters need to be determined based on the features of the noise in the target seismic data. Recently, machine learning–based seismic noise attenuation has been widely studied because it provides suitable results by learning noise features from noisy data, unlike conventional physics‐based approaches. There are many important factors in machine learning, and, here, we focus on the loss functions of machine learning in terms of seismic random noise attenuation. The most widely used loss function is , but we train a model with various kinds of single and multiple loss functions and attenuate seismic random noise. We analyse the efficiency of loss functions by comparing the noise‐attenuated results of synthetic and field seismic data qualitatively and quantitatively. Our analysis indicates that the multiple loss function with the norm can be a proper choice for random noise suppression of seismic data.

© 2024 European Association of Geoscientists & Engineers. © 2023 European Association of Geoscientists & Engineers.
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/content/journals/10.1111/1365-2478.13449
2024-02-21
2025-03-17

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References

  1. Anvari, R., Kahoo, A.R., Mohammadi, M., Khan, N.A. & Chen, Y. (2019) Seismic random noise attenuation using sparse low‐rank estimation of the signal in the time‐frequency domain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1612–1618.
     [Google Scholar]
  2. Bektaş, S. & Şişman, Y. (2010) The comparison of l1 and l2‐norm minimization methods. International Journal of the Physical Sciences, 5, 1721–1727.
     [Google Scholar]
  3. Brunet, D., Vrscay, E.R. & Wang, Z. (2011) On the mathematical properties of the structural similarity index. IEEE Transactions on Image Processing, 21, 1488–1499.
     [Google Scholar]
  4. Chen, Z., Badrinarayanan, V., Lee, C.‐Y. & Rabinovich, A. (2018) Gradnorm: gradient normalization for adaptive loss balancing in deep multitask networks. In International conference on machine learning (pp. 794–803). Cambridge, MA: PMLR.
     [Google Scholar]
  5. Claerbout, J.F. & Muir, F. (1973) Robust modeling with erratic data. Geophysics, 38, 826–844.
     [Google Scholar]
  6. Cohen, I., Huang, Y., Chen, J., Benesty, J., Benesty, J., Chen, J., Huang, Y. & Cohen, I. (2009) Pearson correlation coefficient. In: Noise reduction in speech processing. Berlin: Springer, pp. 1–4.
     [Google Scholar]
  7. Ebadi, M.R. (2017) Coherent and incoherent seismic noise attenuation using parabolic radon transform and its application in environmental geophysics. Modeling Earth Systems and Environment, 3, 18.
     [Google Scholar]
  8. 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]
  9. Goudarzi, A. & Riahi, M.A. (2012) Seismic coherent and random noise attenuation using the undecimated discrete wavelet transform method with WDGA technique. Journal of Geophysics and Engineering, 9, 619–631.
     [Google Scholar]
  10. Groenendijk, R., Karaoglu, S., Gevers, T. & Mensink, T. (2021) Multi‐loss weighting with coefficient of variations. In Proceedings of the IEEE/CVF winter conference on applications of computer vision. Piscataway, NJ: IEEE, pp. 1469–1478.
     [Google Scholar]
  11. He, K., Zhang, X., Ren, S. & Sun, J. (2016) Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. Piscataway, NJ: IEEE, pp. 770–778.
     [Google Scholar]
  12. Hlebnikov, V., Elboth, T., Vinje, V. & Gelius, L.‐J. (2021) Noise types and their attenuation in towed marine seismic: a tutorial. Geophysics, 86, W1–W19.
     [Google Scholar]
  13. Jun, H. & Cho, Y. (2022) Repeatability enhancement of time‐lapse seismic data via a convolutional autoencoder. Geophysical Journal International, 228, 1150–1170.
     [Google Scholar]
  14. Jun, H., Jou, H.‐T., Kim, C.‐H., Lee, S.H. & Kim, H.‐J. (2020) Random noise attenuation of sparker seismic oceanography data with machine learning. Ocean Science, 16, 1367–1383.
     [Google Scholar]
  15. Jun, H., Kim, C.‐H. & Kim, H.‐J. (2021) Machine‐learning based noise attenuation of field seismic data using noise data acquisition. Journal of the Korean Society of Mineral and Energy Resources Engineers, 58, 408–417.
     [Google Scholar]
  16. Kendall, A., Gal, Y. & Cipolla, R. (2018) Multi‐task learning using uncertainty to weigh losses for scene geometry and semantics. In Proceedings of the IEEE conference on computer vision and pattern recognition. Piscataway, NJ: IEEE, pp. 7482–7491.
     [Google Scholar]
  17. Kim, Y., Oh, D., Huh, S., Song, D., Jeong, S., Kwon, J., Kim, M., Kim, D., Ryu, H., Jung, J., et al,. (2021) Deep learning‐based statistical noise reduction for multidimensional spectral data. Review of Scientific Instruments, 92, 073901.
     [Google Scholar]
  18. Li, G., Li, Y. & Yang, B. (2017) Seismic exploration random noise on land: modeling and application to noise suppression. IEEE Transactions on Geoscience and Remote Sensing, 55, 4668–4681.
     [Google Scholar]
  19. Li, H., Yang, W. & Yong, X. (2018) Deep learning for ground‐roll noise attenuation. In: SEG Technical program expanded abstracts 2018. Houston, TX: Society of Exploration Geophysicists, pp. 1981–1985.
     [Google Scholar]
  20. Liu, B. & Liu, Q. (2020) Random noise reduction using SVD in the frequency domain. Journal of Petroleum Exploration and Production Technology, 10, 3081–3089.
     [Google Scholar]
  21. Liu, W., Cao, S. & Chen, Y. (2015) Seismic time–frequency analysis via empirical wavelet transform. IEEE Geoscience and Remote Sensing Letters, 13, 28–32.
     [Google Scholar]
  22. Liu, W. & Chen, W. (2019) Recent advancements in empirical wavelet transform and its applications. IEEE Access, 7, 103770–103780.
     [Google Scholar]
  23. Moreno, L., Blanco, D., Muñoz, M.L. & Garrido, S. (2011) L1–l2‐norm comparison in global localization of mobile robots. Robotics and Autonomous Systems, 59, 597–610.
     [Google Scholar]
  24. Saad, O.M. & Chen, Y. (2020) Deep denoising autoencoder for seismic random noise attenuation. Geophysics, 85, V367–V376.
     [Google Scholar]
  25. Saad, O.M. & Chen, Y. (2021) A fully unsupervised and highly generalized deep learning approach for random noise suppression. Geophysical Prospecting, 69, 709–726.
     [Google Scholar]
  26. Si, X. & Yuan, Y. (2018) Random noise attenuation based on residual learning of deep convolutional neural network. In SEG international exposition and annual meeting. Houston, TX: Society of Exploration Geophysicists, pp. 1986–1990.
     [Google Scholar]
  27. Strickert, M., Schleif, F.‐M., Seiffert, U. & Villmann, T. (2008) Derivatives of Pearson correlation for gradient‐based analysis of biomedical data. Inteligencia Artificial. Revista Iberoamericana de Inteligencia Artificial, 12, 37–44.
     [Google Scholar]
  28. Sun, Q.‐F., Xu, J.‐Y., Zhang, H.‐X., Duan, Y.‐X. & Sun, Y.‐K. (2022) Random noise suppression and super‐resolution reconstruction algorithm of seismic profile based on GAN. Journal of Petroleum Exploration and Production Technology, 12, 2107–2119.
     [Google Scholar]
  29. Wang, Z., Bovik, A.C., Sheikh, H.R. & Simoncelli, E.P. (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing, 13, 600–612.
     [Google Scholar]
  30. Wang, Z., Simoncelli, E.P. & Bovik, A.C. (2003) Multiscale structural similarity for image quality assessment. In The Thirty‐Seventh Asilomar conference on signals, systems & computers, 2003. Piscataway, NJ: IEEE, vol. 2, pp. 1398–1402.
     [Google Scholar]
  31. Yang, L., Chen, W., Wang, H. & Chen, Y. (2021) Deep learning seismic random noise attenuation via improved residual convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 59, 7968–7981.
     [Google Scholar]
  32. Yang, L., Wang, S., Chen, X., Saad, O.M., Chen, W., Oboue, Y. A. S.I. & Chen, Y. (2021) Unsupervised 3‐D random noise attenuation using deep skip autoencoder. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–16.
     [Google Scholar]
  33. Yuan, Y., Si, X. & Zheng, Y. (2020) Ground‐roll attenuation using generative adversarial networks. Geophysics, 85, WA255–WA267.
     [Google Scholar]
  34. Zhang, F., Dai, R. & Liu, H. (2014) Seismic inversion based on l1‐norm misfit function and total variation regularization. Journal of Applied Geophysics, 109, 111–118.
     [Google Scholar]
  35. Zhang, K., Zuo, W., Chen, Y., Meng, D. & Zhang, L. (2017) Beyond a Gaussian denoiser: residual learning of deep CNN for image denoising. IEEE Transactions on Image Processing, 26, 3142–3155.
     [Google Scholar]
  36. Zhang, Q., Wang, H., Chen, W. & Huang, G. (2021) A local radon transform for seismic random noise attenuation. Journal of Applied Geophysics, 186, 104264.
     [Google Scholar]
  37. Zhao, H., Gallo, O., Frosio, I. & Kautz, J. (2016) Loss functions for image restoration with neural networks. IEEE Transactions on Computational Imaging, 3, 47–57.
     [Google Scholar]
  38. Zhao, X., Lu, P., Zhang, Y., Chen, J. & Li, X. (2019) Swell‐noise attenuation: a deep learning approach. The Leading Edge, 38, 934–942.
     [Google Scholar]
  39. Zhou, L. & Tang, J. (2017) Fraction‐order total variation blind image restoration based on l1‐norm. Applied Mathematical Modelling, 51, 469–476.
     [Google Scholar]
/content/journals/10.1111/1365-2478.13449
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Loss functions in machine learning for seismic random noise attenuation
Geophysical Prospecting 72, 978 (2024); https://doi.org/10.1111/1365-2478.13449
/content/journals/10.1111/1365-2478.13449
/content/journals/10.1111/1365-2478.13449
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  • Article Type: Research Article
Keyword(s): data processing; noise; seismics; signal processing

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