1887
Volume 69, Issue 7
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

This study introduces a new attribute to identify seismic erratic noise, i.e. outlier, in the context of unsupervised anomaly detection and is defined as local outlier probabilities. The local outlier probabilities calculate scores of degrees of isolation, i.e. outlier‐ness, for each object in a data set, which represents how far an object is deviated from its surrounding objects. Since the local outlier probabilities combines a density‐based outlier detection method with a statistically oriented scheme, its scoring system provides regularized outlier‐ness, which is an outlier probability, to be used for making a binary decision to do inclusion or exclusion of an object; such a decision only requires a simple and straightforward threshold on a probability. Based on the binary decision that flags outliers versus non‐outliers, local outlier probabilities‐denoising workflows are developed by combining multiple steps to complete an application of the local outlier probabilities to attenuate seismic erratic noise. Higher stability and improved robustness in the detection and rejection of seismic erratic noise have been achieved by implementing moving windows and decision tree‐based processes. To avoid loss of useful signal energy, signal enhancement applications are additionally suggested. Numerical experiments on synthetic data investigate the applicability of the proposed algorithms to seismic erratic noise attenuation. Field data examples demonstrate the feasibility of a local outlier probabilities‐denoising application as an effective tool in seismic denoising portfolio.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.13123
2021-08-09
2024-03-29
Loading full text...

Full text loading...

References

  1. Abma, R. and Claerbout, J. (1995) Lateral prediction for noise attenuation by t‐x and f‐x techniques. Geophysics, 60, 1887–1896.
    [Google Scholar]
  2. Abma, R.L. and Kabir, N. (2006) 3D interpolation of irregular data with a POCS algorithm. Geophysics, 71, E91–E97.
    [Google Scholar]
  3. Baardman, R.H. and Hegge, R.F. (2020) Machine learning approaches for use in deblending. The Leading Edge, 39, 188–194.
    [Google Scholar]
  4. Breunig, M.M., Kriegel, H.P., Ng, R. and Sander, J. (2000) LOF: Identifying density‐based local outliers. Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104.
  5. Canales, L.L. (1984) Random noise reduction. SEG Technical Program Expanded Abstracts, 525–527.
    [Google Scholar]
  6. Candès, E.J., Romberg, J.K. and Tao, T. (2006) Stable signal recovery from incomplete and inaccurate measurements. Communications on Pure and Applied Mathematics, 59, 1207–1223.
    [Google Scholar]
  7. Candès, E.J. and Plan, Y. (2010) Matrix completion with noise. Proceedings of the IEEE, 98, 925–936.
    [Google Scholar]
  8. Candès, E.J., Li, X., Ma, Y. and Wright, J. (2011) Robust principal component analysis?. Journal of the Association for Computing Machinery, 58, 11:1–11:37.
    [Google Scholar]
  9. Chen, Y. (2017) Fast dictionary learning for noise attenuation of multidimensional seismic data. Geophysical Journal International, 209, 21–31.
    [Google Scholar]
  10. Chen, Y., Fomel, S. and Hu, J. (2014) Iterative deblending of simultaneous source seismic data using seislet‐domain shaping regularization. Geophysics, 79, V179–V189.
    [Google Scholar]
  11. Chen, Y. and Fomel, S. (2015) Random noise attenuation using local signal‐and‐noise orthogonalization. Geophysics, 80, WD1–WD9.
    [Google Scholar]
  12. Chen, Y., Zu, S., Wang, T. and Chen, X. (2019) Deblending of simultaneous source data using a structure‐oriented space‐varying median filter. Geophysical Journal International, 216, 1214–1232.
    [Google Scholar]
  13. Donoho, D.L. (1995) De‐noising by soft‐thresholding. IEEE Transactions on Information Theory, 41, 613–627.
    [Google Scholar]
  14. Fomel, S. (2007) Local seismic attributes. Geophysics, 72, A29–A33.
    [Google Scholar]
  15. Fomel, S. and Liu, Y. (2010) Seislet transform and seislet frame. Geophysics, 75, V25–V38.
    [Google Scholar]
  16. Gan, S., Wang, S., Chen, Y. and Chen, X. (2016) Simultaneous‐source separation using iterative seislet‐frame thresholding. IEEE Geoscience and Remote Sensing Letters, 13, 197–201.
    [Google Scholar]
  17. Goldstein, M. and Uchida, S. (2016) A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data. Plos One, 11, 1–31.
    [Google Scholar]
  18. Hawkins, D. (1980) Identification of Outliers. London:Chapman and Hall.
    [Google Scholar]
  19. Hennenfent, G. and Hermann, F.J. (2006) Seismic denoising with nonuniformly sampled curvelets. Computing in Science & Engineering, 8, 16–25.
    [Google Scholar]
  20. Huang, G., Bai, M., Zhao, Q., Chen, W. and Chen, Y. (2021) Erratic noise suppression using iterative structure‐oriented space‐varying median filtering with sparsity constraint. Geophysical Prospecting, 69, 101–121.
    [Google Scholar]
  21. Huang, W., Wang, R., Gong, X. and Chen, Y. (2017) Iterative deblending of simultaneous‐source seismic data with structuring median constraint. IEEE Geoscience and Remote Sensing Letters, 15, 58–62.
    [Google Scholar]
  22. Huo, S., Luo, Y. and Kelamis, P.G. (2012) Simultaneous source separation via multidirectional vector‐median filtering. Geophysics, 77, V123–V131.
    [Google Scholar]
  23. Jeong, W., Tsingas, C. and Almubarak, M.S. (2020a) A numerical study on deblending of land simultaneous shooting acquisition data via rank‐reduced filtering and signal enhancement applications. Geophysical Prospecting, 68, 1742–1757.
    [Google Scholar]
  24. Jeong, W., Tsingas, C. and Almubarak, M.S. (2020b) Local outlier factor as part of a workflow for detecting and attenuating blending noise in simultaneously acquired data. Geophysical Prospecting, 68, 1523–1539.
    [Google Scholar]
  25. Kriegel, H.P., Kröger, P., Schubert, E. and Zimek, A. (2009) LoOP: Local outlier probabilities. Proceedings of the 18th ACM Conference on Information and Knowledge Management, 1649–1652.
    [Google Scholar]
  26. Mallat, S. (2008) A Wavelet Tour of Signal Processing: The Sparse Way. San Diego: Academic.
    [Google Scholar]
  27. Oropeza, V. and Sacchi, M.D. (2011) Simultaneous seismic data denoising and reconstruction. Geophysics, 76, V25–V32.
    [Google Scholar]
  28. Papadimitrious, S., Kitagawa, H., Gibbons, P.B. and Faloutsos, C. (2003) LOCI: Fast outlier detection using the local correlation integral. Proceedings of the 19th international Conference on Data Engineering, 315–326.
    [Google Scholar]
  29. Saad, O.M. and Chen, Y. (2020) Deep denoising autoencoder for seismic random noise attenuation. Geophysics, 85, V367–V376.
    [Google Scholar]
  30. Saad, O.M. and Chen, Y. (2021) A fully unsupervised and highly generalized deep learning approach for random noise suppression. Geophysical Prospecting, 69, 709–726.
    [Google Scholar]
  31. Siahsar, M.A.N., Abolghasemi, V. and Chen, Y. (2017) Simultaneous denoising and interpolation of 2D seismic data using data‐driven non‐negative dictionary learning. Signal Processing, 141, 309–321.
    [Google Scholar]
  32. Soubaras, R. (1995) Prestack random and impulsive noise attenuation by f‐x projection filtering. SEG Technical Program Expanded Abstracts, 711–714.
    [Google Scholar]
  33. Sternfels, R., Viguier, G., Gondoin, R. and Meur, D.L. (2015) Multidimensional simultaneous random plus erratic noise attenuation and interpolation for seismic data by joint low‐rank and sparse inversion. Geophysics, 80, WD129–WD141.
    [Google Scholar]
  34. Trickett, S.R. (2003) F‐xy eigenimage noise suppression. Geophysics, 68, 751–759.
    [Google Scholar]
  35. Tsingas, C., Kim, Y.S. and Yoo, J. (2016) Broadband acquisition, deblending, and imaging employing dispersed source arrays. The Leading Edge, 35, 354–360.
    [Google Scholar]
  36. Tsingas, C., Mubarak, S.M., Jeong, W., Shuhail, A.A. and Trzesniowski, Z. (2020) 3D distributed and dispersed source array acquisition and data processing. The Leading Edge, 39, 392–400.
    [Google Scholar]
  37. Wang, B., Chen, X., Li, J. and Cao, J. (2016) An improved weighted projection onto convex sets method for seismic data interpolation and denoising. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9, 228–235.
    [Google Scholar]
  38. Yu, S., Ma, J., Zhang, X. and Sacchi, M.D. (2014) Interpolation and denoising of high dimension seismic data by learning a tight frame. Geophysics, 80, V119–V132.
    [Google Scholar]
  39. Yu, S., Ma, J. and Wang, W. (2019) Deep learning for denoising. Geophysics, 84, V333–V350.
    [Google Scholar]
  40. Zhao, Q., Du, Q., Gong, X. and Chen, Y. (2018) Signal‐preserving erratic noise attenuation via iterative robust sparsity‐promoting filter. IEEE Geoscience and Remote Sensing Letters, 56, 3547–3560.
    [Google Scholar]
  41. Zhou, Z., Li, X., Wright, J., Candès, E.J. and Ma, Y. (2010) Stable principal component pursuit. 2010 IEEE International symposium on Information Theory, 1518–1522.
    [Google Scholar]
  42. Zhou, Y. (2017) A POCS method for iterative deblending constrained by a blending mask. Journal of Applied Geophysics, 138, 245–254.
    [Google Scholar]
  43. Zu, S., Zhou, H., Wu, R., Mao, W. and Chen, Y. (2019) Hybrid‐sparsity constrained dictionary learning for iterative deblending of extremely noisy simultaneous‐source data. IEEE Transactions on Geoscience and Remote Sensing, 57, 2249–2262.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.13123
Loading
/content/journals/10.1111/1365-2478.13123
Loading

Data & Media loading...

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