1887
Volume 73, Issue 6
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Efficient noise removal in seismic data is crucial for accurately analysing subsurface structures because noise generated during field acquisition can considerably degrade data quality. Traditional single‐domain denoising methods often struggle to preserve weak signals in prestack seismic data, potentially leading to the loss of critical information. To address this issue, we propose a novel dual‐domain (DD) denoising approach called non‐local means via patch ordering in DD (DD–PONLM). This method leverages the strengths of both time–space and transform domains to minimize the leakage of weak events. By employing non‐local self‐similarity and iterative processing in the time–space domain and discrete cosine transform domain, the proposed method effectively reduces noise while preserving weak signals. We validate the effectiveness of our method through extensive testing on both asynthetic and a field example. The results are compared with several traditional single‐domain methods, demonstrating that DD–PONLM considerably improves the preservation of weak signals and reduces artefacts, such as the Gibbs phenomenon, associated with transform domain processing. This DD strategy not only enhances the signal‐to‐noise ratio but also preserves structural fidelity, making it a robust solution for seismic data denoising.

© 2025 European Association of Geoscientists & Engineers. © 2025 European Association of Geoscientists & Engineers.
Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.70046
2025-07-09
2026-02-15

  • From This Site

    /content/journals/10.1111/1365-2478.70046
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. AntoniB., C.Bartomeu, and M.Jean‐Michel. 2011. “Non‐Local Means Denoising.” Image Processing On Line2011: 208–212. https://doi.org/10.5201/ipol.2011.bcm_nlm
     [Google Scholar]
  2. BonarD., and M.Sacchi. 2012. “Denoising Seismic Data Using the Nonlocal Means Algorithm.” Geophysics77: A5–A8. https://doi.org/10.1190/geo2011‐0235.1
     [Google Scholar]
  3. BuadesA., B.Coll, and J. M.Morel. 2005. “A Review of Image Denoising Algorithms, With a New One.” Multiscale Modeling & Simulation4: 490–530. https://doi.org/10.1137/040616024
     [Google Scholar]
  4. CanalesL. L.1984. “Random Noise Reduction.” In SEG Technical Program Expanded Abstracts 1984, 525–527. Society of Exploration Geophysicists. https://doi.org/10.1190/1.1894168
  5. ChenY., and S.Fomel. 2021. “Nonstationary Local Signal‐and‐Noise Orthogonalization.” Geophysics86: V409–V418. https://doi.org/10.1190/geo2020‐0151.1
     [Google Scholar]
  6. CoifmanR. R., and D. L.Donoho. 1995. “Wavelets and Statistics, Lecture Notes in Statistics.” In Translation‐Invariant de‐Noising, edited by A.Antoniadis and G.Oppenheim, 125–150. Springer New York. https://doi.org/10.1007/978‐1‐4612‐2544‐7_9
     [Google Scholar]
  7. DongX. T., Y.Li, and B. J.Yang. 2019. “Desert Low‐Frequency Noise Suppression by Using Adaptive DnCNNs Based on the Determination of High‐Order Statistic.” Geophysical Journal International219: 1281–1299. https://doi.org/10.1093/gji/ggz363
     [Google Scholar]
  8. ElbothT., F.Geoteam, H. H.Qaisrani, and T.Hertweck. 2008. “De‐Noising Seismic Data in the Time‐Frequency Domain.” In SEG Technical Program Expanded Abstracts 2008, 2622–2626. Society of Exploration Geophysicists. https://doi.org/10.1190/1.3063887
  9. ElbothT., I.Vik Presterud, and D.Hermansen. 2010. “Time‐Frequency Seismic Data De‐Noising.” Geophysical Prospecting58: 441–453. https://doi.org/10.1111/j.1365‐2478.2009.00846.x
     [Google Scholar]
  10. ElumalaiK., S.Kumar, B.Lall, R. K.Patney. 2018. “Denoising of Pre‐Stack Seismic Data Using Subspace Estimation Methods.” IET Signal Processing12: 992–999. https://doi.org/10.1049/iet‐spr.2017.0556
     [Google Scholar]
  11. FanH., X.Xie, Y.Zhang, and N.Zou. 2018. “Contourlet Based Image Denoising Method Combined Recursive Cycle‐Spinning Algorithm.” In Machine Learning and Intelligent Communications, 444–450. Springer International Publishing. https://doi.org/10.1007/978‐3‐319‐73564‐1_44
     [Google Scholar]
  12. FreireS. L. M., and T. J.Ulrych. 1988. “Application of Singular Value Decomposition to Vertical Seismic Profiling.” Geophysics53: 778–785. https://doi.org/10.1190/1.1442513
     [Google Scholar]
  13. GórszczykA., A.Adamczyk, and M.Malinowski. 2014. “Application of Curvelet Denoising to 2D and 3D Seismic Data—Practical Considerations.” Journal of Applied Geophysics105: 78–94. https://doi.org/10.1016/j.jappgeo.2014.03.009
     [Google Scholar]
  14. GulunayN.1986. “FXDECON and Complex Wiener Prediction Filter.” In SEG Technical Program Expanded Abstracts 1986, 279–281. Society of Exploration Geophysicists.. https://doi.org/10.1190/1.1893128
  15. HlebnikovV., T.Elboth, V.Vinje, L.‐J.Gelius. 2020. “Noise Types and Their Attenuation in Towed Marine Seismic: A Tutorial.” Geophysics86: W1–W19. https://doi.org/10.1190/geo2019‐0808.1
     [Google Scholar]
  16. LiS., X.Zhang, J.Zhang, L.Liu. 2024. “Adaptive Dual‐Domain Filtering for Random Seismic Noise Removal.” Geophysics89: V377–V394. https://doi.org/10.1190/geo2023‐0532.1
     [Google Scholar]
  17. LiuD., Z.Deng, C.Wang, et al. 2022. “An Unsupervised Deep Learning Method for Denoising Prestack Random Noise.” IEEE Geoscience and Remote Sensing Letters19: 1–5. https://doi.org/10.1109/LGRS.2020.3019400
     [Google Scholar]
  18. LiuG., X.Chen, J.Du, K.Wu. 2012. “Random Noise Attenuation Using f‐x Regularized Nonstationary Autoregression.” Geophysics77: V61–V69. https://doi.org/10.1190/geo2011‐0117.1
     [Google Scholar]
  19. LiuY., S.Fomel, and C.Liu. 2015. “Signal and Noise Separation in Prestack Seismic Data Using Velocity‐Dependent Seislet Transform.” Geophysics80: WD117–WD128. https://doi.org/10.1190/geo2014‐0234.1
     [Google Scholar]
  20. LiuY., N.Liu, and C.Liu. 2014. “Adaptive Prediction Filtering in T‐x‐y Domain for Random Noise Attenuation Using Regularized Nonstationary Autoregression.” Geophysics80: V13–V21. https://doi.org/10.1190/geo2014‐0011.1
     [Google Scholar]
  21. LuY., Y.Huang, W.Xue, G.Zhang. 2019. “Seismic Data Processing Method Based on Wavelet Transform for De‐Noising.” Cluster Computing22: 6609–6620. https://doi.org/10.1007/s10586‐018‐2355‐0
     [Google Scholar]
  22. MalehmirA., R.Durrheim, G.Bellefleur, et al. 2012. “Seismic Methods in Mineral Exploration and Mine Planning: A General Overview of Past and Present Case Histories and a Look Into the Future.” Geophysics77: WC173–WC190. https://doi.org/10.1190/geo2012‐0028.1
     [Google Scholar]
  23. NaghizadehM.2012. “Seismic Data Interpolation and Denoising in the Frequency‐Wavenumber Domain.” Geophysics77: V71–V80. https://doi.org/10.1190/geo2011‐0172.1
     [Google Scholar]
  24. NuhaH., M.Deriche, M.Mohandes. 2019. Bilateral Filters with Elliptical Gaussian Kernels for Seismic Surveys Denoising//2019 5th International Conference on Frontiers of Signal Processing (ICFSP). 141–144. https://doi.org/10.1109/ICFSP48124.2019.8938084
  25. ObouéY. A. S. I., Y.Chen, S.Fomel, et al. 2024. “An advanced median filter for improving the signal‐to‐noise ratio of seismological datasets.” Computers & Geosciences, 182: 105464. https://doi.org/10.1016/j.cageo.2023.105464
     [Google Scholar]
  26. OropezaV., and M.Sacchi. 2011. “Simultaneous Seismic Data Denoising and Reconstruction via Multichannel Singular Spectrum Analysis.” Geophysics76: V25–V32. https://doi.org/10.1190/1.3552706
     [Google Scholar]
  27. RamI., M.Elad, and I.Cohen. 2013a. “Image Denoising Using NL‐Means via Smooth Patch Ordering.” In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 1350–1354. IEEE. https://doi.org/10.1109/ICASSP.2013.6637871
  28. RamI., M.Elad, and I.Cohen. 2013b. “Image Processing Using Smooth Ordering of Its Patches.” IEEE Transactions on Image Processing22: 2764–2774. https://doi.org/10.1109/TIP.2013.2257813
     [Google Scholar]
  29. ShaoD., Y.Zhao, Y.Li, et al. 2022. “Noisy2Noisy: Denoise Pre‐Stack Seismic Data Without Paired Training Data With Labels.” IEEE Geoscience and Remote Sensing Letters19: 1–5. https://doi.org/10.1109/LGRS.2022.3145835
     [Google Scholar]
  30. ShaoO., L.Wang, X.Hu, Z.Long. 2019. “Seismic Random Noise Attenuation Using Nonlocal‐Means via Smooth Patch Ordering.” Journal of Applied Geophysics167: 1–10. https://doi.org/10.1016/j.jappgeo.2019.05.005
     [Google Scholar]
  31. TrickettS.2008. “F‐xy Cadzow Noise Suppression.” In SEG Technical Program Expanded Abstracts 2008, 2586–2590. Society of Exploration Geophysicists. https://doi.org/10.1190/1.3063880
  32. WangH., and Y.Chen. 2021. “Adaptive Frequency‐Domain Nonlocal Means for Seismic Random Noise Attenuation.” Geophysics86: V143–V152. https://doi.org/10.1190/geo2019‐0798.1
     [Google Scholar]
  33. WangH., C.Tao, S.Chen, et al. 2017. “Highly Efficient Methods of Seismic Data Acquisition Based on Sparse Constraints and Blended‐Source Excitation.” Chinese Journal of Geophysics (in Chinese)60: 3518–3538. https://doi.org/10.6038/cjg20170920
     [Google Scholar]
  34. WangX., J.Zhang, and H.Cheng. 2021. “A Joint Framework for Seismic Signal Denoising Using Total Generalized Variation and Shearlet Transform.” IEEE Access9: 6661–6673. https://doi.org/10.1109/ACCESS.2021.3049644
     [Google Scholar]
  35. WeiW., G.Jinghuai, L.Kang, et al. 2009. Structure‐oriented Gaussian filter for seismic detail preserving smoothing//2009 16th IEEE International Conference on Image Processing (ICIP). 601–604. https://doi.org/10.1109/ICIP.2009.5413869
  36. YangP., J.Gao, and W.Chen. 2013. “On Analysis‐Based Two‐Step Interpolation Methods for Randomly Sampled Seismic Data.” Computers & Geosciences51: 449–461. https://doi.org/10.1016/j.cageo.2012.07.023
     [Google Scholar]
  37. ZhangC., and M.Van Der Baan. 2018. “Multicomponent Microseismic Data Denoising by 3D Shearlet Transform.” Geophysics83: A45–A51. https://doi.org/10.1190/geo2017‐0788.1
     [Google Scholar]
  38. ZhangM., Y.Liu, M.Bai, Y.Chen. 2019. “Seismic Noise Attenuation Using Unsupervised Sparse Feature Learning.” IEEE Transactions on Geoscience and Remote Sensing57: 9709–9723. https://doi.org/10.1109/TGRS.2019.2928715
     [Google Scholar]
  39. ZhangR., and T. J.Ulrych. 2003. “Physical Wavelet Frame Denoising.” Geophysics68: 225–231. https://doi.org/10.1190/1.1543209
     [Google Scholar]
  40. ZhangY., and B.Wang. 2023. “Unsupervised Seismic Random Noise Attenuation by a Recursive Deep Image Prior.” Geophysics88: V473–V485. https://doi.org/10.1190/geo2022‐0612.1
     [Google Scholar]
  41. ZhaoY. X., Y.Li, and B. J.Yang. 2020. “Denoising of Seismic Data in Desert Environment Based on a Variational Mode Decomposition and a Convolutional Neural Network.” Geophysical Journal International221: 1211–1225. https://doi.org/10.1093/gji/ggaa071
     [Google Scholar]
  42. ZhouQ., J.Gao, Z.Wang, et al. 2016. Adaptive Variable Time Fractional Anisotropic Diffusion Filtering for Seismic Data Noise Attenuation. IEEE Transactions on Geoscience and Remote Sensing, 54: 1905–1917. https://doi.org/10.1109/TGRS.2015.2490158
  43. ZhuW., S. M.Mousavi, and G. C.Beroza. 2019. “Seismic Signal Denoising and Decomposition Using Deep Neural Networks.” IEEE Transactions on Geoscience and Remote Sensing57: 9476–9488. https://doi.org/10.1109/TGRS.2019.2926772
     [Google Scholar]
/content/journals/10.1111/1365-2478.70046
Loading
Random Noise Suppression of Prestack Seismic Data Using Non‐Local Means via Patch Ordering in the Dual‐Domain
Geophysical Prospecting 73, e70046 (2025); https://doi.org/10.1111/1365-2478.70046
/content/journals/10.1111/1365-2478.70046
/content/journals/10.1111/1365-2478.70046
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): denoising; dual‐domain; non‐local self‐similarity; patch ordering; prestack seismic data

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