1887
Volume 66, Issue 8
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Random noise attenuation, preserving the events and weak features by improving signal‐to‐noise ratio and resolution of seismic data are the most important issues in geophysics. To achieve this objective, we proposed a novel seismic random noise attenuation method by building a compound algorithm. The proposed method combines sparsity prior regularization based on shearlet transform and anisotropic variational regularization. The anisotropic variational regularization which is based on the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation attenuates noises while preserving the events of seismic data and it effectively avoids the fine‐scale artefacts due to shearlets from the restored seismic data. The proposed method is formulated as a convex optimization problem and the split Bregman iteration is applied to solve the optimization problem. To verify the effectiveness of the proposed method, we test it on several synthetic seismic datasets and real datasets. Compared with three methods (the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation, shearlets and shearlet‐based weighted anisotropic total variation), the numerical experiments indicate that the proposed method attenuates random noises while alleviating artefact and preserving events and features of seismic data. The obtained result also confirms that the proposed method improves the signal‐to‐noise ratio.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12672
2018-09-11
2020-02-23
Loading full text...

Full text loading...

References

  1. AbmaR. and ClaerboutJ.1995. Lateral prediction for noise attenuation by t‐x and f‐x techniques. Geophysics60, 1887–1896.
    [Google Scholar]
  2. BeckoucheS. and MaJ.2014. Simultaneous dictionary learning and denoising for seismic data. Geophysics79(3), A27–A31.
    [Google Scholar]
  3. BekaraM. and BaanM.2007. Local singular value decomposition for signal enhancement of seismic data. Geophysics72, 59–65.
    [Google Scholar]
  4. BekaraM. and BaanM.2009. Random and coherent noise attenuation by empirical mode decomposition. Geophysics74, 89–98.
    [Google Scholar]
  5. BergouniouxM. and PiffetL.2010. A second‐order model for image denoising. Journal of Set Valued and Variational Analysis18, 277–306.
    [Google Scholar]
  6. BonarD. and SacchiM.2012. Denoising seismic data using the nonlocal means algorithm. Geophysics77(1), A5–A8.
    [Google Scholar]
  7. CadzowJ.1988. Signal enhancement – A composite property mapping algorithm. IEEE Transactions on Acoustics, Speech, and Signal Processing36, 49–62.
    [Google Scholar]
  8. CanalesL.1984. Random noise reduction. 54th Annual International Meeting, Society of Exploration Geophysicists, Expanded Abstract, 525–527.
  9. CaoJ., ZhaoJ. and HuZ.2015. 3D seismic denoising based on a low‐redundancy curvelet transform. Journal of Geophysics and Engineering12, 566–576.
    [Google Scholar]
  10. ChenY., MaJ. and FomelS.2016. Double‐sparsity dictionary for seismic noise attenuation. Geophysics81(2), 17–30.
    [Google Scholar]
  11. ChenY., ZhouY., ChenW., ZuS., HuangW. and ZhangD.2017. Empirical low‐rank approximation for seismic noise attenuation. IEEE Transactions on Geoscience and Remote Sensing55(8), 4696–4711.
    [Google Scholar]
  12. DuB.andLines B.2000. Attenuating coherent noise by wavelet transform. Exploration Geophysics31, 353–358.
    [Google Scholar]
  13. EasleyG., LabateD. and ColonnaF.2009. Shearlet‐based total variation diffusion for denoising. IEEE Transactions on Image Processing18(2), 260–268.
    [Google Scholar]
  14. GanS., WangS., ChenY., ChenX., HuangW. and ChenH.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]
  15. GaoJ., SacchiM. and ChenX.2013. A fast reduced‐rank interpolation method for prestack seismic volumes that depend on four spatial dimensions. Geophysics78(1), V21–V30.
    [Google Scholar]
  16. GemechuD., YuanH. and MaJ.2017. Random noise attenuation using an improved anisotropic total variation regularization. J. Appl. Geophys.144 (2017), 173–187.
    [Google Scholar]
  17. GórszczykA., AdamczykA. and MalinowskiM.2014. Application of curvelet denoising to 2D and 3D seismic data—Practical considerations. Journal of Applied Geophysics105, 78–94.
    [Google Scholar]
  18. GuoK. and LabateD.2007. Optimally sparse multidimensional representation using shearlets. SIAM Journal of Mathematical Analysis39, 298–318.
    [Google Scholar]
  19. HäuserS.2011. Fast finite shearlet transform, a tutorial. Preprint, University of Kaiserslautern.
    [Google Scholar]
  20. HäuserS. and MaJ.2012. Seismic data reconstruction via shearlet regularized directional inpainting. Preprint.
  21. HauserS. and SteidlG.2013. Convex multiclass segmentation with shearlet regularization. International Journal of Computer Mathematics90 (1), 62–81.
    [Google Scholar]
  22. HennenfentG. and HerrmannF.2006. Seismic denoising with non‐uniformly sampled curvelets. Computing in Science & Engineering8, 16–25.
    [Google Scholar]
  23. HosseiniS., JavaherianA., HassaniH., TorabiS. and SadriM.2015. Adaptive attenuation of aliased ground roll using the shearlet transform. Journal of Applied Geophysics112, 190–205.
    [Google Scholar]
  24. HuangW., WangR., YangY., ChenY, LiH. and GanS.2016. Damped multichannel singular spectrum analysis for 3D random noise attenuation. Geophysics81(4), V261–V270.
    [Google Scholar]
  25. HuangW., WangR., YangY., GanS. and ChenY.2017. Signal extraction using randomized‐order multichannel singular spectrum analysis. Geophysics82(2), V59–V74.
    [Google Scholar]
  26. KongD. and PengZ.2015. Seismic random noise attenuation using shearlet and total generalized variation. Journal of Geophysics and Engineering12, 1024–1035.
    [Google Scholar]
  27. KutyniokG. and SauerT.2009. Adaptive directional subdivision schemes and shearlet multiresolution analysis. SIAM Journal of Mathematical Analysis41, 1436–1471.
    [Google Scholar]
  28. LariH. and GholamiA.2014. Curvelet‐TV regularized Bregman iteration for seismic random noise attenuation. Journal of Applied Geophysics109, 233–241.
    [Google Scholar]
  29. LiuC., WangD., WangT., HuB. and SuY.2015. Surface wave attenuation using the shearlet and S transforms. 77th EAGE Conference and Exhibition, Madrid, Spain, 1–4 June 2015.
    [Google Scholar]
  30. LiuL., PlonkaG. and MaJ.2017. Seismic data interpolation and denoising by learning a tensor tight frame. Inverse Problems33(10), 105011.
    [Google Scholar]
  31. LiuQ., LiB. and LinM.2015. Image deblurring associated with shearlet sparsity and weighted anisotropic total variation. Journal of Electronic Imaging24, 023001–13.
    [Google Scholar]
  32. LuW.2006. Adaptive noise attenuation of seismic images based on singular value decomposition and texture direction detection. Journal of Geophysics and Engineering3, 28–34.
    [Google Scholar]
  33. MaJ. and PlonkaG.2010. The curvelet transform. IEEE Signal Processing Magazine27(2), 118–133.
    [Google Scholar]
  34. MerouaneA., YilmazO. and BaysalE.2015. Random noise attenuation using 2‐dimensional shearlet transform. SEG Technical Program, Expanded Abstracts, 4770–4774.
    [Google Scholar]
  35. NazariS., GholtashiS., KahooA., MarviH. and AhmadifardA.2016. Sparse time‐frequency representation for seismic noise reduction using low‐rank and sparse decomposition. Geophysics81(2016), V117–V124.
    [Google Scholar]
  36. NeelamaniR., BaumsteinA., GillardD., HadidiM. and SorokaW.2008. Coherent and random noise attenuation using the curvelet transform. The Leading Edge27, 240–248.
    [Google Scholar]
  37. OropezaV. and SacchiM.2011. Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis. Geophysics76, 25–32.
    [Google Scholar]
  38. RudinL., OsherS. and FatemiE.1992. Nonlinear total variation based noise removal algorithms. Physica D60, 259–268.
    [Google Scholar]
  39. SabbioneJ., SacchiM. and VelisD.R.2015. Radon transform‐based microseismic event detection and signal‐to‐noise ratio enhancement. Journal of Applied Geophysics113, 51–63.
    [Google Scholar]
  40. Sacchi, M. and Porsani, M.1999. Fast high resolution parabolic Radon transform. 89th Annual International Meeting, SEG, Expanded Abstracts, 1477–1480.
    [Google Scholar]
  41. Shu, X. and Ahuja, N.2010. Hybrid compressive sampling via a new total variation TVL1. Lecture Notes in Computer Science6316, 393–404.
    [Google Scholar]
  42. TangG. and MaJ.2010. Application of total‐variation‐based curvelet shrinkage for three‐dimensional seismic data denoising. IEEE Geoscience and Remote Sensing Letters8(1), 103–107.
    [Google Scholar]
  43. VandeghinsteB., GoossensB., HolenR., VanhoveC., PizuricaA., VandenbergheS. et al. 2013. Iterative CT reconstruction using shearlet‐based regularization. IEEE Transactions on Nuclear Science60(5), 3305–3317.
    [Google Scholar]
  44. WangX., GaoJ., ChenW. and YangC.2015. The seismic random noise attenuation method based on enhanced bandelet transform. Journal of Applied Geophysics116, 146–155.
    [Google Scholar]
  45. WangY.1999. Random noise attenuation using forward–backward linear prediction. Journal of Seismic Exploration8, 133–142.
    [Google Scholar]
  46. YangW., WangR., ChenY., WuJ., QuS., YuanJ. et al. 2015. Application of spectral decomposition using regularized non‐stationary autoregression to random noise attenuation. Journal of Geophysics and Engineering12(2015), 175–187.
    [Google Scholar]
  47. ZhuL., LiuE. and McClellanJ.H.2015. Seismic data denoising through multiscale and sparsity‐promoting dictionary learning. Geophysics80(2015), WD45–WD57.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12672
Loading
/content/journals/10.1111/1365-2478.12672
Loading

Data & Media loading...

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