1887
Volume 68, Issue 5
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

Random noise attenuation utilizing predictive filtering achieves great performance in denoising seismic data. Conventional predictive filtering methods are based on fixed filter operators and neglect the complexity of structures. In this way, the denoised data cannot meet the requirement of balancing the signal preservation and noise removal. In this study, we proposed a structural complexity‐guided predictive filtering method that utilizes an adapted filter operator to adjust the changes of structural complexity. The proposed structural complexity‐guided predictive filtering mainly consists of two stages. A slope field information is acquired according to plane‐wave destruction to assess the structural complexity. In addition, an adaptive filter operator is obtained to denoise the seismic data according to the adaptive factor. Both synthetic data and real seismic profiles are employed to examine the denoising capacity and flexibility of the refined predictive filtering using adaptive lengths. The analysis of the predicted results shows that adaptive predictive filtering is powerful and has the ability to eliminate random noises with negligible distortions.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12941
2020-02-26
2024-04-26
Loading full text...

Full text loading...

References

  1. AbmaR. and ClaerboutJ.F.1995. Lateral prediction for noise attenuation by t‐x and f‐x techniques. Geophysics60, 1887–1896.
    [Google Scholar]
  2. AkramJ.2018. An application of waveform denoising for microseismic data using polarization‐linearity and time‐frequency thresholding. Geophysical Prospecting66, 872–893.
    [Google Scholar]
  3. CanalesL.L.1984. Random noise reduction. SEG Technical Program, Expanded Abstract, 525–527).
  4. ChaseM.K.1992. Random noise reduction by fxy prediction filtering. Exploration Geophysics23, 51–56.
    [Google Scholar]
  5. ChenK. and SacchiM.D.2017. Robust f‐x projection filtering for simultaneous random and erratic seismic noise attenuation. Geophysical Prospecting65, 650–668.
    [Google Scholar]
  6. ChenY.2017. Fast dictionary learning for noise attenuation of multidimensional seismic data. Geophysical Journal International209, 21–31.
    [Google Scholar]
  7. ChenY., BaiM. and ChenY.2019. Obtaining free USArray data by multi‐dimensional seismic reconstruction. Nature Communications10, 4434.
    [Google Scholar]
  8. ChenY. and FomelS.2015. Random noise attenuation using local signal‐and‐noise orthogonalization. Geophysics80, WD1–WD9.
    [Google Scholar]
  9. ChenY. and MaJ.2014. Random noise attenuation by fx empirical‐mode decomposition predictive filtering. Geophysics79, V81–V91.
    [Google Scholar]
  10. ChengJ., SacchiM. and GaoJ.2018. Computational efficient multi‐dimensional singular spectrum analysis for prestack seismic data reconstruction. Geophysics84, 1–36.
    [Google Scholar]
  11. ClaerboutJ.F.1986. Fundamentals of Geophysical Data Processing With Applications to Petroleum Prospecting. McGraw‐Hill.
    [Google Scholar]
  12. ClaerboutJ.F.1992. Earth Soundings Analysis: Processing Versus Inversion, Vol. 6. Blackwell Scientific Publications.
    [Google Scholar]
  13. FehmersG.C. and HöckerC.F.2003. Fast structural interpretation with structure‐oriented filtering. Geophysics68, 1286–1293.
    [Google Scholar]
  14. FomelS.2002. Applications of plane‐wave destruction filters. Geophysics67, 1946–1960.
    [Google Scholar]
  15. FosterD.J. and MosherC.C.1992. Suppression of multiple reflections using the radon transform. Geophysics57, 386–395.
    [Google Scholar]
  16. GalbraithM.1991. Random noise attenuation by fx prediction: a tutorial. SEG Technical Program, Expanded Abstracts, 1428–1431.
  17. GemechuD., MaJ. and YongX.2018. A compound method for random noise attenuation. Geophysical Prospecting66, 1548–1567.
    [Google Scholar]
  18. GuittonA. and ClaerboutJ.2010. An algorithm for interpolation in the pyramid domain. Geophysical Prospecting58, 965–976.
    [Google Scholar]
  19. GulunayN.1986. FXDECON and complex wiener prediction filter. SEG Technical Program, Expanded Abstracts, 279–281.
  20. GulunayN.2018. Signal leakage in f–x deconvolution algorithms. Geophysics82, W31–W45.
    [Google Scholar]
  21. GulunayN., SudhakarV., GerrardC. and MonkD.1993. Prediction filtering for 3‐D poststack data. SEG Technical Program, Expanded Abstract, 1183–1186.
  22. HarrisP. and WhiteR.1997. Improving the performance of f–x prediction filtering at low signal‐to‐noise ratios. Geophysical Prospecting45, 269–302.
    [Google Scholar]
  23. HerrmannF.J.2010. Randomized sampling and sparsity: getting more information from fewer samples. Geophysics75, WB173–WB187.
    [Google Scholar]
  24. HornbostelS.C.1991. Spatial prediction filtering in the t‐x and f‐x domains. Geophysics56, 2019–2026.
    [Google Scholar]
  25. HuangW., WangR., ChenY., LiH. and GanS.2016. Damped multichannel singular spectrum analysis for 3D random noise attenuation. Geophysics81, V261–V270.
    [Google Scholar]
  26. KhalilA., SunJ., ZhangY. and PooleG.2014. RTM noise attenuation and image enhancement using time‐shift gathers. 76th EAGE Conference and Exhibition, June 2014, Extended Abstract.
  27. LiS., LiuB., RenY., ChenY., YangS., WangY.et al. 2019. Deep‐learning inversion of seismic data. IEEE Transactions on Geoscience and Remote Sensing, 1–5.
    [Google Scholar]
  28. LiuB., GuoQ., LiS., LiuB., RenY., PangY.et al. 2020a. Deep learning inversion of electrical resistivity data. IEEE Transactions on Geoscience and Remote Sensing.
    [Google Scholar]
  29. LiuB., PangY., MaoD., WangJ., LiuZ., WangN.et al. 2020b. A rapid four‐dimensional resistivity data inversion method using temporal segmentation. Geophysical Journal International.
    [Google Scholar]
  30. LiuG., ChenX., DuJ. and WuK.2012. Random noise attenuation using f‐x regularized nonstationary autoregression. Geophysics77, V61–V69.
    [Google Scholar]
  31. LiuG., FomelS., JinL. and ChenX.2009. Stacking seismic data using local correlation. Geophysics74, V43–V48.
    [Google Scholar]
  32. LiuY., ZhangP. and LiuC.2017. Seismic data interpolation using generalised velocity‐dependent seislet transform. Geophysical Prospecting65, 82–93.
    [Google Scholar]
  33. MahdadA., DoulgerisP. and BlacquiereG.2011. Separation of blended data by iterative estimation and subtraction of blending interference noise. Geophysics76, Q9–Q17.
    [Google Scholar]
  34. MarfurtK.J.2014. Seismic attributes and the road ahead. SEG Technical Program Expanded Abstracts, 4421–4426).
  35. MarfurtK.J., SudhakerV., GersztenkornA., CrawfordK.D. and NissenS.E.1999. Coherency calculations in the presence of structural dip. Geophysics64, 104–111.
    [Google Scholar]
  36. Marple JrS.L. and CareyW.M.1989. Digital spectral analysis with applications. The Journal of the Acoustical Society of America86, 2043.
    [Google Scholar]
  37. MontanaC.A. and MargraveG.F.2004. Spatial prediction filtering in fractional fourier domains. SEG Technical Program, Expanded Abstract, 2112–2115.
  38. RussellB., HampsonD. and ChunJ.1990a. Noise elimination and the radon transform, Part 1. The Leading Edge9, 18–23.
    [Google Scholar]
  39. RussellB., HampsonD. and ChunJ.1990b. Noise elimination and the radon transform, Part 2. The Leading Edge9, 31–37.
    [Google Scholar]
  40. SacchiM.D. and NaghizadehM.2009. Adaptive linear prediction filtering for random noise attenuation. SEG Technical Program, Expanded Abstracts, 3347–3351.
  41. SiahsarM.A.N., AbolghasemiV. and ChenY.2017. Simultaneous denoising and interpolation of 2D seismic data using data‐driven non‐negative dictionary learning. Signal Processing141, 309–321.
    [Google Scholar]
  42. SiahsarM.A.N., GholtashiS., KahooA.R., ChenW. and ChenY.2017. Data‐driven multi‐task sparse dictionary learning for noise attenuation of 3D seismic data. Geophysics82, V385–V396.
    [Google Scholar]
  43. SpitzS.1991. Seismic trace interpolation in the fx domain. Geophysics56, 785–794.
    [Google Scholar]
  44. TreitelS.1974. The complex wiener filter. Geophysics39, 169–173.
    [Google Scholar]
  45. WangH., ZhangQ., ZhangG., FangJ. and ChenY.2020. Self‐training and learning the waveform features of microseismic data using an adaptive dictionary. Geophysics85, 1–61.
    [Google Scholar]
  46. YuleG.U.1927. Vii. On a method of investigating periodicities disturbed series, with special reference to Wolfer's sunspot numbers. Philosophical Transactions of the Royal Society London A226, 267–298.
    [Google Scholar]
  47. ZhangK., LinN., FuC., ZhangD., JinX. and ZhangC.2019. Reservoir characterisation method with multi‐component seismic data by unsupervised learning and colour feature blending. Exploration Geophysics50, 1–12.
    [Google Scholar]
  48. ZuS., ZhouH., RuR., JiangM. and ChenY.2019. Dictionary learning based on dip patch selection training for random noise attenuation. Geophysics84, V169–V183.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12941
Loading
/content/journals/10.1111/1365-2478.12941
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): 2D structures; Seismic data processing; Signal processing

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