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Method for wavelet denoising of multi‑angle prestack seismic data

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
When processing seismic survey data, it is desirable to reduce random noise without lowering the seismic resolution or lessening the number of seismic stacking folds. To this end, here we introduce a wavelet denoising of multi-angle (WDMA) method for prestack seismic data. Unlike the traditional method of directly stacking multi-angle gathers, our proposed WDMA method does not rely on the simple averaging of multi-angle prestack data, and when denoising the final result does not require averaging of direct stacking. Instead, WDMA first decomposes single-angle gathers through wavelet decomposition to determine local noise and obtain a detailed estimation, following which the average weighting coefficient is calculated and the data are reconstructed. Based on the above steps, we mainly studied the wavelet decomposition, optimization of the coefficient weight and weight mode, and the number of angle gathers. We used synthetic prestack angle gathers and field seismic data to validate the effectiveness of the proposed method. Even when the ratio of maximum noise amplitude to maximum effective signal amplitude was 60%, our proposed method could still achieve a good denoising effect by conducting WDMA two or more times. Compared with soft threshold wavelet denoising or the traditional stacking method, our results demonstrate that the proposed WDMA method can obtain high-quality seismic data using fewer frames of angle gathers and can simultaneously perform denoising and stacking.
Czasopismo
Rocznik
Strony
1515--1524
Opis fizyczny
Bibliogr. 24 poz., rys.
Twórcy
autor
  • State Key Laboratory of Natural Gas Hydrate, Beijing 100028, China
  • College of Geophysics, Chengdu University of Technology, Chengdu 610059, China
autor
  • State Key Laboratory of Natural Gas Hydrate, Beijing 100028, China
autor
  • State Key Laboratory of Natural Gas Hydrate, Beijing 100028, China
  • Zhanjiang Branch, CNOOC China Limited, Zhanjiang 524057, China
Bibliografia
  • 1. Aki K, Richards PG (2002) Quantitative seismology
  • 2. Bai M, Wu J (2018) Seismic deconvolution using iteartive transform-domain sparse inversion. J Seism Explor 27:103–116
  • 3. Cao J, Zhao J, Hu Z (2015) 3D seismic denoising based on a low-redundancy curvelet transform. J Geophys Eng 12(4):566–576
  • 4. Chen Y, Jin Z (2015) Simultaneously removing noise and increasing resolution of seismic data using waveform shaping. IEEE Geosci Remote Sens Lett 13(1):102–104
  • 5. Chen W, Yuan J, Chen Y, Gan S (2017a) Preparing the initial model for iterative deblending by median filtering. J Seism Explor 26(1):25–47
  • 6. Chen W, Zhang D, Chen Y (2017b) Random noise reduction using a hybrid method based on ensemble empirical mode decomposition. J Seism Explor 26(3):227–249
  • 7. Fattahi H, Karimpouli S (2016) Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods. Comput Geosci 20(5):1075–1094
  • 8. Habib W, Sarwar T, Siddiqui AM, Touqir I (2017) Wavelet denoising of multiframe optical coherence tomography data using similarity measures. IET Image Proc 11(1):64–79
  • 9. Han J, van der Baan M (2015) Microseismic and seismic denoising via ensemble empirical mode decomposition and adaptive thresholding. Geophysics 80(6):KS69–KS80. https://doi.org/10.1190/geo2014-0423.1
  • 10. Heil CE, Walnut DF (1989) Continuous and discrete wavelet transforms. SIAM Rev 31(4):628–666
  • 11. Hou S, Zhang F, Li X, Zhao Q, Dai H (2018) Simultaneous multicomponent seismic denoising and reconstruction via K-SVD. J Geophys Eng 15(3):681–694
  • 12. Kong D, Peng Z (2015) Seismic random noise attenuation using shearlet and total generalized variation. J Geophys Eng 12(6):1024–1035
  • 13. Li JH, Zhang YJ, Qi R, Liu QH (2017) Wavelet-based higher order correlative stacking for seismic data denoising in the curvelet domain. IEEE J Sel Top Appl Earth Observations Remote Sensing 10(8):3810–3820
  • 14. Luo X, Bhakta T (2017) Estimating observation error covariance matrix of seismic data from a perspective of image denoising. Comput Geosci 21(2):205–222
  • 15. Mayer MA, Borsdorf A, Wagner M, Hornegger J, Mardin CY, Tornow RP (2012) Wavelet denoising of multiframe optical coherence tomography data. Biomed Opt Express 3(3):572–589
  • 16. Ratas M (2019). Application of haar wavelet method for solving non-linear evolution equations. In: AIP Conference proceedings (Vol. 2116, No. 1, p. 330004). AIP Publishing LLC.
  • 17. Sang Y, Sun J, Meng X, Jin H, Peng Y, Zhang X (2019) Seismic random noise attenuation based on PCC classification in transform domain. IEEE Access 8:30368–30377
  • 18. Selesnick IW, Baraniuk RG, Kingsbury NC (2005) The dual-tree complex wavelet transform. IEEE Signal Process Mag 22(6):123–151
  • 19. Soleimani M, Rafiei M (2016) Imaging seismic data in complex structures by introducing the partial diffraction surface stack method. Stud Geophys Geod 60(4):644–661
  • 20. Walter GG, Shen X (2000) Wavelets and other orthogonal systems. CRC Press
  • 21. Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Transactions Image Process 13(4):600–612. https://doi.org/10.1109/TIP.2003.819861
  • 22. Wang H, Han L, Liu C, Wei Y (2014) Application of suppressing random noise in seismic data based on Trivashrink and DTCWT. Global Geology 17(4):231–237
  • 23. Xie ZM, Wang EF, Zhang GH, Zhao GC, Chen XG (2004) Seismic signal analysis based on the dual-tree complex wavelet packet transform. Acta Seismol Sin 17(1):117–122
  • 24. Yilmaz Öz (2001) Seismic data analysis: processing, inversion, and interpretation of seismic data. Society of Exploration Geophysicists. https://doi.org/10.1190/1.9781560801580
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7b94c29c-9503-4e6d-bfde-9c6436ececc1
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