1887

Abstract

Summary

Denoising is of great importance in seismic data processing. A variety of denoising methods have been proposed in the past several decades. However, it remains a longstanding problem to effectively separate noise and useful signal in the 2D/3D data set. In addition, seismic denoising is often performed trace by trace and the lateral continuity is usually not taken into consideration. To solve these two problems, we propose a novel method named adaptive empirical wavelet transform (AEWT) for seismic random noise reduction. In the AEWT, we take advantage of the outstanding denoising performance of EWT and the lateral continuity of multidimensional seismic data. When dealing with 2D data sets, the AEWT considers the spatial lateral continuity between the traces using the dominant frequency criterion (DFC) method, which selects the intrinsic mode functions by adaptively judging the dominant frequency of each trace. Both 2D synthetic and field data examples show successful performance of the proposed AEWT.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201700578
2017-06-12
2024-03-29
Loading full text...

Full text loading...

References

  1. Chen, Y. and Fomel, S.
    [2015] Random noise attenuation using local signaland-noise orthogonalization. Geophysics, 80(6), WD1–WD9.
    [Google Scholar]
  2. ChenY. and MaJ.
    [2014] Random noise attenuation by f-x empirical-mode decomposition predictive filtering. Geophysics, 79(3), V81–V91.
    [Google Scholar]
  3. Gilles, J. G.
    [2013] Empirical wavelet transform. IEEE Transactions on Signal Processing, 61(16), 3999–4010.
    [Google Scholar]
  4. Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., Zheng, Q., Yen, N., Tung, C. and Liu, H.
    [1998] The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences. 454(1971), 903–995.
    [Google Scholar]
  5. Liu, W., Cao, S. and Chen, Y.
    [2016] Seismic time-frequency analysis via empirical wavelet transform. IEEE Geoscience and Remote Sensing Letters, 13, 28–32.
    [Google Scholar]
  6. Liu, W., Cao, S., Chen, Y. and Zu, S.
    [2016] An effective approach to attenuate random noise based on compressive sensing and curvelet transform. Journal of Geophysics and Engineering, 13(2), 135–145.
    [Google Scholar]
  7. Zhang, R. and Ulrych, T.
    [2003] Physical wavelet frame denoising. Geophysics, 68(1), 225–231.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201700578
Loading
/content/papers/10.3997/2214-4609.201700578
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