1887

Abstract

Summary

Affected by complex environmental factors, the seismic data collected in desert area are disturbed by a large number of random noise, which makes the signal-to-noise ratio of seismic data low. Moreover, the frequency band of low frequency random noise will overlap with some effective signal bands, which is not conducive to the realization of signal-noise separation. Considering the problems mentioned above, a low-rank matrix approximation algorithm based on variational mode decomposition is proposed to suppress the random noise of desert seismic data. In this paper, we decompose the input signal into several low-rank mode components by the variational mode decomposition method, and extract the intrested low-rank components by solving the weighted nuclear norm minimization of the low-rank modes. The proposed method suppresses a large amount of low-frequency random noise in desert area, and solves the problem of separating part of effective signal from noise in the same frequency band. Through the comparative analysis with other methods in numerical simulation experiments on synthetic and actual seismic data in desert areas, the proposed method suppresses low-frequency noise effectively and recover the events continuously.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201901352
2019-06-03
2024-03-28
Loading full text...

Full text loading...

References

  1. Dragomiretskiy, K. and Zosso, D.
    [2014] Variational mode decomposition.IEEE Transactions on Signal Processing, 62(3), 531–544.
    [Google Scholar]
  2. Gu, S., Zhang, L., Zuo, W. and Feng, X.
    [2014] Weighted Nuclear Norm Minimization with Application to Image Denoising. 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, 23–28.
    [Google Scholar]
  3. Ma, J., Plonka, G. and Chauris, H.
    [2010] A new sparse representation of seismic data using adaptive easy-path wavelet transform.IEEE Geoscience and Remote Sensing Letters, 7(3), 540–544.
    [Google Scholar]
  4. Naghizadeh, M. and Sacchi, M.
    [2012] Multicomponent f-x seismic random noise attenuation via vector autoregressive operators.GEOPHYSICS, 77(2), V91–V99.
    [Google Scholar]
  5. Oropeza, V. and Sacchi, M.
    [2011] Simultaneous seismic data denoising and reconstruction via multichannel singular spectrum analysis.GEOPHYSICS, 76(3), V25–V32.
    [Google Scholar]
  6. Wang, T., Zhang, M., Yu, Q. and Zhang, H.
    [2012] Comparing the applications of EMD and EEMD on time–frequency analysis of seismic signal.Journal of Applied Geophysics, 83, 29–34.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201901352
Loading
/content/papers/10.3997/2214-4609.201901352
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