1887

Abstract

Summary

This research introduces a method for background random noise attenuation in seismic data giving priority to the preservation of coherent seismic events and automation of algorithm. Since statistical characteristics of random noise are different than those of coherent events, in the proposed method, after defining a few statistical features, fuzzy C-Mean clustering was carried out on some randomly selected data samples from the seismic section. Then, the resulting membership functions along with the output of the adaptive Wiener filter were used so that automatic training of ANFIS could take place. Then, the acquired weights of the ANFIS were generalized to the whole data set based on the calculation of the statistical features. The proposed method was applied on both synthetic and real data sets and the results were compared to those of the conventional methods. The research findings revealed that the method was of a considerably higher performance in random noise attenuation as well as preserving the coherent events.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201601589
2016-05-30
2024-03-29
Loading full text...

Full text loading...

References

  1. Al-YahyaK.
    [1991] Application of the partial Karhunen-Loeve transform to suppress random noise in seismic sections. Geophysical Prospecting39, 77–93.
    [Google Scholar]
  2. Aminzadeh, F., GrootP. D.
    [2004] Soft Computing for qualitative and quantitative seismic object and reservoir property prediction, Part 1. Neural Network Applications, 22, 49–54.
    [Google Scholar]
  3. BekaraM., BaanM. V. D.
    [2009] Random and coherent noise attenuation by empirical mode decomposition. Geophysics74(5), 89–98.
    [Google Scholar]
  4. Candy, J. V.
    , [2009] Bayesian signal processing: Classical, modern and particle filtering methods. John Wiley & Sons.
    [Google Scholar]
  5. DjarfourN., AifaT., Baddarik., Mihoubi, A. FerahtiaJ.
    [2008] Application of feedback connection artificial neural network to seismic data filtering. C. R. Geoscience, 340, 335–344.
    [Google Scholar]
  6. FaniR., HashemiH.
    [2011] Random noise attenuation by application of GK clustering on relevant seismic attributes. 124th SEG Conference, Tokyo, Japan.
    [Google Scholar]
  7. JengY., LiY. W., ChenC. S., ChienH. Y.
    [2009] Adaptive filtering of random noise in near-surface seismic and ground-penetrating radar data. Journal of Applied Geophysics68, 36–46.
    [Google Scholar]
  8. LariH. A., GholamiA.
    [2014] TV regularized Bregman iteration for seismic random noise attenuation. Journal of Applied Geophysics, 109, 233–241.
    [Google Scholar]
  9. Lin, H.
    [2014] Seismic random noise elimination by adaptive time-frequency peak filtering. IEEE Geosciences and Remote Sensing Letters11(1), 337–341.
    [Google Scholar]
  10. LiuY., LiuC., YangD.
    [2009] A 1D time-varying median filter for seismic random, spike-like noise elimination. Geophysics74(1), 17–24.
    [Google Scholar]
  11. LiuY.
    (2013) Noise reduction by vector median filtering. Geophysics78(3), 79–86.
    [Google Scholar]
  12. HajianA., ZomorrodianH., StylesP.
    [2012] Simultaneous estimation of shape factor and depth of subsurface cavities from residual gravity anomalies using feed-forward back propagation neural networks. Acta Geophysica60(4), 1043–1075.
    [Google Scholar]
  13. KimiaefarR., SiahkoohiH. R., HajianA. R., KalhorA.
    [2015] Seismic Random Noise Attenuation Using Artificial Neural Network and Wavelet Packet Analysis. Manuscript submitted for publication in Arabian Journal of Geosciences.
    [Google Scholar]
  14. Miller, J. J.
    [2000] Four regional seismic lines: National Petroleum Reserve- Alaska. USGS Geologic Division, Central Region Energy Team, USA.
    [Google Scholar]
  15. Neelamani, R., BaumsteinA. I., GillardD. G., HadidiM. T., SorokaW. I.
    [2008] Coherent and random noise attenuation using the curvelet transform. The Leading Edge27, 240–248.
    [Google Scholar]
  16. RawatA., DyalS. S.
    [2010] Resolution enhancement of seismic data using stationary wavelet transform. 8th Biennial International Conference & Exposition on Petroleum Geophysics, Hyderabad.
    [Google Scholar]
  17. SheriffR. E.
    [1997] Seismic resolution a key element. AAPG Explorer18(10), 44–51.
    [Google Scholar]
  18. Stein, R., Bartley, N.
    (1983) Continuously time-variable recursive digital band-pass filters for seismic signal processing: Geophysics, 48, 702–712.
    [Google Scholar]
  19. ZhangY., TianX., DengX., CaoY.
    [2010] Seismic denoising based on modified BP neural network. Sixth International Conference on 4:1825–1829.
    [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201601589
Loading
/content/papers/10.3997/2214-4609.201601589
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