AccScience Publishing / JSE / Article
Submit to JSE
Cite this article
2
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
ARTICLE

Artificial neural networks applied to reduce the noise type of ground roll

P.L.B. SOARES1 ,  J.P. SILVA1,2 ,  M.D. SANTOS3
Show Less
1 Postgraduate Program in Communication Systems and Automation, PPGSCA - Federal University of Semiarid Region - UFERSA, Mossoro, Brazil.,
2 Department of Electrical Engineering, Federal University of Rio Grande do Norte - UFRN, Natal, Brazil.,
3 Department of Informatics, Federal University of Paraiba - UFPB, Joao Pessoa, Brazil.,
JSE 2015, 24(1), 1–14;
Submitted: 9 June 2025 | Revised: 9 June 2025 | Accepted: 9 June 2025 | Published: 9 June 2025
© 2025 by the Authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Download PDF
Cite
XML
Abstract

Soares, P.L.B, Silva, J.P and Santos, M.D., 2015. Artificial neural networks applied to reduce the noise type of ground roll. Journal of Seismic Exploration, 24: 1-14. Seismograms exhibit a good approximation of a geological structure. However, the images they show are generally contaminated by irrelevant information. The noise ground roll in these images can contribute significantly to the distortion of the data present in the desired information, due to the scattering of waves in deeper regions of geological layers. In-this work, we used a method based on Haar and Daubechies wavelets applied in conjunction with artificial neural networks to reduce the noise ground roll. This type of noise is normally present in earth seismic images and it is similar to those found in oil reservoirs.

Keywords
artificial neural networks
wavelets
ground roll
image processing
References
  1. Corso, G., Kuhn, P., Lucena, L.S. and Thomé, Z., 2003. Seismic ground roll time-frequency
  2. filtering using the Gaussian wavelet transform. Physical A, 318: 551-561.
  3. Dash, P.C., Mishra, M.K., Tarasia, N., Das, S. and Mund, G.B., 2012. An ANN based two
  4. pass-two phase adaptive filtering of a digital image corrupted by SPN. Internat. J. Comput.
  5. Applic., 40: 20-27.
  6. Daubechies, I., 1988. Orthonormal bases of compactly supported wavelets. Comm. Pure Appl.
  7. Mathemat., 41: 909-996.
  8. Daubechies, I., 1990. The wavelets transform, time-frequency localization and signal analysis. IEEE
  9. Transact. Informat. Theory, 36: 961-1005.
  10. Deighan, A.J. and Watts, D.R., 1997. Ground roll suppression using the wavelet transform.
  11. Geophysics, 62: 1896-1903.
  12. Ensiklopedi Seismik Online. 2012. Seismic Processing with Seismic Unix - Part 2.
  13. {http://ensiklopediseismik. blogspot.com. br/2010/11/seismic-processing-with-seismic-unix_22.html].
  14. Filho, O.M. and Neto, H.V., 1999. Processamento Digital de Imagens. Brasport, Rio de Janeiro.
  15. Kaliraj, G. and Baskar, S., 2010. An efficient approach for the removal of impulse noise from the
  16. corrupted image using neural network based impulse detector. Imagem Vision Comput., 28:
  17. 458-466.
  18. Leite, F.E.A., Montagne, G.C.R., Vasconcelos, G.L. and Lucena, L.S., 2008. Optimal wavelet
  19. filter for suppression of coherent noise with an application to 28 seismic data. Physica A,
  20. 387: 1439-1445.
  21. Linville, A.F. and Meek, R.A., 1997. A procedure for optimally removing localized coherent noise.
  22. Geophysics, 60: 191-203.
  23. Ma, X., Zhang, J. and Song, A., 2009. 3D reservoir modelling with the aid of artificial neural
  24. networks. Internat. Collog. Comput., Communic., Control, Managem., 4: 446-450.
  25. Mallat, S.A., 1989. Theory for multiresolution signal decomposition: wavelet representation. IEEE
  26. Trans. Pattern Anal. Mach. Intellig., 11: 674-693.
  27. Mishra, S.K., Panda, G. and Meher, S., 2010. Chebysev functional link artificial neural networks
  28. for denoising of image corrupted by salt and pepper noise. ACEEE Internat. J. Signal Image
  29. Process., 1: 42-46.
  30. Pan, G.W., 2001. Wavelet in Electromagnetic and Devices Modelling. John Wiley & Sons Inc.,
  31. New York.
  32. Santos, M.D., Doria, A.N., Mata, W. and Silva, J.P., 2011. New antena modelling using wavelets
  33. for heavy oil thermal recovering methods. J. Petrol. Sci. Engin., 76: 63-75.
  34. Saradhadevi, V. and Sundaram, V., 2012. An enhanced two-stage impulse noise removal technique
  35. for SAR images based on fast ANFIS and fuzzy decision. Europ. J. Scientif. Res., 68:
  36. 506-522.
  37. Shi, W.J., Wang, X.Z., Zhang, D.Q., Wang, F. and Ma, M.Y., 2006. A novel FOCAL technique
  38. based on BP-ANN. Optik, 117: 145-150.
  39. Thomas, J.E., 2001. Fundamentos de Engenharia de Petroleo. Interciéncia Petrobras, Rio de
  40. Janeiro.
  41. Yilmaz, O., 2001. Seismic Data Analysis. SEG, Tulsa, OK. Vol.1: 150-169.
  42. Yilmaz, O., 2003. Seismic Data Processing. SEG, Tulsa, OK, 526 pp.
Share
Back to top
Journal of Seismic Exploration, Electronic ISSN: 0963-0651 Print ISSN: 0963-0651, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept