News and Announcements
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: 29 April 2014 | Accepted: 9 November 2014 | Published: 1 February 2015
© 2015 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/ )
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
- Corso, G., Kuhn, P., Lucena, L.S. and Thomé, Z., 2003. Seismic ground roll time-frequencyfiltering using the Gaussian wavelet transform. Physical A, 318: 551-561.
- Dash, P.C., Mishra, M.K., Tarasia, N., Das, S. and Mund, G.B., 2012. An ANN based twopass-two phase adaptive filtering of a digital image corrupted by SPN. Internat. J. Comput.Applic., 40: 20-27.
- Daubechies, I., 1988. Orthonormal bases of compactly supported wavelets. Comm. Pure Appl.Mathemat., 41: 909-996.
- Daubechies, I., 1990. The wavelets transform, time-frequency localization and signal analysis. IEEETransact. Informat. Theory, 36: 961-1005.
- Deighan, A.J. and Watts, D.R., 1997. Ground roll suppression using the wavelet transform.Geophysics, 62: 1896-1903.
- Ensiklopedi Seismik Online. 2012. Seismic Processing with Seismic Unix - Part 2.{http://ensiklopediseismik. blogspot.com. br/2010/11/seismic-processing-with-seismic-unix_22.html].
- Filho, O.M. and Neto, H.V., 1999. Processamento Digital de Imagens. Brasport, Rio de Janeiro.
- Kaliraj, G. and Baskar, S., 2010. An efficient approach for the removal of impulse noise from thecorrupted image using neural network based impulse detector. Imagem Vision Comput., 28:458-466.
- Leite, F.E.A., Montagne, G.C.R., Vasconcelos, G.L. and Lucena, L.S., 2008. Optimal waveletfilter for suppression of coherent noise with an application to 28 seismic data. Physica A,387: 1439-1445.
- Linville, A.F. and Meek, R.A., 1997. A procedure for optimally removing localized coherent noise.Geophysics, 60: 191-203.
- Ma, X., Zhang, J. and Song, A., 2009. 3D reservoir modelling with the aid of artificial neuralnetworks. Internat. Collog. Comput., Communic., Control, Managem., 4: 446-450.
- Mallat, S.A., 1989. Theory for multiresolution signal decomposition: wavelet representation. IEEE
- Trans. Pattern Anal. Mach. Intellig., 11: 674-693.
- Mishra, S.K., Panda, G. and Meher, S., 2010. Chebysev functional link artificial neural networksfor denoising of image corrupted by salt and pepper noise. ACEEE Internat. J. Signal ImageProcess., 1: 42-46.
- Pan, G.W., 2001. Wavelet in Electromagnetic and Devices Modelling. John Wiley & Sons Inc.,New York.
- Santos, M.D., Doria, A.N., Mata, W. and Silva, J.P., 2011. New antena modelling using waveletsfor heavy oil thermal recovering methods. J. Petrol. Sci. Engin., 76: 63-75.
- Saradhadevi, V. and Sundaram, V., 2012. An enhanced two-stage impulse noise removal techniquefor SAR images based on fast ANFIS and fuzzy decision. Europ. J. Scientif. Res., 68:506-522.
- Shi, W.J., Wang, X.Z., Zhang, D.Q., Wang, F. and Ma, M.Y., 2006. A novel FOCAL techniquebased on BP-ANN. Optik, 117: 145-150.
- Thomas, J.E., 2001. Fundamentos de Engenharia de Petroleo. Interciéncia Petrobras, Rio deJaneiro.
- Yilmaz, O., 2001. Seismic Data Analysis. SEG, Tulsa, OK. Vol.1: 150-169.
- Yilmaz, O., 2003. Seismic Data Processing. SEG, Tulsa, OK, 526 pp.
