Publications Internationales
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13
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Item Tomographic velocity images by artificial neural networks(2007) Djarfour, Noureddine; Ferahtia, J.; Baddari, K.The present study deals with the use of Elman artificial neural network (feedback connexion) to reconstruct the velocity image from a traveltime in the seismic tomography experiment. This recurrent connection provides the advantage to store values from the previous time step, which can be used in the actual time step. The backpropagation algorithm has been used to learn the suggested neural network. Efficiency of these networks has been tested in training and generalization phases. A comparative reconstruction with two classical methods was performed using backprojection and Algebraic Reconstruction Techniques (ART). The obtained results clearly show improvements of the quality of the reconstruction obtained by artificial neural networksItem Impulse noise reduction in 2D electrical resistivity imaging data based on fuzzy logic(IEEE, 2011) Ferahtia, J.; Djarfour, Noureddine; Baddari, Kamel; Khaldoun, AsmaeItem Seismic noise filtering based on Generalized Regression Neural Networks(Elsevier, 2015) Djarfour, Noureddine; Ferahtia, Jalal; Babaia, Foudel; Baddari, Kamel; Said, El-adjThis paper deals with the application of Generalized Regression Neural Networks to the seismic data filtering. The proposed system is a class of neural networks widely used for the continuous function mapping. They are based on the well known nonparametric kernel statistical estimators. The main advantages of this neural network include adaptability, simplicity and rapid training. Several synthetic tests are performed in order to highlight the merit of the proposed topology of neural network. In this work, the filtering strategy has been applied to remove random noises as well as source-related noises from real seismic data extracted from a field in the South of Algeria. The obtained results are very promising and indicate the high performance of the proposed filter in comparison to the well known frequency–wavenumber filterItem Application of a radial basis function artificial neural network to seismic data inversion(Elsevier, 2009) Baddari, Kamel; Aïfa, Tahar; Djarfour, Noureddine; Ferahtia, JalalItem Incorporation of a non-linear image filtering technique for noise reduction in seismic data(Springer, 2010) Ferahtia, Jalal; Baddari, Kamel; Djarfour, Noureddine; Kassouri, Abdel KaderItem Seismic noise attenuation by means of an anisotropic non-linear diffusion filter(Elsevier, 2011) Baddari, Kamel; Ferahtia, Jalal; Aïfa, Tahar; Djarfour, NoureddineItem Seismic attributes combination to enhance detection of bright spot associated with hydrocarbons(Taylor & Francis, 2012) Farfour, M.; Yoon, W.J.; Ferahtia, J.; Djarfour, NoureddineItem Image-based processing techniques applied to seismic data filtering(Elsevier, 2013) Ferahtia, J.; Aïfa, Tahar; Baddari, K.; Djarfour, Noureddine; Eladj, S.Item A fuzzy logic-based filter for the removal of spike noise from 2D electrical resistivity data(Elsevier, 2012) Ferahtia, J.; Djarfour, Noureddine; Baddari, K.; Kheldoun, AissaIn this paper, a filter based on fuzzy logic is proposed to remove spike noise from 2 dimensional electrical resistivity data. The noise detection used in this paper is based on differentiating noisy samples from the central sample inside a moving window. These fuzzy derivatives are used by the fuzzy inference system to detect corrupted samples. To assess the performance of the proposed filter for the removal of spike noise, the root-mean squared error as well as the signal-to-noise ratio were used as an objective criterion. It has been demonstrated by synthetic and real examples that the proposed filter achieves quite good results compared to the standard median filter as well as to the very effective SD-ROM filterItem Application of signal dependent rank-order mean filter to the removal of noise spikes from 2D electrical resistivity imaging data(2009) Ferahtia, J.; Djarfour, Noureddine; Baddari, K.; Guérin, R.It is well-known that when inverting two-dimensional (2D) electrical resistivity data, a major source of errors is the presence of noise and in particular noise spikes. The popular median filter is often applied to the removal of single spikes. However, when the signal is highly corrupted with successive spikes, the median filter performance is poor. This paper deals with the use of the signal dependent rank-order mean filter for the detection and removal of noise spikes from highly corrupted 2D electrical resistivity imaging data. In addition to its computational simplicity, this filter is shown to be extremely robust, even in the presence of very strong noise, especially when it is applied recursively. The signal dependent rank-order mean filter was tested on 2D synthetic resistivity data contaminated by near-surface inhomogeneities and the results confirmed efficient removal of the disturbances normally associated with near-surface inhomogeneities. The signal dependent rank-order mean filter was also applied to field data and demonstrated its ability to significantly improve the accuracy of the inversion process and to produce good visual results in the inverted electrical sections