Publications Internationales
Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13
Browse
4 results
Search Results
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 Application of a radial basis function artificial neural network to seismic data inversion(Elsevier, 2009) Baddari, Kamel; Aïfa, Tahar; Djarfour, Noureddine; Ferahtia, JalalItem Application of feedback connection artificial neural network to seismic data filtering(Elsevier, 2008) Djarfour, Noureddine; Aïfa, Tahar; Baddari, K.; Mihoubi, A.; Ferahtia, J.The Elman artificial neural network (ANN) (feedback connection) was used for seismic data filtering. The recurrent connection that characterizes this network offers the advantage of storing values from the previous time step to be used in the current time step. The proposed structure has the advantage of training simplicity by a back-propagation algorithm (steepest descent). Several trials were addressed on synthetic (with 10% and 50% of random and Gaussian noise) and real seismic data using respectively 10 to 30 neurons and a minimum of 60 neurons in the hidden layer. Both an iteration number up to 4000 and arrest criteria were used to obtain satisfactory performances. Application of such networks on real data shows that the filtered seismic section was efficient. Adequate cross-validation test is done to ensure the performance of network on new data setsItem Acoustic impedance inversion by feedback artificial neural network(Elsevier, 2010) Baddari, K.; Djarfour, Noureddine; Aïfa, Tahar; Ferahtia, J.The determination of acoustic impedance distribution from the seismic data field measurement can be expressed as an ill-posed inverse problem. This work deals with the use of the Elman artificial neural network (ANN) (feedback connection) for the seismic data inversion. In the proposed structure the hidden neuron outputs from the previous time step are fed back to their inputs through time delay units; this enables them to process temporal behaviour and provide multi-step-ahead predictions. The ANN architectures and learning rules are presented to allow the best estimate of acoustic impedance from seismic data. The effects of network architectures using 5 to 60 neurons and 10 to 90 neurons in the hidden layer respectively for synthetic and real data on the rate of convergence and prediction accuracy of ANN models are discussed. The behaviour of networks observed on training data is very similar to the one observed on test data. The results obtained clearly prove the feasibility of the proposed method for seismic data inversion by feedback neural networks. Different tests indicate that the back-propagation conjugate gradient algorithm can easily train the proposed Elman ANN structure without getting stuck in local minima