TY  - JOUR
T1  - Tomographic Velocity Images by Artificial Neural Networks
AU - , N. Djarfour AU - , J. Ferahtia AU - , K. Baddari 
JO  - Journal of Engineering and Applied Sciences
VL  - 2
IS  - 4
SP  - 775
EP  - 782
PY  - 2007
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2007.775.782
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2007.775.782
KW  - Elman neuron networks training
KW  -back-propagation
KW  -traveltime
KW  -velocity
KW  -tomography
KW  -backprojection
KW  -ART
AB  - 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 networks.
ER  -