@article{MAKHILLJEAS20072412752,
    title = {Tomographic Velocity Images by Artificial Neural Networks},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {2},
    number = {4},
    pages = {775-782},
    year = {2007},
    issn = {1816-949x},
    doi = {jeasci.2007.775.782},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2007.775.782},
    author = {N. Djarfour,J. Ferahtia and},
    keywords = {Elman neuron networks training,back-propagation,traveltime,velocity,tomography,backprojection,ART},
    abstract = {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.}
    }