  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 sets.
