Effects of Learning Parameters on Learning Procedure and Performance of a BPNN
We examined the effects of changing learning parameters on the learning procedure and performance of back-propagation neural networks used to pick seismic arrivals. The results show that such change mainly affects the speed of convergence of the learning procedures, and does not affect the BPNN structure and its overall performance. A relationship between the learning parameters and iteration number is obtained. This relationship may be used as a guide to check the convergence of the learning procedure and the BPNN performance. We also use a weight map of BPNN structure to analyze its interior and performance. Two BPNNs used to pick seismic arrivals from three-component and single-component seismograms have similar weight patterns and operate in a similar way, although they have different structures and trained by different training dataset.
learning procedure back-propagation neural network convergence seismic arrivals picking pattern recognition