Application of neural networks for seismic phase identification

The effectiveness of a multilayered feedforward neural network for seismic phase identification was investigated. The database consisted of seismograms from 75 earthquakes and 75 underground nuclear explosions. For learning, the conjugate gradient error backpropagation algorithm with a weight-elimination method was used. Results indicate that feedforward neural networks appear to outperform a conventional Bayesian classifier in a problem where the task was restricted to identifying only two of the principal regional phases, Pg and Lg, on earthquake and explosion seismograms of the western United States