A comparison of neural network performance for seismic phase identification

Abstract

Traditional techniques of analysis and interpretation of seismic events involve a series of complex steps involving sophisticated signal processing as well as many manual tasks. Automating each of these steps is an important goal of this ongoing research. The paper discusses the use of neural networks in performing phase identification, namely the discrimination of distinct seismic waves within a seismogram. The scope is further restricted to the identification of only two of the regional principal phases, Pg and Lg, among the signals collected in the western United States. Using a database of 75 earthquakes and 75 underground nuclear explosions, the performance of several types of neural networks was compared. The performance of probabilistic neural network (PNN), radial basis function (RBF) network and learning vector quantization (LVQ) network is compared with a back-propagation network that combines the conjugate-gradient method with a weight-elimination strategy. The results indicate that the latter outperformed all other methods tested.