Application of quadratic neural networks to seismic signal classification

Abstract

This paper solves the seismic signal classification problem using the quadratic neural networks with closed-boundary discriminating surfaces. In this study, we have demonstrated the quadratic neural network (QNN) potential capabilities in application to the seismic signal classification problems and show that the efficiency achieved here, is much better to what obtained with conventional multilayer neural networks. Firstly, we have performed some pre-processing on the long period recordings to cancel out the instrumental and attenuation side effects. Secondly, we have extracted the ARMA filter coefficients of the windowed P-wave phase through some matrix manipulations using the conventional Prony ARMA modeling scheme. The derived coefficients are then applied to QNN for training and classification. The results have shown that a quadratic neuron is likely to have a performance similar to that of a multilayer perceptron when the target is to discriminate distribution of points in clusters within the input space.