An experiment in the use of trained neural networks for regional seismic event classification

Abstract

A neural network employing the back propagation learning paradigm has been developed as an experiment in the automatic classification of small regional earthquakes and quarry explosions. The network has been used in the analysis of 66 events recorded by the NORESS array in southern Norway. The input vector consists of three broadband discriminants including the spectral ratios of Sn/Pn and Lg/Pn waves, and the mean cepstral variance of Pn, Sn, and Lg. Two hidden layers are used, consisting of 8 and 2 units. The output vector consists of two units which correspond to the classification of explosion or earthquake. The network was first trained using input vectors from the entire dataset. The network was able to perfectly model the training set with no classification errors. For comparison, an optimum linear classifier used with the same dataset resulted in 5 errors and 19 uncertain classifications. Next, the network was trained with half of the events and tested with the remaining half. This resulted in 5 errors and 2 uncertain classifications. This compares with 5 errors and 18 uncertain events for the optimum linear classifier. The apparent advantage of the neural network over the optimum linear classifier is the network's ability to model complex decision regions and in the reduction of the number of uncertain events.

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