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A radial basis network for seismic signal discrimination

An application of the radial basis function network to seismic waveform classification is presented. The network performs generalization and discrimination of input patterns using an external teacher. Modifications to this scheme are described. They include: (1) changing the size of the spheres; (2) using a random walk scheme during testing; (3) gradually decreasing the initial radii to avoid overlap of two distinct regions; (4) a conflict resolution mechanism; and (5) a simple means of decreasing the sphere radius. The applications to seismic signals include using the moments over a sliding window and the first several points of a wavelet. The speed of training of this network exceeds that of backpropagation with the same error rate.