Abstract
One of the first operations in seismic signal processing system is to distinguish between valid and invalid records. Since valid signals are characterized by a combination of their time and frequency properties, wavelets are natural candidates for describing seismic features in a compact way. This paper investigates an approach to seismic pattern recognition comprising wavelet-based feature extraction, feature selection based on the mutual information criterion and neural classification based on feedforward networks. The ability of the wavelet transform to capture discriminating information from seismic data in a small number of features is compared against alternative feature reduction techniques, including statistical moments.
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