Event identification from seismic/magnetic feature vectors: a comparative study

The event identification problem plays a large role in the application of unattended ground sensors to the monitoring of borders and checkpoints. The choice of features and methods for classifying features affects how accurately these classifications are made. Finding features which reliably distinguish events of interest may require measurements based on separate physical phenomena. Classification methods include neural net versus fuzzy logic approaches, and within the neural category, different architectures and transfer functions for reaching decisions. This study examines ways of optimizing feature sets and surveys common techniques for classifying feature vectors corresponding to physical events. We apply each technique to samples of existing data, and compare discrimination attributes. Specifically, we calculate the confusion matrices for each technique applied to each sample dataset, and reduce them statistically to scalar scores. In addition, we gauge how the accuracy of each method is degraded by reducing the feature vector length by one element. Finally, we gather rough estimates of the relative cpu performance of the forward prediction algorithms