Neural network classification of subsurface targets

A classification technique for classifying subsurface targets based on the measured electric fields was developed in this paper. It was observed that subsurface targets can successfully be classified by using the magnitude of the scattered fields with the offset-VSP setup. The performance of the classification scheme was investigated with three different classification algorithms. It was observed that a neural network used with a distance criterion provided the best performance for this application. Use of the classical Bayesian classifier results in more misclassification than the neural network algorithms. The performance of both neural network algorithms exceeded the performance of the classical Bayesian classifier. The effect of the type of training data on the performance of the classifier was also studied in this paper. Training with a broad range of SNRs results in better classification rates as expected. But classifiers trained at 10 dB SNR also result in comparable performance. This unusual behavior shown by the network in this investigation is valid only for this specific application, and is probably due to the type of data used for classification.<>