A hybrid neural network for seismic pattern recognition

An artificial neural network designed to recognize seismic patterns is presented. It is a hybrid model because it consists of both unsupervised and supervised learning. The unsupervised layer plays the feature extracting role, and the supervised layer is responsible for class decision. When learning is completed, the user presents a seismic pattern to this model to obtain a decision on to which class the input pattern belongs. If the model fails to recognize a pattern, that means there are no nodes located in the output layer that produce a large enough response. Then, the model will automatically decrease its vigilance threshold to become more tolerant. This automatic-tolerance-adjusted mechanism is demonstrated on some examples, such as recognizing patterns in translation, scaling, noise, or deformation. The concepts are based on competitive learning from Kohonen, self-organization learning from Fukushima, and the delta rule