Classification of seismic signals by integrating ensembles of neural networks

We examine a classification problem in which seismic waveforms of natural earthquakes are to be distinguished from waveforms of man-made explosions. We present an integrated classification machine (ICM), which is a hierarchy of artificial neural networks (ANN's) that are trained to classify the seismic waveforms. In order to maximize the gain of combining the multiple ANN's, we suggest construction of a redundant classification environment (RCE) that consists of several "experts" whose expertise depends on the different input representations to which they are exposed. In the proposed scheme, the experts are ensembles of ANN, trained on different Bootstrap replicas. We use various network architectures, different time-frequency decompositions of the seismic waveforms, and various smoothening levels in order to achieve an RCE. A confidence measure for the ensemble's classification is defined based on the agreement (variance) within the ensembles, and an algorithm for a nonlinear integration of the ensembles using this measure is presented. An implementation on a data set of 380 seismic events is described, where the proposed ICM had classified correctly 92% of the testing signals. The comparison we made with classical methods indicates that combining a collection of ensembles of ANN's can be used to handle complex high dimensional classification problems.