Classification of seismic waveforms by integrating ensembles of neural networks

The problem considered is the discrimination between natural and artificial seismic events, based on their waveform recording. We build a classification environment consists of several ensembles of neural networks trained on boot-strap sample sets, using various data representations and architectures. The integration of the different ensembles is made in a nonconstant signal adaptive manner, using a posterior confidence measure based on the agreement (variance) within the ensembles. The proposed integrated classification machine achieved 92.1% correct classification on the seismic test data. Cross validation tests and comparisons indicate that such integration of a collection of ANN's ensembles is a robust way for handling high dimensional problems with a complex nonstationary signal space as in the current seismic classification problem.