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 Bootstrap Sample Sets, using various data representations and architectures. The integration of the different Ensembles is made in a non-constant 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 non-stationary signal space as in the current Seismic Classification problem.