Classification of seismic waveforms by integrating ensembles of neural networks

Yair Shimshoni, Nathan Intrator

Research output: Contribution to conferencePaperpeer-review

Abstract

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.

Original languageEnglish
Pages368-376
Number of pages9
StatePublished - 1996
EventProceedings of the 1996 1st International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, NICROSP'96 - Venice, Italy
Duration: 21 Aug 199623 Aug 1996

Conference

ConferenceProceedings of the 1996 1st International Workshop on Neural Networks for Identification, Control, Robotics, and Signal/Image Processing, NICROSP'96
CityVenice, Italy
Period21/08/9623/08/96

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