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Analysis and Testing of High-Frequency Regional Seismic Discriminants

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Final rept. 27 Aug 1991-31 Jul 1992

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The objective of this research was to test the applicability of artificial neural networks ANNs for seismic event identification. Research has focused on 101 small events within regional distances of the NORESS array for which independent source identifications are available. Twelve signal parameters were extracted from the P-, S-, and Lg-waves of each event. A number of descriptive statistics were calculated for the purpose of understanding the parameter dataspace. These included means, variances, tests for normality, and cross-correlations between signal parameters. A Stepwise Discriminant Analysis was performed to eliminate parameters which do not contribute to the identification. The six most important parameters were the PnLg spectral ratio from 5-10 Hz, the mean cepstral variance, the PnSn wideband spectral ratio, the PnLg spectral ratio from 2-5 Hz, the PnSn spectral ratio from 2-5 Hz, and the PnLg spectral ratio from 10-20 Hz. Principal Components Analysis was applied to the reduced parameter dataset to aid in the design of the ANN. Three principal components account for more than 85 of the data variance, providing a useful guide to the appropriate hidden layer dimensionality. The final ANN design consisted of an input layer with 12 units, a single hidden layer with three units, and an output layer of one unit. The ANN was trained using the backpropagation learning algorithm. The ANN correctly identified all but two of the 101 events, two explosions, were misclassified as earthquakes. Experiments were also conducted using inputs from 1, 5, 10, 15, 20, and 25 NORESS elements as a means of testing the ANN sensitivity to SNR. Identification performance increased from 77 for one element to 98 for 25 elements.

Subject Categories:

  • Seismology
  • Nuclear Weapons

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