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"DTIC ADA252442: Data To Test And Evaluate The Performance Of Neural Network Architectures For Seismic Signal Discrimination. Volume 2. Neural Computing For Seismic Phase Identification" and the language of the book is English.


“DTIC ADA252442: Data To Test And Evaluate The Performance Of Neural Network Architectures For Seismic Signal Discrimination. Volume 2. Neural Computing For Seismic Phase Identification” Metadata:

  • Title: ➤  DTIC ADA252442: Data To Test And Evaluate The Performance Of Neural Network Architectures For Seismic Signal Discrimination. Volume 2. Neural Computing For Seismic Phase Identification
  • Author: ➤  
  • Language: English

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  • Internet Archive ID: DTIC_ADA252442

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"DTIC ADA252442: Data To Test And Evaluate The Performance Of Neural Network Architectures For Seismic Signal Discrimination. Volume 2. Neural Computing For Seismic Phase Identification" Description:

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This report describes the application of a neural computing approach for automated initial identification of seismic phases (P or S) recorded by 3- component stations. We use a 3-layer back-propagation neural network to identify phases on the basis of their polarization attributes. This approach is much easier to develop than a more traditional rule-based system because of the high-dimensionality of the input (8-10 polarization attributes), and because the data are station-dependent. The neural network approach also performs 3-7% better than a linear multivariate method. Most of the gain is for signals with low signal-to-noise ratio since the non-linear neural network classifier is less sensitive to outliers (or noisy data) than the linear multivariate method. Another advantage of the neural network approach is that it is easily adapted to data recorded by new stations. For example, we find that we achieve 75-80% identification accuracy for a new station without system retraining (e.g., using a network derived from data from a different station). The data required for retraining can be accumulated in about two weeks of continuous operation of the new station, and training takes less than one hour on a Sun4 Sparc station. After this retraining, the identification accuracy increases to 90%. We have recently added context (e.g., the number of arrivals before and after the arrival under consideration) to the input of the neural network, and we have found that this further improves the identification accuracy by 3-5%. This neural network approach performs better than competing technologies for automated initial phase identification, and it is amenable to machine-learning techniques to automate the process of acquiring new knowledge.

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  • Number of Files: 16
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  • Added Date: 2018-03-07 00:00:55
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