Accession Number:

ADA275058

Title:

Initial Wave-Type Identification with Neural Networks and its Contribution to Automated Processing in IMS Version 3.0

Descriptive Note:

Technical rept. Nov 1992-Dec 1993

Corporate Author:

SCIENCE APPLICATIONS INTERNATIONAL CORP SAN DIEGO CA

Report Date:

1993-12-10

Pagination or Media Count:

48.0

Abstract:

This report describes a new 4-class neural network for automated identification of initial wave type Teleseism, Regional P, Regional S, or Noise for data recorded by 3-component stations or arrays. This is an extension of the 2-class P or S neural network that we developed for 3-component stations Patnaik and Sereno, 1991. The input data are dominant period, polarization attributes, contextual information e.g., measurements related to a group of arrivals, a spectral representation of the horizontal-to-vertical power ratio, and the slowness determined by f-k analysis for array stations. We used a three-staged approach, and each stage consists of a 2-class neural network. The first stage separates signal from noise. The signals are passed to the second stage which separates regional S phases from regional P phases and teleseisms. The regional P phases and teleseisms are passed to the final stage which separates them into two distinct classes. A three-layer backpropagation neural network is used at each stage. Neural networks were trained for six 3- component IRISIDA stations in the CIS, and a 4-element micro-array in Kislovodsk. The identification accuracy of the neural networks is 90 for most of the stations that we tested. The neural network module was integrated into the Intelligent Monitoring System IMS, and it was applied to the 3-component IRISIDA data under simulated operational conditions. The result was a reduction in the number false-alarms produced by the automated processing and interpretation system by about 60

Subject Categories:

  • Seismology

Distribution Statement:

APPROVED FOR PUBLIC RELEASE