Application of neural networks for seismic phase identification

Author

Jang, Gyu-Sang ; Dowla, Farid ; Vemuri, V.

Author_Institution

Dept. of Electr. Eng., California Univ., Davis, CA, USA

fYear

1991

fDate

18-21 Nov 1991

Firstpage

899

Abstract

The effectiveness of a multilayered feedforward neural network for seismic phase identification was investigated. The database consisted of seismograms from 75 earthquakes and 75 underground nuclear explosions. For learning, the conjugate gradient error backpropagation algorithm with a weight-elimination method was used. Results indicate that feedforward neural networks appear to outperform a conventional Bayesian classifier in a problem where the task was restricted to identifying only two of the principal regional phases, Pg and Lg, on earthquake and explosion seismograms of the western United States

Keywords

earthquakes; geophysical techniques; geophysics computing; neural nets; nuclear explosions; seismology; Lg waves; Pg waves; United States; conjugate gradient error backpropagation algorithm; earthquakes; multilayered feedforward network; neural networks; principal regional phases; seismic phase identification; underground nuclear explosions; weight-elimination method; Background noise; Earthquakes; Event detection; Explosions; Feedforward neural networks; Laboratories; Monitoring; Multi-layer neural network; Neural networks; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1991. 1991 IEEE International Joint Conference on

Print_ISBN

0-7803-0227-3

Type

conf

DOI

10.1109/IJCNN.1991.170514

Filename

170514