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Title: Real-time neural network earthquake profile predictor

Abstract

A neural network has been developed that uses first-arrival energy to predict the characteristics of impending earthquake seismograph signals. The propagation of ground motion energy through the earth is a highly nonlinear function. This is due to different forms of ground motion as well as to changes in the elastic properties of the media throughout the propagation path. The neural network is trained using seismogram data from earthquakes. Presented with a previously unseen earthquake, the neural network produces a profile of the complete earthquake signal using data from the first seconds of the signal. This offers a significant advance in the real-time monitoring, warning, and subsequent hazard minimization of catastrophic ground motion. 17 figs.

Inventors:
;
Issue Date:
Research Org.:
University of California
OSTI Identifier:
187057
Patent Number(s):
5,490,062
Application Number:
PAN: 8-241,060
Assignee:
Univ. of California, Oakland, CA (United States)
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Patent
Resource Relation:
Other Information: PBD: 6 Feb 1996
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; SEISMOGRAPHS; NEURAL NETWORKS; EARTHQUAKES; DATA ANALYSIS; FORECASTING; SEISMIC EFFECTS; REAL TIME SYSTEMS

Citation Formats

Leach, R R, and Dowla, F U. Real-time neural network earthquake profile predictor. United States: N. p., 1996. Web.
Leach, R R, & Dowla, F U. Real-time neural network earthquake profile predictor. United States.
Leach, R R, and Dowla, F U. Tue . "Real-time neural network earthquake profile predictor". United States.
@article{osti_187057,
title = {Real-time neural network earthquake profile predictor},
author = {Leach, R R and Dowla, F U},
abstractNote = {A neural network has been developed that uses first-arrival energy to predict the characteristics of impending earthquake seismograph signals. The propagation of ground motion energy through the earth is a highly nonlinear function. This is due to different forms of ground motion as well as to changes in the elastic properties of the media throughout the propagation path. The neural network is trained using seismogram data from earthquakes. Presented with a previously unseen earthquake, the neural network produces a profile of the complete earthquake signal using data from the first seconds of the signal. This offers a significant advance in the real-time monitoring, warning, and subsequent hazard minimization of catastrophic ground motion. 17 figs.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1996},
month = {2}
}