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Title: Earthquake early warning system using real-time signal processing

Abstract

An earthquake warning system has been developed to provide a time series profile from which vital parameters such as the time until strong shaking begins, the intensity of the shaking, and the duration of the shaking, can be derived. Interaction of different types of ground motion and changes in the elastic properties of geological media throughout the propagation path result in a highly nonlinear function. We use neural networks to model these nonlinearities and develop learning techniques for the analysis of temporal precursors occurring in the emerging earthquake seismic signal. The warning system is designed to analyze the first-arrival from the three components of an earthquake signal and instantaneously provide a profile of impending ground motion, in as little as 0.3 sec after first ground motion is felt at the sensors. For each new data sample, at a rate of 25 samples per second, the complete profile of the earthquake is updated. The profile consists of a magnitude-related estimate as well as an estimate of the envelope of the complete earthquake signal. The envelope provides estimates of damage parameters, such as time until peak ground acceleration (PGA) and duration. The neural network based system is trained using seismogram data frommore » more than 400 earthquakes recorded in southern California. The system has been implemented in hardware using silicon accelerometers and a standard microprocessor. The proposed warning units can be used for site-specific applications, distributed networks, or to enhance existing distributed networks. By producing accurate, and informative warnings, the system has the potential to significantly minimize the hazards of catastrophic ground motion. Detailed system design and performance issues, including error measurement in a simple warning scenario are discussed in detail.« less

Authors:
;
Publication Date:
Research Org.:
Lawrence Livermore National Lab., CA (United States)
Sponsoring Org.:
USDOE, Washington, DC (United States)
OSTI Identifier:
267474
Report Number(s):
UCRL-JC-123270; CONF-9609132-1
ON: DE96010831; TRN: 96:004416
DOE Contract Number:  
W-7405-ENG-48
Resource Type:
Conference
Resource Relation:
Conference: 1996. Institute of Electrical and Electronics Engineers (IEEE) workshop on neural networks for signal processing, Keihanna (Japan), 4-6 Sep 1996; Other Information: PBD: Feb 1996
Country of Publication:
United States
Language:
English
Subject:
05 NUCLEAR FUELS; 44 INSTRUMENTATION, INCLUDING NUCLEAR AND PARTICLE DETECTORS; EARTHQUAKES; DETECTION; GROUND MOTION; MEASURING INSTRUMENTS; DESIGN; REAL TIME SYSTEMS; NUCLEAR FACILITIES; SAFETY; ACCELEROMETERS; NEURAL NETWORKS; WAVE PROPAGATION; SEISMOGRAPHS; ERRORS; MONITORS

Citation Formats

Leach, R.R. Jr., and Dowla, F.U. Earthquake early warning system using real-time signal processing. United States: N. p., 1996. Web.
Leach, R.R. Jr., & Dowla, F.U. Earthquake early warning system using real-time signal processing. United States.
Leach, R.R. Jr., and Dowla, F.U. Thu . "Earthquake early warning system using real-time signal processing". United States. https://www.osti.gov/servlets/purl/267474.
@article{osti_267474,
title = {Earthquake early warning system using real-time signal processing},
author = {Leach, R.R. Jr. and Dowla, F.U.},
abstractNote = {An earthquake warning system has been developed to provide a time series profile from which vital parameters such as the time until strong shaking begins, the intensity of the shaking, and the duration of the shaking, can be derived. Interaction of different types of ground motion and changes in the elastic properties of geological media throughout the propagation path result in a highly nonlinear function. We use neural networks to model these nonlinearities and develop learning techniques for the analysis of temporal precursors occurring in the emerging earthquake seismic signal. The warning system is designed to analyze the first-arrival from the three components of an earthquake signal and instantaneously provide a profile of impending ground motion, in as little as 0.3 sec after first ground motion is felt at the sensors. For each new data sample, at a rate of 25 samples per second, the complete profile of the earthquake is updated. The profile consists of a magnitude-related estimate as well as an estimate of the envelope of the complete earthquake signal. The envelope provides estimates of damage parameters, such as time until peak ground acceleration (PGA) and duration. The neural network based system is trained using seismogram data from more than 400 earthquakes recorded in southern California. The system has been implemented in hardware using silicon accelerometers and a standard microprocessor. The proposed warning units can be used for site-specific applications, distributed networks, or to enhance existing distributed networks. By producing accurate, and informative warnings, the system has the potential to significantly minimize the hazards of catastrophic ground motion. Detailed system design and performance issues, including error measurement in a simple warning scenario are discussed in detail.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1996},
month = {2}
}

Conference:
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