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Future of earthquake early warning : quantifying uncertainty and making fast automated decisions for applications

Citation

Wu, Stephen (2014) Future of earthquake early warning : quantifying uncertainty and making fast automated decisions for applications. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:05192014-145205444

Abstract

Earthquake early warning (EEW) systems have been rapidly developing over the past decade. Japan Meteorological Agency (JMA) has an EEW system that was operating during the 2011 M9 Tohoku earthquake in Japan, and this increased the awareness of EEW systems around the world. While longer-time earthquake prediction still faces many challenges to be practical, the availability of shorter-time EEW opens up a new door for earthquake loss mitigation. After an earthquake fault begins rupturing, an EEW system utilizes the first few seconds of recorded seismic waveform data to quickly predict the hypocenter location, magnitude, origin time and the expected shaking intensity level around the region. This early warning information is broadcast to different sites before the strong shaking arrives. The warning lead time of such a system is short, typically a few seconds to a minute or so, and the information is uncertain. These factors limit human intervention to activate mitigation actions and this must be addressed for engineering applications of EEW. This study applies a Bayesian probabilistic approach along with machine learning techniques and decision theories from economics to improve different aspects of EEW operation, including extending it to engineering applications.

Existing EEW systems are often based on a deterministic approach. Often, they assume that only a single event occurs within a short period of time, which led to many false alarms after the Tohoku earthquake in Japan. This study develops a probability-based EEW algorithm based on an existing deterministic model to extend the EEW system to the case of concurrent events, which are often observed during the aftershock sequence after a large earthquake.

To overcome the challenge of uncertain information and short lead time of EEW, this study also develops an earthquake probability-based automated decision-making (ePAD) framework to make robust decision for EEW mitigation applications. A cost-benefit model that can capture the uncertainties in EEW information and the decision process is used. This approach is called the Performance-Based Earthquake Early Warning, which is based on the PEER Performance-Based Earthquake Engineering method. Use of surrogate models is suggested to improve computational efficiency. Also, new models are proposed to add the influence of lead time into the cost-benefit analysis. For example, a value of information model is used to quantify the potential value of delaying the activation of a mitigation action for a possible reduction of the uncertainty of EEW information in the next update. Two practical examples, evacuation alert and elevator control, are studied to illustrate the ePAD framework. Potential advanced EEW applications, such as the case of multiple-action decisions and the synergy of EEW and structural health monitoring systems, are also discussed.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Earthquake Early Warning; Bayesian probability; decision theory; machine learning; Importance Sampling; Relevance vector machine; Performance-Based Earthquake Engineering
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Civil Engineering
Minor Option:Geophysics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Beck, James L.
Thesis Committee:
  • Heaton, Thomas H. (chair)
  • Beck, James L.
  • Cochran , Elizabeth S.
  • Gillen, Benjamin J.
Defense Date:12 May 2014
Funders:
Funding AgencyGrant Number
George Housner FellowshipUNSPECIFIED
U.S. Geological SurveyUNSPECIFIED
Gordon and Betty Moore FoundationUNSPECIFIED
Record Number:CaltechTHESIS:05192014-145205444
Persistent URL:http://resolver.caltech.edu/CaltechTHESIS:05192014-145205444
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:8243
Collection:CaltechTHESIS
Deposited By: Stephen Wu
Deposited On:21 May 2014 16:40
Last Modified:20 Apr 2017 18:51

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