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Real-Time Bayesian Analysis of Ground Motion Envelopes for Earthquake Early Warning


Karakus, Gokcan (2016) Real-Time Bayesian Analysis of Ground Motion Envelopes for Earthquake Early Warning. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/Z9PN93JS.


Current earthquake early warning systems usually make magnitude and location predictions and send out a warning to the users based on those predictions. We describe an algorithm that assesses the validity of the predictions in real-time. Our algorithm monitors the envelopes of horizontal and vertical acceleration, velocity, and displacement. We compare the observed envelopes with the ones predicted by Cua & Heaton's envelope ground motion prediction equations (Cua 2005). We define a "test function" as the logarithm of the ratio between observed and predicted envelopes at every second in real-time. Once the envelopes deviate beyond an acceptable threshold, we declare a misfit. Kurtosis and skewness of a time evolving test function are used to rapidly identify a misfit. Real-time kurtosis and skewness calculations are also inputs to both probabilistic (Logistic Regression and Bayesian Logistic Regression) and nonprobabilistic (Least Squares and Linear Discriminant Analysis) models that ultimately decide if there is an unacceptable level of misfit. This algorithm is designed to work at a wide range of amplitude scales. When tested with synthetic and actual seismic signals from past events, it works for both small and large events.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Real-time; Bayesian; Earthquake Early Warning; Reality Check; Algorithm; California Earthquake Early Warning System; ShakeAlert
Degree Grantor:California Institute of Technology
Division:Engineering and Applied Science
Major Option:Civil Engineering
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Heaton, Thomas H.
Thesis Committee:
  • Beck, James L. (chair)
  • Heaton, Thomas H.
  • Ampuero, Jean-Paul
  • Kanamori, Hiroo
  • Asimaki, Domniki
Defense Date:21 January 2016
Additional Information:Supplementary data is included as a zipped file. The files contain some of the data the author collected during his PhD at Caltech. The data sets are mainly divided into three categories: acceleration, velocity and displacement.
Record Number:CaltechTHESIS:02242016-172347324
Persistent URL:
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:9584
Deposited By: Gokcan Karakus
Deposited On:01 Mar 2016 22:59
Last Modified:28 Oct 2021 23:09

Thesis Files

PDF - Final Version
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[img] Archive (ZIP) (Datasets for acceleration, velocity and displacement) - Supplemental Material
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