Phd records
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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenWed, 31 Jan 2024 19:16:35 +0000Real-Time Bayesian Analysis of Ground Motion Envelopes for Earthquake Early Warning
https://resolver.caltech.edu/CaltechTHESIS:02242016-172347324
Authors: {'items': [{'email': 'gokcankarakus@gmail.com', 'id': 'Karakus-Gokcan', 'name': {'family': 'Karakus', 'given': 'Gokcan'}, 'show_email': 'NO'}]}
Year: 2016
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.https://thesis.library.caltech.edu/id/eprint/9584