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Matching Waveform Envelopes for Earthquake Early Warning

Citation

Roh, Becky (2021) Matching Waveform Envelopes for Earthquake Early Warning. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/hw8k-zx98. https://resolver.caltech.edu/CaltechTHESIS:11112020-213135157

Abstract

Current earthquake early warning (EEW) algorithms are continuously optimized to strive for fast, accurate source parameter estimates for the rupturing earthquake (i.e. magnitude, location), which are then used to predict ground motions expected at a site. However, they may still struggle with challenging cases, such as offshore events and complex sequences. An envelope-based two-part search algorithm is developed to handle such cases. This algorithm matches different templates to the incoming observed ground motion envelopes to find the optimal earthquake source parameter estimates.

The algorithm consists of two methods. Method I is the standard grid search, and it uses Cua-Heaton ground motion envelopes as its templates; Method II is the extended catalog search, and its templates are waveform envelopes from past real and synthetic earthquakes. The grid search is intended for robustness and provides approximate average solutions, whereas the extended catalog search matches envelopes considering the station’s specific site and path effects. In parallel execution, Methods I and II work together – either by confirming each other’s solutions or accepting the solution with stronger fits – to provide the best parameter estimates based on waveform-based data.

The main advantage of the two-part search algorithm is its ability to find parameter estimates of reduced uncertainties using the P-wave data from a single station. Many algorithms wait until multiple stations are triggered to reduce tradeoffs between the magnitude and location. This waiting time, however, is detrimental in EEW, for it jeopardizes the warning time that can be issued to nearby regions expected to experience strong shaking. The use of a single station would virtually eliminate this waiting time, maximizing the warning time without the cost in accuracy of the estimates.

Because EEW is a race against time, further actions are taken for more rapid estimation of the earthquake source parameters. A Bayesian approach using prior information has the potential to reduce uncertainties that arise in the initial time points due to tradeoffs between the magnitude and location. This essentially increases the confidence of the initial parameter estimates, allowing alerts to be issued faster. A KD tree nearest neighbor search is also introduced to reduce latency in the time it takes to find the best-fitting solutions. In comparison to an exhaustive, brute-force search, it cuts the searching time by only examining through a fraction of the total database.

An envelope-based algorithm examines the shape and relative frequency content and makes appropriate judgments, just as a human seismologist would; it also addresses the issue of data transmission latencies. Overall, this algorithm is able to interpret the complexity of earthquakes and assess the features they hold to ultimately communicate information of significant ground shaking to different regions.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Earthquake Early Warning
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):
  • Heaton, Thomas H.
Thesis Committee:
  • Asimaki, Domniki (chair)
  • Heaton, Thomas H.
  • Hall, John F.
  • Ross, Zachary E.
  • Allen, Richard
  • Minson, Sarah
Defense Date:29 September 2020
Non-Caltech Author Email:beckyheeroh (AT) gmail.com
Record Number:CaltechTHESIS:11112020-213135157
Persistent URL:https://resolver.caltech.edu/CaltechTHESIS:11112020-213135157
DOI:10.7907/hw8k-zx98
ORCID:
AuthorORCID
Roh, Becky0000-0002-3905-0086
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:13998
Collection:CaltechTHESIS
Deposited By: Becky Roh
Deposited On:12 Nov 2020 18:20
Last Modified:18 Dec 2020 00:20

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