Mechanical and Civil Engineering Seminar
PhD. Thesis Defense
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 using the P-wave data from a single station for M < 6.5 events. This is valuable for regions with sparse station coverage, such as offshore events. 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 regions near the epicenter, creating larger blind zones. The use of a single station would reduce or even virtually eliminate this waiting time, maximizing the warning time without the cost in accuracy of the estimates. Another advantage of the algorithm, particularly of Method II, is the ability to capture large (M > 7) complex sequences (i.e. multiple sources rupturing closely in time and in space). By doing so, regions would be alerted to expect further strong shaking even after the initial rupture.
Because EEW is a race against time, further actions are taken for more confidence in the 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 both shape and relative frequency content and makes appropriate judgments, just as a human seismologist would. The use of envelopes also addresses the issue of data transmission latencies. Overall, this algorithm is able to identify offshore events and earthquakes in sparse station coverage by using the P-wave from 1-2 stations. It is also able to interpret the complexity of earthquakes and assess the features they hold to ultimately communicate information of significant ground shaking to different regions.