Discussion and Conclusion
In this study, we evaluated the viability of earthquake fingerprint searching methods for EEW, using database structure to reduce searching time for large databases. Specifically, we evaluated the GbA as an example of the EEW fingerprint search algorithm. We found that database size is a critical factor in providing reliable predictions of ground motion (PGA, PGV, PGD) and source parameters (magnitude and hypocenter distance) for EEW. We also present the KD tree approach to reduce the searching time, so that large database searching is feasible for real-time implementations in EEW. By empirical validation, we demonstrated that the searching time using KD tree can be approximately 85% less than the exhaustive approach for the GbA EEW earthquake database. (Strauss et al, 2017) has studied extensively on the cost-benefit effects of a warning system in the United States; the study has shown that the number of injuries from earthquakes can be reduced by more than 50% if EEW can provide timely and accurate alerts. One of the potential applications of the database searching method is to directly estimate peak ground motions from the observed ground motions for any given site in real-time seismology application such as EEW; it avoids the multi-step modeling errors that could be accumulated through source parameter estimation and the ground motion attenuation relationship, since the final errors can lead to significant uncertainties in the final shaking information. Ideally, the goal of EEW is to serve as an alarm for severe ground shaking in real-time rather than source characterization. The fingerprint searching methodology could also be extended to tackle other challenges in EEW, such as event detection (i.e. earthquake/noise discrimination).
