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Published August 2017 | Published + Supplemental Material
Journal Article Open

Combining Multiple Earthquake Models in Real Time for Earthquake Early Warning

Abstract

The ultimate goal of earthquake early warning (EEW) is to provide local shaking information to users before the strong shaking from an earthquake reaches their location. This is accomplished by operating one or more real‐time analyses that attempt to predict shaking intensity, often by estimating the earthquake's location and magnitude and then predicting the ground motion from that point source. Other EEW algorithms use finite rupture models or may directly estimate ground motion without first solving for an earthquake source. EEW performance could be improved if the information from these diverse and independent prediction models could be combined into one unified, ground‐motion prediction. In this article, we set the forecast shaking at each location as the common ground to combine all these predictions and introduce a Bayesian approach to creating better ground‐motion predictions. We also describe how this methodology could be used to build a new generation of EEW systems that provide optimal decisions customized for each user based on the user's individual false‐alarm tolerance and the time necessary for that user to react.

Additional Information

© 2017 Seismological Society of America. Manuscript received 31 October 2016; First Published on June 13, 2017. Data and resources: Acceleration waveforms were obtained from the Incorporated Research Institutions for Seismology (IRIS) Data Management Center BREQ_FAST request service at http://ds.iris.edu/ds/nodes/dmc/manuals/breq_fast/ (last accessed May 2016). Details of earthquake early warning (EEW) algorithm reports are available in the electronic supplement to this article. The authors would like to thank Annemarie Baltay and T. C. Hanks who provided internal reviews, constructive criticisms, and useful discussions. Annemarie Baltay further provided background research on ground‐motion uncertainties. Thanks are also due to Elizabeth Cochran who provided ShakeAlert performance statistics. The authors would also like to acknowledge the journal editors and reviewers for helping revise and improve the article. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Attached Files

Published - BSSA-2016331.1.pdf

Supplemental Material - 2016331_esupp_Movie_S1.mp4

Supplemental Material - 2016331_esupp_Movie_S2.mp4

Supplemental Material - 2016331_esupp_Movie_S3.mp4

Supplemental Material - 2016331_esupp_Movie_S4.mp4

Supplemental Material - 2016331_esupp_Table_S1.csv

Supplemental Material - 2016331_esupp_Table_S2.csv

Supplemental Material - 2016331_esupp_Table_S3.csv

Supplemental Material - 2016331_esupp_Table_S4.csv

Supplemental Material - 2016331_esupp_Table_S5.csv

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Additional details

Created:
August 19, 2023
Modified:
October 25, 2023