S22B-06
Combining Multiple Rupture Models in Real-Time for Earthquake Early Warning

Tuesday, 15 December 2015: 11:35
308 (Moscone South)
Sarah E Minson1, Stephen Wu2, James L Beck3 and Thomas H Heaton3, (1)U.S. Geological Survey, Earthquake Science Center, Menlo Park, CA, United States, (2)California Institute of Technology, Arcadia, CA, United States, (3)California Institute of Technology, Pasadena, CA, United States
Abstract:
The ShakeAlert earthquake early warning system for the west coast of the United States is designed to combine information from multiple independent earthquake analysis algorithms in order to provide the public with robust predictions of shaking intensity at each user’s location before they are affected by strong shaking. The current contributing analyses come from algorithms that determine the origin time, epicenter, and magnitude of an earthquake (On-site, ElarmS, and Virtual Seismologist). A second generation of algorithms will provide seismic line source information (FinDer), as well as geodetically-constrained slip models (BEFORES, GPSlip, G-larmS, G-FAST). These new algorithms will provide more information about the spatial extent of the earthquake rupture and thus improve the quality of the resulting shaking forecasts.


Each of the contributing algorithms exploits different features of the observed seismic and geodetic data, and thus each algorithm may perform differently for different data availability and earthquake source characteristics. Thus the ShakeAlert system requires a central mediator, called the Central Decision Module (CDM). The CDM acts to combine disparate earthquake source information into one unified shaking forecast. Here we will present a new design for the CDM that uses a Bayesian framework to combine earthquake reports from multiple analysis algorithms and compares them to observed shaking information in order to both assess the relative plausibility of each earthquake report and to create an improved unified shaking forecast complete with appropriate uncertainties. We will describe how these probabilistic shaking forecasts can be used to provide each user with a personalized decision-making tool that can help decide whether or not to take a protective action (such as opening fire house doors or stopping trains) based on that user’s distance to the earthquake, vulnerability to shaking, false alarm tolerance, and time required to act.