From Earthquake Source Parameters to Ground‐Motion Warnings near You: The ShakeAlert Earthquake Information to Ground‐Motion (eqInfo2GM) Method

We present a new near‐real‐time method for converting earthquake source parameters into ground motion (GM) at locations across the west coast of the United States. This method, called earthquake information to ground motion (eqInfo2GM), has been implemented as part of the ShakeAlert earthquake early warning (EEW) system and makes estimated GM accessible to users on the EEW timeframe of seconds, as the U.S. Geological Survey ShakeMap does at higher resolution and accuracy in the minutes following a seismic event. Whereas the higher fidelity ShakeMap comes at the cost of longer processing times, eqInfo2GM effectively provides a predicted ShakeMap before shaking arrives at many locations. We describe key design details, including ground‐motion prediction equations (GMPEs) implemented, modifications made to optimize for speed, and formats created for conveying GM severity. The GM output format determines added latency and reflects a trade‐off between speed and accuracy; for our test earthquake data set, added latency is in the 0.01–1.5 s range after earthquake source parameters have been generated. GMPE implementations are validated against predicted ShakeMaps (without observations), with almost all events showing minimal mean shaking intensity level differences, reflecting variations only in treatment of source distances and VS30 data. Comparison against ShakeMaps computed with observations (a proxy for true GM) show larger differences, demonstrating the challenges of working in the EEW timeframe, when full source characterization and peak ground motion observations are both unavailable. Although specific configurations and features of the method will evolve as the needs of the EEW user community become evident, eqInfo2GM is expected to improve the overall utility of EEW alerts by providing end users with estimates of predicted local GM hazard. Such near‐real‐time estimates will enable users to decide more accurately what action to take to reduce the impact of imminent, potentially damaging shaking