Published February 2026 | Version Published
Journal Article Open

Refinements to the Attenuated Propagation of Local Earthquake Shaking (APPLES) ground-motion-based earthquake early warning algorithm

  • 1. ROR icon California Institute of Technology
  • 2. ROR icon United States Geological Survey

Abstract

We refined the Attenuated ProPagation of Local Earthquake Shaking (APPLES) ground-motion-based earthquake early warning (EEW) approach, and directly compare APPLES performance with that of the source-characterization-based U.S. ShakeAlert EEW system for a suite of historical earthquakes in the U.S. West Coast and Japan. APPLES is an extension of the Propagation of Local Undamped Motion (PLUM) algorithm in which observed shaking intensity at seismic stations is used to forward-predict intensity distributions to surrounding areas using an attenuation model derived from an intensity prediction equation. We test new configuration options within APPLES, such as using the second highest estimated ground motion rather than the maximum, to better match median ground-motion observations and reduce alerts for small magnitude earthquakes, both of which are key alerting priorities within ShakeAlert. We evaluate these configurations alongside ShakeAlert by comparing the ground-motion estimation accuracy and available warning times relative to station observations and ShakeMap distributions. Our preferred APPLES configuration produces accurate ground-motion estimates and corresponds better with median observations compared to ShakeAlert's estimates. This preferred configuration substantially reduces alert issuance for M < 5.0 earthquakes compared to the previous APPLES configuration, and alert-release criteria can further restrict alerts to primarily M ≥ 5.5 earthquakes without requiring magnitude estimation. Prioritizing matching median-observed ground motions may reduce APPLES warning times compared to configurations that were tuned to avoid missed alerts (such as those that use the maximum estimated ground motions), which can lead to shorter warning times compared to ShakeAlert for the same alert threshold. However, station-based warning time assessments demonstrate that APPLES can outperform ShakeAlert for high target thresholds. APPLES is a simple, independent EEW approach that may improve the robustness of EEW for the West Coast of the U.S.

Copyright and License

© The Author(s) 2025. Published by Oxford University Press on behalf of The Royal Astronomical Society 2025.
This work is written by (a) US Government employee(s) and is in the public domain in the US.

Acknowledgement

This research was supported by the USGS Earthquake Hazards Program through the ShakeAlert Project with Cooperative Agreements G22AC00416 and G24AC00477. We thank the Editors, Annemarie Baltay, two anonymous reviewers, and members of the ShakeAlert community for helpful comments and discussions that improved this work. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

Data Availability

Earthquake source information and ShakeMaps were obtained from the Advanced National Seismic System (ANSS) Comprehensive Catalogue (ComCat; U.S. Geological Survey [USGS] 2017earthquake.usgs.gov, last accessed July 2025). The population distribution used in our analysis is the LandScan Global data set with 30 arcsec resolution (Lebakula et al2024). Seismic time‐series data for the simulated real‐time tests for the original ShakeAlert testing platform test suite (Cochran et al2018) are available at the Southern California Earthquake Data Center (SCEDC) at https://scedc.caltech.edu/data/eewtesting.html (last accessed July 2025; SCEDC 2013). All analysis and figures were created using Python (https://www.python.org; last accessed July 2025). The Supplementary Materials contains additional figures pertaining to our analysis and results.

Supplemental Material

Supplementary data (PDF)

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Additional details

Funding

United States Geological Survey
G22AC00416
United States Geological Survey
G24AC00477

Dates

Submitted
2025-07-22
Accepted
2025-10-15
Available
2025-10-21
Published online
Available
2025-12-17
Corrected and typeset

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