Incorporating Intensity Distance Attenuation Into PLUM Ground‐Motion‐Based Earthquake Early Warning in the United States: The APPLES Configuration
Abstract
We develop Attenuated ProPagation of Local Earthquake Shaking (APPLES), a new configuration for the United States West Coast version of the Propagation of Local Undamped Motion (PLUM) earthquake early warning (EEW) algorithm that incorporates attenuation into its ground-motion prediction procedures. Under APPLES, instead of using a fixed radius to forward-predict observed peak ground shaking to the area surrounding a seismic station, the forward-predicted intensity at a location depends on the distance from the station using an intensity prediction relationship. We conduct conceptual tests of maximum intensity distribution predictions in APPLES and PLUM using a catalog of ShakeMaps to confirm that the attenuation relationship in APPLES is appropriately modeling shaking distributions for West Coast earthquakes. Then, we run APPLES and PLUM in simulated real-time tests to determine warning time performance. Finally, we compare real-time alert behavior during the 2022 M6.4 Ferndale, California, earthquake and other recent events. We find that APPLES presents two potential improvements to PLUM by reducing over-alerting during smaller magnitude earthquakes and by increasing warning times in some locations during larger earthquakes. APPLES can produce missed and late alerts in locations that experience shaking intensities close to the level used to issue alerts, so preferred alerting strategies with APPLES would use alert thresholds that are lower than the intensities targeted for EEW alerts. We find alerts using APPLES are also similar to those for the source-based approaches currently used in the ShakeAlert EEW system, which will make APPLES easier to integrate into the system.
Copyright and License
© 2024 The Authors. Earth's Future published by Wiley Periodicals LLC on behalf of American Geophysical Union. This is an open access article under thet erms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Acknowledgement
We thank Jessica Murray and two anonymous reviewers for providing thoughtful feedback that improved this manuscript. This work also benefited from discussions with other members of the ShakeAlert Project, including Timothy Clements, Allen Husker, and Clara Yoon. This research was supported by the U.S. Geological Survey Earthquake Hazards Program through the ShakeAlert Project, under Cooperative Agreement No. G21AC10561. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Contributions
Conceptualization: Jessie K. Saunders, Elizabeth S. Cochran, Julian J. Bunn, Annemarie S. Baltay, Sarah E. Minson, Colin T. O’Rourke
Data curation: Jessie K. Saunders, Julian J. Bunn
Formal analysis: Jessie K. Saunders, Elizabeth S. Cochran
Methodology: Jessie K. Saunders, Elizabeth S. Cochran, Julian J. Bunn, Annemarie S. Baltay, Sarah E. Minson, Colin T. O’Rourke
Data Availability
Earthquake source information, ShakeMaps, DYFI data, and ShakeAlert performance information were obtained from the U.S. Geological Survey earthquake event pages (USGS, 2017, last accessed January 2024). The ShakeMaps selected for our analysis use the most recent version of the ShakeMap software available for each earthquake, and primarily use ShakeMaps from the ShakeMap Atlas (Marano et al., 2023). The population data used in our analysis are the 2020 estimate from the Center for Internal Earth Science Information Network - CIESIN - Columbia University (2018). Information about the ShakeAlert alerts issued through the Wireless Emergency Alert system can be found at https://warn.pbs.org (last accessed January 2024). Seismic time-series data for the simulated real-time tests for the original ShakeAlert testing platform test suite (Cochran, Kohler, et al., 2018) are available at the Southern California Earthquake Data Center (SCEDC) at https://scedc.caltech.edu/data/eewtesting.html (last accessed July 2023; SCEDC, 2013). Data from the following seismic networks were used in this study: Anza regional network (AZ; Vernon, 1982), Red Sísmica del Noroeste de México (BC; Centro de Investigación Científica y de Educación Superior de Ensenada [CISESE], 1980), Berkeley Digital Seismograph Network (BDSN; BK; BDSN, 2014), Cascade Chain Volcano Monitoring (CC; Cascades Volcano Observatory/USGS, 2001), California Strong Motion Instrumentation Program (CE; California Geological Survey, 1972), Southern California Seismic Network (CI; California Institute of Technology and USGS Pasadena, 1926), Canadian National Seismograph Network (CN; Natural Resources Canada, 1975), Global Seismograph Network—IRIS/USGS (IU; Alburquerque Seismological Laboratory (ASL)/USGS, 2014), USGS Northern California Network (NC; USGS Menlo Park, 1966), Nevada Seismic Network (NN; University of Nevada, Reno, 1971), U.S. National Strong-Motion Network (NP; USGS, 1931), NEPTUNE Canada (NV; Ocean Networks Canada, 2009), Ocean Observatories Initiative (OO; Rutgers University, 2013), University of Oregon and Pacific Northwest Seismic Network (UO; University of Oregon, 1990), U.S. National Seismic Network (US; ASL/USGS, 1990), Pacific Northwest Seismic Network (UW; University of Washington, 1963). The U.S. West Coast version of the PLUM algorithm is stored in the code.usgs.gov repository following the protocol for the ShakeAlert EEW algorithms. All analysis and figures were created using Python (https://python.org). The Supporting Information S1 contains additional figures and tables pertaining to our analysis and results.
Conflict of Interest
The authors declare no conflicts of interest relevant to this study.
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Additional details
- ISSN
- 2328-4277
- United States Geological Survey
- ShakeAlert G21AC10561
- Caltech groups
- Seismological Laboratory