Incorporating Intensity Distance Attenuation Into PLUM Ground-Motion-Based Earthquake Early Warning in the United States: The APPLES Configuration

被引:0
|
作者
Saunders, Jessie K. [1 ]
Cochran, Elizabeth S. [2 ]
Bunn, Julian J. [1 ]
Baltay, Annemarie S. [3 ]
Minson, Sarah E. [3 ]
O'Rourke, Colin T. [4 ]
机构
[1] CALTECH, Pasadena, CA 91125 USA
[2] US Geol Survey, Earthquake Sci Ctr, Pasadena, CA USA
[3] US Geol Survey, Earthquake Sci Ctr, Moffett Field, CA USA
[4] US Geol Survey, Earthquake Sci Ctr, Seattle, WA USA
关键词
earthquake early warning; warning systems; alert performance; PLUM; ShakeAlert system; ground motions; shaking intensity; West Coast USA; Ferndale; SEISMIC NETWORK; PERFORMANCE; RIDGECREST; CALIFORNIA; ALGORITHM;
D O I
10.1029/2023EF004126
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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. Earthquake early warning systems aim to provide a few seconds notice of incoming shaking from an earthquake before shaking arrives at the alerted location. There are many ways to go about creating early warning alerts. The approach we use, called PLUM, calculates alert regions directly from station observations, where the level of shaking estimated for a location is simply the maximum-observed shaking at stations within a specified distance of that location. The value of this specified distance causes trade-offs between the accuracy of the estimated shaking and the amount of warning time that PLUM can provide. In this work, we modify the PLUM approach to vary its shaking estimates based on where the location is compared to the station: locations near stations have shaking estimates that are similar to the station observations, and locations that are farther away have lower shaking estimates than the station observations. We find this new approach improves the accuracy of estimated shaking while maintaining (and sometimes increasing) warning times compared to PLUM. This new approach also produces shaking estimates that are similar to those produced by the current United States earthquake early warning system, which will make it easier to combine them in the future. We added intensity attenuation with distance into the Propagation of Local Undamped Motion (PLUM) algorithm's prediction scheme (which we call Attenuated ProPagation of Local Earthquake Shaking (APPLES)) APPLES reduces over-alerting for smaller-magnitude events and can increase warning times in some areas Optimized alerting strategies with APPLES show comparable performance to PLUM during large-magnitude events
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页数:27
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