Real-Time Earthquake Detection and Alerting Behavior of PLUM Ground-Motion-Based Early Warning in the United States

Abstract

We examine the real-time earthquake detection and alerting behavior of the Propagation of Local Undamped Motion (PLUM) earthquake early warning (EEW) algorithm and compare PLUM’s performance with the real-time performance of the current source-characterization-based ShakeAlert System. In the United States (U.S.), PLUM uses a two-station approach to detect earthquakes. Once a detection is confirmed, observed modified Mercalli intensity (MMI) distributions are forecast onto a regular grid, in which the preferred alert regions are grid cells with MMI 4.0+ forecasts. Although locations of dense station coverage allow PLUM to detect small (M < 4.5) earthquakes typically not considered for EEW in the U.S., a PLUM detection on a small earthquake does not always generate an alert. This is because PLUM alerts are determined by current shaking distributions. If the MMI 4.0+ shaking subsides prior to detection confirmation by shaking at a second neighboring station, the prior MMI 4.0+ information will not be in the alert forecasts. Of the 432 M 3.0+ U.S. West Coast earthquakes in 2021, 33 produced ground motions large enough to be detected by PLUM. Twenty-four generated MMI 4.0+ PLUM alerts, whereas ShakeAlert issued public EEW alerts for 13 of these earthquakes. We compare PLUM and ShakeAlert alert regions with ShakeMap and “Did You Feel It?” intensity distributions. Because PLUM alert regions surround stations observed to have strong ground motions (regardless of earthquake magnitude), PLUM alerts reliably include locations that experience significant shaking. This is not necessarily the case for ShakeAlert alert regions when there are large errors in magnitude or epicenter estimates. For two of the largest earthquakes in our real-time dataset, the M 6.0 Antelope Valley and M 5.1 Petrolia earthquakes, the inclusion of PLUM would have improved real-time ShakeAlert performance. Our results indicate that incorporation of PLUM into ShakeAlert will improve the robustness of the EEW system.

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

“…The MMI predictions and alert messages are updated each second using the current MMI observations at the update time, until the stations associated with the event have not observed MMI 3.0+ for 10 s; at this time the event is declared over. A cost‐reduction analysis of PLUM with this alert grid configuration found a 60 km prediction radius to be beneficial for the widest range of EEW performance needs (Cochran et al., 2022), and was adopted in the internal real‐time testing of PLUM starting in July 2021 (Saunders, Minson, Baltay, et al., 2022).…”

“…When PLUM was tested for ShakeAlert, PLUM accurately detected earthquakes with few to no false event detections (Cochran et al., 2019; Kilb et al., 2021). PLUM alert regions also consistently covered areas of significant shaking, which was not always achieved by source‐based algorithms because of location and magnitude errors (Minson et al., 2020; Saunders, Minson, Baltay, et al., 2022).…”

“…These tests also revealed some potential limitations of the current PLUM implementation for the ShakeAlert context specifically. First, during smaller magnitude earthquakes, PLUM can over‐alert to areas that experience likely‐not‐felt shaking (Saunders, Minson, Baltay, et al., 2022). While these smaller earthquakes are within the magnitude range for issuing public alerts in ShakeAlert (Kohler et al., 2020) and produce significant shaking intensities of interest for EEW (Chaffeur et al., 2022; Minson, Baltay, et al., 2021; Minson et al., 2022), the spatial footprint of this shaking can be much smaller than the PLUM prediction radius.…”

“…Perhaps one of the simplest approaches is to use current ground‐motion observations to predict that nearby locations within a given prediction radius of an observation will also be shaken. The Propagation of Local Undamped Motion (PLUM) is one such approach that has been implemented in Japan (Kodera et al., 2018, 2020, 2021) and is being tested for use in the United States (Cochran et al., 2019, 2022; Kilb et al., 2021; Minson et al., 2020; Saunders, Minson, Baltay, et al., 2022).…”