Geology topics

Colin T O’Rourke

Publications and source records attributed to Colin T O’Rourke.

5 recordsLinked to original sources

USGS2024

Incorporating intensity distance attenuation into PLUM ground-motion-based earthquake early warning in the United States: The APPLES configuration

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 M 6.4 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.

California, Oregon, Washington↗

USGS2022

Testing the ShakeAlert earthquake early warning system using synthesized earthquake sequences

We test the behavior of the United States (US) West Coast ShakeAlert earthquake early warning (EEW) system during temporally close earthquake pairs to understand current performance and limitations. We consider performance metrics based on source parameter and ground‐motion forecast accuracy, as well as on alerting timeliness. We generate ground‐motion times series for synthesized earthquake sequences from real data by combining the signals from pairs of well‐recorded earthquakes (⁠4.4≤M≤7.1⁠) using time shifts ranging from −60 to +180 s. We examine fore‐ and aftershock sequences, near‐simultaneous events in different source regions, and simulated out‐of‐network and offshore earthquakes. We find that the operational ShakeAlert algorithms Earthquake Point‐source Integrated Code (EPIC) and Finite‐Fault Rupture Detector (FinDer) and the Propagation of Local Undamped Motion (PLUM) method perform largely as expected: EPIC provides the best source location estimates and is often fastest but can underestimate magnitudes or, in extreme cases, miss large earthquakes; FinDer provides real‐time line‐source models and unsaturated magnitude estimates for large earthquakes but currently cannot process concurrent events and may mislocate offshore earthquakes; PLUM identifies pockets of strong ground motion, but can overestimate alert areas. Implications for system performance are: (1) spatially and temporally close events are difficult to identify separately; (2) challenging scenarios with foreshocks that are close in space and time can lead to missed alerts for large earthquakes; and (3) in these situations the algorithms can often estimate ground motion better than source parameters. To improve EEW, our work suggests revisiting the current algorithm weighting in ShakeAlert, to continue developments that focus on using ground‐motion data to aggregate alerts from multiple algorithms, and to investigate methods to optimally leverage algorithm ground‐motion estimates. For testing and certification of EEW performance in ShakeAlert and other EEW systems where applicable, we also suggest that 25 of our 73 scenarios become part of the baseline data set.

California↗

USGS2022

Real-time earthquake detection and alerting behavior of PLUM ground-motion-based early warning in the United States

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.

California, Oregon, Washington↗

USGS2022

Alert optimization of the PLUM earthquake early warning algorithm for the western United States

We determine an optimal alerting configuration for the propagation of local undamped motion (PLUM) earthquake early warning (EEW) algorithm for use by the U.S. ShakeAlert system covering California, Oregon, and Washington. All EEW systems should balance the primary goal of providing timely alerts for impactful or potentially damaging shaking while limiting alerts for shaking that is too low to be of concern (precautionary alerts). The PLUM EEW algorithm forward predicts observed ground motions to nearby sites within a defined radius without accounting for attenuation, avoiding the earthquake source parameter estimation step of most EEW algorithms. PLUM was originally developed in Japan where the alert regions and ground motions for which alerts are issued differ from those implemented by ShakeAlert. We compare predicted ground motions from PLUM to ShakeMap‐reported ground motions for a set of 22 U.S. West Coast earthquakes of magnitude 4.4–7.2 and evaluate available warning times. We examine a range of prediction radii (20–100 km), thresholds used to issue an alert (alert threshold), and levels of impactful or potentially damaging shaking (target threshold). We find optimal performance when the alert threshold is close to the target threshold, although higher target ground motions benefit from somewhat lower alert thresholds to ensure timely alerts. We also find that performance, measured as the cost reduction that a user can achieve, depends on the user’s tolerance for precautionary alerts. Users with a low target threshold and high tolerance for precautionary alerts achieve optimal performance when larger prediction radii (60–100 km) are used. In contrast, users with high target thresholds and low tolerance for precautionary alerts achieve better performance for smaller prediction radii (30–60 km). Therefore, setting the PLUM prediction radius to 60 km balances the needs of many users and provides warning times of up to ∼20 s.

California, Oregon, Washington↗

USGS2021

The PLUM earthquake early warning algorithm: A retrospective case study of West Coast, USA, data

The PLUM (Propagation of Local Undamped Motion) earthquake early warning (EEW) algorithm differs from typical source-based EEW algorithms as it predicts shaking directly from observed shaking without first deriving earthquake source information (e.g., magnitude and epicenter). Here, we determine optimal PLUM event detection thresholds for U.S. West Coast earthquakes using two data sets: 558 M3.5+ earthquakes (California, Oregon, Washington; 2012–2017) and the ShakeAlert test suite of historic and problematic signals (1999–2015). PLUM computes Modified Mercalli Intensity ( I MMI ) using velocity and acceleration data, leveraging co-located sensors to avoid problematic signals. An event detection is issued when the observed I MMI exceeds a given threshold(s). We find a two-station detection method using I MMI trigger thresholds of 4.0 and 3.0 for the first and second stations, respectively, is optimal for detecting M4.5+ earthquakes. PLUM detected 79 events in the 2012–2017 data set, reporting (not including telemetry or alert dissemination) detection times on par, and sometimes faster than current EEW methods (mean 8 s; median 6 s). As expected, detection times were slower for the older 1999–2015 earthquakes ( N = 21; mean 11 s; median 6 s) when station coverage was sparser. Of the 31 PLUM detected M5+ events (10 2012–2017; 21 1999–2015), theoretically 20 (∼65%) could provide timely warnings. PLUM issued no false detections and avoided issuing detections for all calibration/anomalous signals, regional and teleseismic events. We conclude PLUM can successfully identify I MMI 4+ shaking from local earthquakes and could complement and enhance EEW in the U.S.

British Columbia, California, Oregon, Washington↗