Quantifying the Value of Real‐Time Geodetic Constraints for Earthquake Early Warning Using a Global Seismic and Geodetic Data Set

Abstract

Geodetic earthquake early warning (EEW) algorithms complement point-source seismic systems by estimating fault-finiteness and unsaturated moment magnitude for the largest, most damaging earthquakes. Because such earthquakes are rare, it has been difficult to demonstrate that geodetic warnings improve ground motion estimation significantly. Here, we quantify and compare timeliness and accuracy of magnitude and ground motion estimates in simulated real time from seismic and geodetic observations for a suite of globally distributed, large earthquakes. Magnitude solutions saturate for the seismic EEW algorithm (we use ElarmS) while the ElarmS-triggered Geodetic Alarm System (G-larmS) reduces the error even for its first solutions. Shaking intensity (Modified Mercalli Intensity, MMI) time series calculated for each station and each event are assessed based on MMI threshold crossings, allowing us to accurately characterize warning times per station. We classify alerts and find that MMI 4 thresholds result in true positive alerts for only 13.7% of sites exceeding MMI 4 with a median warning time of 18.9 s for ElarmS, while G-larmS issues true positive alerts for 52.3% of all sites exceeding MMI 4 with a significantly longer median warning time of 55.8 s. The geodetic EEW system reduces the number of missed alerts for a threshold of MMI 4 from 48.7% to 19.2% for all sites, but also increases the number of false positive alerts from 1.2% to 13.4% of all sites. By quantifying increased accuracy in magnitude, ground motion estimation, and alert timeliness, we demonstrate that finite-fault geodetic algorithms add significant value, including better cost savings performance, to point-source seismic EEW systems for large earthquakes.

How Often Can Earthquake Early Warning Systems Alert Sites With High‐Intensity Ground Motion?

“…The three algorithms are arguably among the most promising EEW algorithms, and they represent three different approaches to EEW. The study approach adopted here is similar to that of Ruhl et al (2019) who have evaluated and compared the seismic point-source algorithm ElarmS and the geodetic finite fault algorithm G-larmS (Grapenthin et al, 2014) on a global data set of 32 earthquakes with M ≥ 6.…”

Weak Near‐Field Behavior of a Tsunami Earthquake: Toward Real‐Time Identification for Local Warning

“…The PGA, PGV, and PGD for all strong‐motion (PGA and PGV) and GNSS recordings (PGD) of these earthquakes are in Figure , along with values from a global database of 16 large ( M 6– M 9) megathrust earthquakes recorded by both geodetic and strong motion networks (Ruhl et al, ; Ruhl et al, ). These are plotted along with the estimated values for each intensity measure from ground‐motion models (GMMs).…”

“…As such, they represent “mean” earthquake behavior, and GMM residuals show deviations of a particular event's properties from the mean (Baltay & Boatwright, ; Sahakian, Baltay, et al, , Sahakian, Melgar, et al, ). Using waveforms from the two databases of HR‐GNSS and acceleration data (Ruhl et al, , ), we find the observed values of peak ground acceleration (PGA), peak ground velocity (PGV), and peak ground displacement (PGD), for near‐field sites (<500 km away from the event). PGA and PGV are obtained from the strong motion data while PGD is from the HR‐GNSS observations.…”

“…The HR‐GNSS data analyzed in this study are from the global database of HR‐GNSS recordings from Ruhl et al (), and the strong‐motion data are from a follow up global earthquake early warning study by Ruhl et al (). Both of those data sets are freely available, and the peak intensity measures can be found in Table S2.…”

“…In particular, for the strong motion data of the Mentawai earthquake we would like to thank Agency for Meteorology, Climatology and Geophysics of Indonesia (BMKG). GNSS data are available for download from Ruhl et al (), https://zenodo.org/record/1434374), and strong‐motion data are available for download from the Ruhl et al () paper (https://zenodo.org/record/1469833). Mentawai strong‐motion records are available at Zenodo (Sahakian et al, ).…”