Optimizing Earthquake Early Warning Alert Distance Strategies Using the July 2019 Mw 6.4 and Mw 7.1 Ridgecrest, California, Earthquakes

Abstract

<jats:title>ABSTRACT</jats:title> <jats:p>The ShakeAlert earthquake early warning system aims to alert people who experience modified Mercalli intensity (MMI) IV+ shaking during an earthquake using source estimates (magnitude and location) to estimate median-expected peak ground motions with distance, then using these ground motions to determine median-expected MMI and thus the extent of MMI IV shaking. Because median ground motions are used, even if magnitude and location are correct, there will be people outside the alert region who experience MMI IV shaking but do not receive an alert (missed alerts). We use 91,000 “Did You Feel It?” survey responses to the July 2019 Mw 6.4 and Mw 7.1 Ridgecrest, California, earthquakes to determine which ground-motion to intensity conversion equation (GMICE) best fits median MMI with distance. We then explore how incorporating uncertainty from the ground-motion prediction equation and the GMICE in the alert distance calculation can produce more accurate MMI IV alert regions for a desired alerting strategy (e.g., aiming to alert 95% of people who experience MMI IV+ shaking), assuming accurate source characterization. Without incorporating ground-motion uncertainties, we find MMI IV alert regions using median-expected ground motions alert fewer than 20% of the population that experiences MMI IV+ shaking. In contrast, we find &amp;gt;94% of the people who experience MMI IV+ shaking can be included in the MMI IV alert region when two standard deviations of ground-motion uncertainty are included in the alert distance computation. The optimal alerting strategy depends on the false alert tolerance of the community due to the trade-off between minimizing missed and false alerts. This is especially the case for situations like the Mw 6.4 earthquake when alerting 95% of the 5 million people who experience MMI IV+ also results in alerting 14 million people who experience shaking below this level and do not need to take protective action.</jats:p>

How Low Should We Alert? Quantifying Intensity Threshold Alerting Strategies for Earthquake Early Warning in the United States

“…Similar to previous analyses (e.g., Minson et al., 2019; Saunders et al., 2020) as well as our individual earthquake examples (Figures 4–6), we find that there is a tradeoff between minimizing the number of missed alerts and minimizing the number of precautionary alerts for a given target threshold. We find that the sum total over the earthquakes in our ShakeMap catalog produces similar alert quality results in terms of area (Figure 8) with those in terms of population (Figure S1 in the Supporting Information S1).…”

“…The MMI extent that corresponds with the alert threshold becomes the radius of the alert region that is used by ShakeAlert partners to deliver warnings to the public. This MMI threshold alerting approach has established tradeoffs between missed and precautionary alerts (e.g., Meier, 2017; Minson et al., 2018, 2019; Saunders et al., 2020), and misunderstandings about the alerting strategy used have led to adjustments in the operational ShakeAlert alert thresholds (Cochran & Husker, 2019; Lin, 2019).…”

“…This creates a large range of uncertainty in the distance over which a given level of shaking intensity can occur even when the earthquake's magnitude is perfectly known. For a M 6+ earthquake, one standard deviation of ground‐motion model uncertainty translates into a 100 km uncertainty in the alert distance using an MMI 3.5 alert threshold (Saunders et al., 2020).…”

A Framework for Evaluating Earthquake Early Warning for an Infrastructure Network: An Idealized Case Study of a Northern California Rail System

“…We have ignored all system latencies including data telemetry, analysis, and alert distribution, and have implicitly assumed an infinitely dense seismic network. We have further assumed that all EEW shaking forecasts are perfect when, in reality, ground motion is highly variable and will cause source-parameter-based EEW to have many missed alerts and some false alerts even if the source parameters of the earthquake rupture are known perfectly (Minson et al, 2019;Saunders et al, 2020).…”

Contributed Reports of Widely Felt Earthquakes in California, United States: If They Felt it, Did They Report it?

“…(Atkinson and Wald, 2007;Worden et al, 2012). DYFI data have thus proved useful beyond expectation to characterize earthquake effects, for myriad reasons including scientific investigations (e.g., Hough, 2012), development of earthquake early warning (e.g., Saunders et al, 2020), and earthquake response (Earle and Wald, 2007).…”