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Expected Contribution Metrics for Earthquake Early Warning Network Telemetry

Description: 

Earthquake early warning (EEW) requires reliable, low-latency data delivery.Limits on funding require engineers and network sponsors to consider which investments in telemetry and stations are most effective in terms of mission and cost.In previous work we presented a method to estimate annualized contributions of individual stations based on return times of strong shaking at the station drawn from hazard curves developed by the USGS National Seismic Hazard Mapping Project (NSHMP). Return times (years) can be interpreted as the reciprocal of the expected participation rate (1/yr) at a given ground motion, i.e., how often the station is expected to experience that ground motion amplitude.For the analysis we use peak ground acceleration (PGA). Hazard curves are available on a 0.05x0.05 degree grid for the western U.S. For an added station, the station-expected contribution is the product of the annual rate, the area improved by the station, and the alert-time reduction because of the new station, with physically meaningful units of km^2-s/yr. While simple to calculate for individual stations, this approach cannot be directly applied to telemetry because a telemetry node can be shared among stations with different seismic hazard rates. To generalize the expected contribution for a telemetry change, we discretize the area affected by the change, and calculate the return time for each discrete cell. The result is an improved and consistent estimate that applies both to individual stations and to collections gathered to a telemetry component or system. We illustrate the approach with stations and telemetry hubs in the Southern California Seismic Network (SCSN) that contribute to the ShakeAlert EEW system. Results provide relative ranking among components and systems, which can be used to guide investments in system hardening and redundancy.

Session: Earthquake Early Warning Optimization and Efficacy [Poster]

Type: Poster

Date: 4/20/2023

Presentation Time: 08:00 AM (local time)

Presenting Author: Glenn Biasi

Student Presenter: No

Invited Presentation: 

Authors