Abstract
Earthquake early warning systems (EEWS) aim to warn end-users of impending ground shaking. They can be most impactful in large earthquakes occurring close to large population centers, where exposure to strong ground shaking is extensive. However, such earthquakes are rare, and EEWS performance expectations remain uncertain. The February 2023 Kahramanmaraş, Türkiye sequence, including the M7.8 Pazarcık and M7.5 Elbistan events, exposed millions to strong ground shaking and produced a rich waveform dataset, offering a test case. We use this data to produce a realistic simulation of warning times. We use the EPIC point source algorithm for real-time earthquake characterization, and incorporate alert delivery latency using a statistical model driven by real-world alert delivery data from California, collected by the MyShake smartphone app. We show EPIC would produce solutions very quickly (4 s for Pazarcık, 10 s for Elbistan). Despite EPIC's expected magnitude underestimation (peak M6.7 for Pazarcık, M7.2 for Elbistan), we show that its magnitude estimate grows large quickly enough to provide areas of MMI 6+ shaking with up to 20 s of warning time, even with alert delivery latencies included, provided that low alerting thresholds of MMI 3 or 4 are used.