Predicting Earthquake Shaking in Real Time: A Comparative Study of Impact‐Based and Hybrid Early Warning Approaches

Earthquake Early Warning (EEW) systems can provide crucial seconds to tens of seconds of advanced notice before the arrival of destructive seismic waves. Their effectiveness, however, depends on how quickly and accurately each system forecasts ground motion using real-time data. This study compares the performance of two distinct EEW algorithms (PLUM, a pure impact-based approach, and a P-wave Shaking Forecast Based EEWS (QuakeUp), which is a hybrid method) by simulating the 2016 M _jma 6.6 Central Tottori earthquake. PLUM ¹ (modified by Kagawa ² ) predicts earthquake shaking based solely on observed seismic intensities. Once a station records intensity above a threshold, PLUM propagates that intensity to nearby locations, making it robust for complex ruptures but reliant on stations having already experienced strong motion. QuakeUp ³ , by contrast, integrates P-wave amplitude measurements with rapid estimates of earthquake location and magnitude; this allows it to forecast shaking before stations register significant ground motion but can introduce uncertainties until the evolving source parameters stabilize. Our offline “playback” of the Central Tottori event demonstrates that PLUM offers consistently high alert accuracy (≥ 90%) once threshold is exceeded but provides limited lead-times for sites near the epicenter. In some cases, stations up to 30 km from the source received effective warnings too late for significant protective actions. Conversely, QuakeUp could issue alerts as early as 3 s after the origin time, yielding up to 12 s of lead-time at 40 km. However, its accuracy briefly dropped to 64% before converging to 100% by around 12 s, reflecting the time required for magnitude and location estimates to mature. In addition, earthquake magnitude and location parameters are provided as a byproduct information. Despite these differences, both algorithms delivered reliable alerts across much of Tottori Prefecture. The results highlight how algorithmic design and station coverage influence warning performance, with PLUM excelling under dense station networks and QuakeUp offering broader, earlier coverage where real-time source parameters can be accurately constrained.