Journal Article

Near-epicenter-based partial matching crossover algorithm for estimating the strong-shaking zone of large earthquakes

Wang, Yuan, Li, Shanyou, Zollo, Aldo, Colombelli, Simona

2024

Overview

The rapid and accurate prediction of earthquake Strong-Shaking Zone (SSZ) is crucial for issuing precise early warnings to regions at high risk of strong ground shaking. Generally, the SSZ is derived from the real-time spatial distribution of observed ground motions. However, during the initial stages of large earthquakes, the SSZ is often underestimated and provide alerts without enough lead-time (the time interval between the alert declaration and the S-wave arrival to the target area). In this study, we propose an innovative approach termed Near-epicenter-based Partial Matching Crossover. Leveraging the characteristic that reliable magnitude estimates for large earthquakes are available earlier than accurate predictions of the peak ground velocity (PGV) distribution, this approach utilizes near-epicenter station data to rapidly estimate the SSZ. It achieves this by matching a segment of the fault, defined by a predetermined length, with the predicted PGV map within a 120 km radius centered at the epicenter. Application of our method to strong motion data from China, Japan and Turkey demonstrates its efficacy in quickly anticipating the post-earthquake intensity distributions for large earthquakes. Specifically, it offers a lead time of 5 s or more for 51.5% (39,354 km2), 43.3% (5772 km2), 31%(47,107 km2) and 75.3% (81,966 km2) of the IMM = V region during the M 8 Wenchuan earthquake, the M 7.3 Kumamoto earthquake, the M 7.8 Syria earthquake and M 7.6 Turkey earthquake, respectively. The presented approach introduces a novel methodology to extend the lead time for earthquake early warnings.

• Publisher: Springer Nature B.V
• Subject: Algorithms / Earthquake prediction / Earthquakes / Ground motion / Lead time / Matching / Real time / S waves / Seismic activity / Shaking / Spatial distribution