Earthquake early warning (EEW) systems can serve as a viable solution to protect specific hazard‐prone targets (major cities or critical infrastructure) against harmful seismic events. Using the example of the Lower Rhine Embayment (western Germany), we present a novel approach for evaluating and optimizing seismic networks for EEW purposes. The network optimization is applied to simulated earthquake scenarios from a hazard-compatible stochastic catalog, which represents a realization of the seismicity in the target area over a given period of time. We propose a densification of the existing network in the area by pre-selecting a number of potential sites with an optimal station configuration using a microgenetic algorithm, minimizing an appropriate cost function associated with the network layout. We show that the new decentralized network significantly improves the warning time and the accuracy of the warnings for levels of shaking for threshold levels of at least 0.02 g. Although the accuracy of the alerts for other cities outside the target area varies depending on their location, we demonstrate that the updated network layout will also improve the warning times for neighboring cities.

地震早期预警 (EEW) 系统可以作为保护特定易受灾害目标（主要城市或关键基础设施）免受有害地震事件影响的可行解决方案。以下莱茵河湾（德国西部）为例，我们提出了一种用于评估和优化地震台网以实现 EEW 目的的新方法。网络优化应用于来自灾害兼容随机目录的模拟地震场景，这代表了在给定时间段内目标区域地震活动的实现。我们建议通过使用微观遗传算法预先选择具有最佳站点配置的多个潜在站点来密集化该地区的现有网络，从而最大限度地减少与网络布局相关的适当成本函数。我们表明，对于至少 0.02 g 的阈值水平，新的去中心化网络显着提高了警告时间和振动水平警告的准确性。尽管针对目标区域以外的其他城市的警报准确性因位置而异，但我们证明更新的网络布局也将改善邻近城市的警报时间。