Hazard-informed optimization of seismic networks for earthquake early warning-the case of the Lower Rhine Embayment (western Germany)

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.