Crowdsourced smartphone-based earthquake early warning systems recently emerged as reliable alternatives to the more expensive solutions based on scientific-grade instruments. For instance, during the 2023 Turkish-Syrian deadly event, the system implemented by the Earthquake Network citizen science initiative provided a forewarning up to 25 seconds. We develop a statistical methodology based on a survival mixture cure model which provides full Bayesian inference on epicentre, depth and origin time, and we design an efficient tempering MCMC algorithm to address multi-modality of the posterior distribution. The methodology is applied to data collected by the Earthquake Network, including the 2023 Turkish-Syrian and 2019 Ridgecrest events.

翻译：基于众包的智能手机地震早期预警系统近期已发展成为科学级仪器昂贵方案的可信替代方案。例如，在2023年土耳其-叙利亚致命地震事件中，由"地震网络"公民科学倡议实施的系统提供了长达25秒的预警。我们提出了一种基于生存混合治愈模型的统计方法，该方法可对震中、震源深度及发震时间进行全贝叶斯推断，并设计了高效的退火MCMC算法以应对后验分布的多峰性。该方法已应用于"地震网络"收集的数据，包括2023年土耳其-叙利亚地震及2019年里奇克莱斯特事件。