2023/03/01 by Luca Maria Aiello, Aiello, Luca, Raffaele Argiento +5
Computer Science · Medicine · #Anomaly Detection Techniques and Applications #Seismology and Earthquake Studies #Data-Driven Disease Surveillance
paper · pdf · doi:10.48550/arxiv.2303.00806
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.