A0556
Title: Survival modelling of smartphone trigger data for earthquake parameter estimation in early warning
Authors: Luca Aiello - University of Milano Bicocca (Italy) [presenting]
Lucia Paci - Universita Cattolica del Sacro Cuore (Italy)
Raffaele Argiento - Università degli Studi di Bergamo (Italy)
Francesco Finazzi - University of Bergamo (Italy)
Abstract: 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 of up to 25 seconds. A statistical methodology is developed based on a survival mixture cure model, which provides full Bayesian inference on epicenter, depth and origin time, and an efficient tempering MCMC algorithm is designed to address the 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.
