Crowdsourced smartphone-based earthquake early warning systems have recently emerged as reliable alternatives to more expensive solutions based on scientific instruments. For example, during the deadly 2023 Pazarcik event in Turkey, the system implemented by the Earthquake Network citizen science initiative provided up to 58 s of warning to people exposed to life-threatening ground shaking. We develop a statistical methodology based on a survival mixture cure model that provides full Bayesian inference on epicentre, depth, and origin time, and we design a tempering Markov chain Monte Carlo algorithm to account for the multi-modality of the posterior distribution. The methodology is applied to data collected by the Earthquake Network during three seismic events, including the 2023 Pazarcik and 2019 Ridgecrest earthquakes.

(2025). Survival modelling of smartphone trigger data in crowdsourced seismic monitoring: with applications to the 2023 Pazarcik and 2019 Ridgecrest earthquakes [journal article - articolo]. In JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A. STATISTICS IN SOCIETY. Retrieved from https://hdl.handle.net/10446/292386

Survival modelling of smartphone trigger data in crowdsourced seismic monitoring: with applications to the 2023 Pazarcik and 2019 Ridgecrest earthquakes

Aiello, Luca;Argiento, Raffaele;Finazzi, Francesco;
2025-01-13

Abstract

Crowdsourced smartphone-based earthquake early warning systems have recently emerged as reliable alternatives to more expensive solutions based on scientific instruments. For example, during the deadly 2023 Pazarcik event in Turkey, the system implemented by the Earthquake Network citizen science initiative provided up to 58 s of warning to people exposed to life-threatening ground shaking. We develop a statistical methodology based on a survival mixture cure model that provides full Bayesian inference on epicentre, depth, and origin time, and we design a tempering Markov chain Monte Carlo algorithm to account for the multi-modality of the posterior distribution. The methodology is applied to data collected by the Earthquake Network during three seismic events, including the 2023 Pazarcik and 2019 Ridgecrest earthquakes.
francesco.finazzi@unibg.it
articolo
13-gen-2025
13-gen-2025
Inglese
online
1
16
Settore STAT-01/B - Statistica per la ricerca sperimentale e tecnologica
Settore STAT-01/A - Statistica
Bayesian analysis; citizen science; cure models; Markov chain Monte Carlo; mixture modelling;
   Real-time Earthquake Risk Reduction for a Resilient Europe
   RISE
   UNIONE EUROPEA - COMMISSIONE EUROPEA
   821115
Aiello, Luca; Argiento, Raffaele; Finazzi, Francesco; Paci, Lucia
info:eu-repo/semantics/article
reserved
(2025). Survival modelling of smartphone trigger data in crowdsourced seismic monitoring: with applications to the 2023 Pazarcik and 2019 Ridgecrest earthquakes [journal article - articolo]. In JOURNAL OF THE ROYAL STATISTICAL SOCIETY. SERIES A. STATISTICS IN SOCIETY. Retrieved from https://hdl.handle.net/10446/292386
Non definito
4
1.1 Contributi in rivista - Journal contributions::1.1.01 Articoli/Saggi in rivista - Journal Articles/Essays
262
File allegato/i alla scheda:
File Dimensione del file Formato  
qnae148.pdf

Solo gestori di archivio

Versione: publisher's version - versione editoriale
Licenza: Licenza default Aisberg
Dimensione del file 818.02 kB
Formato Adobe PDF
818.02 kB Adobe PDF   Visualizza/Apri
Pubblicazioni consigliate

Aisberg ©2008 Servizi bibliotecari, Università degli studi di Bergamo | Terms of use/Condizioni di utilizzo

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/292386
Citazioni
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact