Smartphone-based earthquake early warning systems (EEWSs) are emerging as a complementary solution to classic EEWSs based on expensive scientific-grade instruments. Smartphone-based systems, however, are characterized by a highly dynamic network geometry and by noisy measurements. Thus, there is a need to control the probability of false alarms and the probability of missed detection. This study proposes a statistical methodology to address this challenge and to jointly estimate in near real-time earthquake parameters like epicenter and depth. The methodology is based on a parametric statistical model, on hypothesis testing and on Monte Carlo simulation. The methodology is tested using data obtained from the Earthquake Network (EQN), a citizen science initiative that implements a global smartphone-based EEWS. It is discovered that, when the probability to miss an earthquake is fixed at 1%, the probability of false alarm is 0.8%, proving that EQN is a robust smartphone-based EEW system.

(2023). A statistical methodology for classifying earthquake detections and for earthquake parameter estimation in smartphone-based earthquake early warning systems [journal article - articolo]. In FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS. Retrieved from https://hdl.handle.net/10446/238609

A statistical methodology for classifying earthquake detections and for earthquake parameter estimation in smartphone-based earthquake early warning systems

Finazzi, Francesco
2023-01-01

Abstract

Smartphone-based earthquake early warning systems (EEWSs) are emerging as a complementary solution to classic EEWSs based on expensive scientific-grade instruments. Smartphone-based systems, however, are characterized by a highly dynamic network geometry and by noisy measurements. Thus, there is a need to control the probability of false alarms and the probability of missed detection. This study proposes a statistical methodology to address this challenge and to jointly estimate in near real-time earthquake parameters like epicenter and depth. The methodology is based on a parametric statistical model, on hypothesis testing and on Monte Carlo simulation. The methodology is tested using data obtained from the Earthquake Network (EQN), a citizen science initiative that implements a global smartphone-based EEWS. It is discovered that, when the probability to miss an earthquake is fixed at 1%, the probability of false alarm is 0.8%, proving that EQN is a robust smartphone-based EEW system.
articolo
2023
Massoda Tchoussi, Frank Yannick; Finazzi, Francesco
(2023). A statistical methodology for classifying earthquake detections and for earthquake parameter estimation in smartphone-based earthquake early warning systems [journal article - articolo]. In FRONTIERS IN APPLIED MATHEMATICS AND STATISTICS. Retrieved from https://hdl.handle.net/10446/238609
File allegato/i alla scheda:
File Dimensione del file Formato  
fams-09-1107243.pdf

accesso aperto

Versione: publisher's version - versione editoriale
Licenza: Creative commons
Dimensione del file 2.43 MB
Formato Adobe PDF
2.43 MB 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/10446/238609
Citazioni
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact