A statistical approach for controlling the probability of false alarm and missed detection in smartphone-based earthquake early warning systems

TL;DR

This paper introduces a statistical maximum likelihood approach to improve smartphone-based earthquake early warning systems by controlling false alarms and missed detections, enabling near real-time earthquake parameter estimation.

Contribution

It presents a novel statistical method tailored for dynamic, noisy smartphone networks to enhance earthquake detection accuracy and reliability.

Findings

Effective control of false alarm probability.

Accurate near real-time estimation of earthquake epicenter and depth.

Validated approach using data from Earthquake Network initiative.

Abstract

Smartphone-based earthquake early warning systems (EEWS) are emerging as a complementary solution to classic EEWS based on expensive scientific-grade instruments. Smartphone-based systems, however, are characterized by a highly dynamic network geometry and by noisy measurements. Thus the need to control the probability of false alarm and the probability of missed detection. This paper proposes a statistical approach based on the maximum likelihood method to address this challenge and to jointly estimate in near real-time earthquake parameters like epicentre and depth. The approach is tested using data coming from the Earthquake Network citizen science initiative which implements a global smartphone-based EEWS.