A simulation framework for statistical inference on the alerting capabilities of smartphone-based earthquake early warning systems. With a case study on the Earthquake Network system in Haiti

TL;DR

This paper introduces a Monte Carlo simulation framework to evaluate the alerting capabilities of smartphone-based earthquake early warning systems, demonstrated through a case study on Haiti's Earthquake Network.

Contribution

It presents a novel simulation approach to assess earthquake detection performance considering network variability, specifically applied to citizen science systems in Haiti.

Findings

Robust early warning possible with low system penetration.

Warning times up to 12 seconds for high-intensity earthquakes.

System effectiveness depends on network geometry and population coverage.

Abstract

Smartphone-based earthquake early warning systems implemented by citizen science initiatives are characterized by a significant variability in their smartphone network geometry. This has an direct impact on the earthquake detection capability and performance of the system. Here, a simulation framework based on the Monte Carlo method is implemented for making inference on relevant quantities of the earthquake detection such as the detection distance from the epicentre, the detection delay and the warning time for people exposed to high ground shaking levels. The framework is applied to Haiti, which has experienced deadly earthquakes in the past decades, and to the network of the Earthquake Network citizen science initiative, which is popular in the country. It is discovered that relatively low penetrations of the initiative among the population allow to offer a robust early warning service, with warning times up to 12 second for people exposed to intensities between 7.5 and 8.5 of the modified Mercalli scale.