Robust Earthquake Early Warning at a Fraction of the Cost: ASTUTI Costa Rica

Abstract

Earthquake early warning's (EEW) fundamental promise is that earthquakes can be rapidly detected so that people and systems can be alerted to take protective action before shaking arrives at their location (Heaton, 1985). In order to maximize warning time and to minimize the population not receiving sufficient warning, EEW requires a dense sensor network so that earthquakes can be detected closest to wherever they may nucleate. The locations where earthquakes nucleate and where people reside, however, might be quite removed from one another. This requirement for dense sensor networks combined with the high cost of expensive scientific-grade sensors currently limits EEW systems to wealthy countries (Allen & Melgar, 2019). Alternatively, a new generation of low-cost accelerometer and geodetic sensors (Cochran, 2018) could make EEW generally accessible. In particular, utilizing smartphones, including via crowd-sourcing, is a potentially transformative way to provide EEW (

“Saving Precious Seconds”—A Novel Approach to Implementing a Low-Cost Earthquake Early Warning System with Node-Level Detection and Alert Generation

“…Off-the-shelf sensors similar to those seen in smartphones are also available as scalable opportunistic sensor nodes [41]. Phone-based sensing is possible because almost all smartphones are equipped with MEMS-based accelerometers.…”

“…An ongoing, low-cost, crowdsourced, smartphone app-based EEW solution is the MyShake Platform; since its release in February 2016, more than 300,000 people globally have downloaded the app, providing EEW networks in different parts of the world within a short period of time [53]. Similarly, a network of 82 smartphones fixated in buildings in Costa Rica proposed an EEW with a significantly lower cost than a scientific-grade network [41]. Another crowdsourced EEW system is the SeismoCloud App, which shows promise in delivering EEW to users in a region [54].…”

“…Enhanced performance of both the fog and edge layer devices have created opportunities for decentralised processing and storage more realistic with the ability to achieve lower latencies, lower operational costs and increased network redundancy. However, when it comes to EEW networks, whether it is high-end sensor networks or low-cost MEMS-based networks, data processing in EEW systems was traditionally carried out at the cloud layer, and the warning sent out as push notifications to the end-users [41,59]. Data-processing at a central location provides the advantage of better control in the detection, collection and processing of data during a disaster and immediately afterwards [16].…”

“…Detection time(δt detect ) is the time taken for a particular detection algorithm to calculate and issue an alert of an earthquake when seismic intensity at a sensor goes above the predefined shaking intensity threshold [41]. Therefore, δt detect depends on the type of the algorithm and its complexity.…”

“…Thereafter, the pre-programmed data simulator in each sensor were triggered at the exact time when the S-wave (with the velocity of 3 km/s) of each earthquake scenario was expected to reach that particular sensor. For the simulations, we assumed that the earthquake waves travelled as circular waves [41]. For the hypothetical earthquake dataset, we used a ground motion dataset captured by our RS sensor network.…”