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An End-to-end Approach for Earthquake Early Warning Using IoT and Deep Learning

Description: 

The primary priority of an earthquake early warning (EEW) system is to detect an earthquake, determine its properties, such as intensity and time of origin, and then send an alert to important locations where vital safety measures need to be taken before the devastating seismic wave arrives. Large-scale EEW systems are currently operational in several countries around the world. Many countries that intend to build systems attribute the decline in fatalities to the large-scale, technologically advanced EEW system. However, deploying such a system on a larger scale has limitations, including communication challenges, response actions, and system deployment and monitoring costs. Due to the high costs of a seismometer, a dense conventional earthquake network is not possible, resulting in a blind zone, an area surrounding the epicenter of an earthquake that cannot be warned before the violent shaking begins. Here, we present low-cost MEMS IoT sensors that have the potential to detect low, moderate, and high earthquake waveforms. Moreover, we take advantage of the advantages of IoT and artificial intelligence and established a dense seismic network that covers the entire Korean peninsula. Furthermore, in real-time, we evaluate our dense seismic network for several low-magnitude earthquakes.

Session: Performance and Progress of Earthquake Early Warning Systems Around the World [Poster]

Type: Poster

Date: 4/16/2025

Presentation Time: 08:00 AM (local time)

Presenting Author: Young-Woo

Student Presenter: No

Invited Presentation: 

Poster Number: 118

Authors