A Study on Earthquake Detection Performance of MEMS Sensors According to Seismic Observation Network Density

Abstract

Earthquake early warning (EEW) is a technology that aims to reduce damage by notifying an alarm message before a large shaking due to an earthquake. The EEW currently adopted by most national or local governments is a network-based method. Network-based EEW produces seismic source information based on seismic wave detection from at least three observation stations, so the density of the observation network is a very important factor in shortening the warning time. However, a huge budget and space are required to construct a dense seismic observation network. In order to compensate for such limitations, all sensors capable of detecting vibration are being expanded and applied to seismometers. The Korea Meteorological Administration (KMA) signed an MOU with a private telecommunication service provider and installed about 6,700 MEMS sensors across the country. This is about 22 times more sensors than the existing KMA seismic observation network. In this study, the seismic detection performance of MEMS sensors and the KMA seismometers installed across the country was analyzed from the perspective of time. Since it is difficult to apply the existing seismic wave detection technology to the MEMS sensor as it is, an artificial intelligence-based seismic detection technology was applied. We compared the analysis results of the KMA observation network and the MEMS observation network in real-time for earthquakes of M 3.5 or greater. As a result of real-time detection, it was found that the high-density of observation network was more effective in detecting earthquakes than the performance of the sensor.