Abstract
As the occurrence of earthquakes is increasing in South Korea, the earthquake early warning (EEW) system becomes indispensable for the protection of high-speed railways. Although the importance of EEW system has been increasing, the number of installed seismic accelerometers in South Korea is not sufficient to provide rapid information. This study uses a stochastic signal analysis technique to utilize the smartphone sensors for the rapid EEW system. From the train vibration data from the low fidelity on-board accelerometer, the virtual earthquake detection data in the train by smartphone sensor has been constructed. To analyze the stochastic characteristics of the constructed data, the short time Fourier transform (STFT) approach has been applied. The study’s overall objective is to offer stochastic approaches that provide effective analysis of the low fidelity sensor data, such as smartphone sensor data, for the rapid EEW system.
Highlights
In South Korea, earthquake awareness is increasing due to a series of consecutive magnitude5.0 earthquakes, including the 2016 Gyeongju [1] and 2017 Pohang [2] earthquakes
For the high-speed Korea Train Express (KTX) railway in South Korea, seismic detection systems have been constructed and operated by seismic accelerometers that are installed at 20–30 km intervals in major piers and tunnels, as well as by borehole accelerometers in the free fields separated from tracks and pier structures [3]
The constructed data were subjected to short time Fourier transform (STFT) and compared with the train vibration data
Summary
In South Korea, earthquake awareness is increasing due to a series of consecutive magnitude. The most recent study is MyShake (http://myshake.berkeley.edu), which developed a seismic observation network application using smartphone sensors and has more than 300,000 registered users worldwide This application can alert users to an earthquake in advance due to its early warning functions. A strategy to distinguish normal vibration load from seismic vibrations is necessary To this end, the vibration and earthquake data measured during train operation were analyzed, and the seismic detection of on-board accelerometers was evaluated. The constructed data were subjected to short time Fourier transform (STFT) and compared with the train vibration data Based on this comparison, we investigated the practicality and effectiveness of detecting an earthquake on a train by this method, with regards to the value of smartphone sensors for this application
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