Stable operation process of earthquake early warning system based on machine learning: trial test and management perspective

Abstract

Earthquake Early Warning (EEW) is an alert system, based on seismic wave propagation theory, to reduce human casualties. EEW systems mainly utilize technologies through both network-based and on-site methods. The network-based method estimates the hypocenter and magnitude of an earthquake using data from multiple seismic stations, while the on-site method predicts the intensity measures from a single seismic station. Therefore, the on-site method reduces the lead time compared to the network-based method but is less accurate. To increase the accuracy of on-site EEW, our system was designed with a hybrid method, which included machine learning algorithms. At this time, machine learning was used to increase the accuracy of the initial P-wave identification rate. Additionally, a new approach using a nearby seismic station, called the 1+ α method, was proposed to reduce false alarms. In this study, an on-site EEW trial operation was performed to evaluate its performance. The warning cases for small and large events were reviewed and the possibility of stable alert decisions was confirmed.

Assessing network-based earthquake early warning systems in low-seismicity areas

“…We believe that developing a damage-based strategic evaluation technique holds practical significance (Iervolino et al, 2011;Papadopoulos et al, 2023). The KMA is also engaged in the development and experimentation of on-site EEW systems based on IM (Ahn et al, 2023). However, these methods differ from networkbased EEWs.…”

“…KMA EEW progress relies on warning time reduction, as it is crucial to warn people as quickly as possible in the case of an earthquake (Bostrom et al, 2022;Ahn et al, 2023). Warning time is a critical factor in assessing preparedness for an earthquake and a vital consideration from a risk management perspective (Allen et al, 2009).…”