No abstract

Most countries and local governments provide earthquake services in the public domain, and they must have high accuracy. If a missed alarm of the Earthquake Early Warning (EEW) system causes many casualties, or if the industrial system is temporarily suspended owing to a false alarm, causing economic losses, it inevitably becomes the responsibility of the government. Therefore, most countries approach the technological improvement of EEW systems carefully by performing simulations and conducting long-term tests to ensure their reliability. In this study, we extract characteristics of the initial P-wave amplitude from an earthquake on the Korean Peninsula and perform trend analysis. We found a common optimal threshold on the Korea Meteorological Administration's seismic observatory network from trend analysis. We then evaluated the performance of the optimized algorithm based on the simulation. The performance evaluated the actual events recorded corresponding to the number of matched, missed, and false events. As the result of the evaluation, the optimized module combination had a significantly lower occurrence of false events than the previous version. Therefore, we expected that the proposed optimization should contribute to improving alarm stability in real-time EEW.

: Mt. SAKRAJIMA in southern Kagosima, japan is one of the most active volcanoes in the world. On 18 August 2013, the SAKRAJIMA volcano recently went into the largest scaled eruption with a huge plume of volcanic ash. Therefore, the concern arises if this considerable amount of ashes might flow into the Korea peninsula as well as Japan. In this paper, we performed numeric experiment to analyze how volcanic product resulted from the SAKRAJIMA volcano has impacted on Korea. In order to predict the spread pathway of ash, HYSPLIT model and UM data has been used and 17th September 2013 has been selected as observation date since it is expected that the volcanic ash would flow into the South Korea. In addition, we have detected ash dispersion by using optical Communication, Ocean and Meteorological Satellite-Geostationary Ocean Color Imager (COMS-GOCI) images. As the results, we come to a very satisfactory conclusion that the spread pathway of volcanoes based on HYSPLIT model are matched 63.52 % with ash dispersion area detected from GOCI satellites image.

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.

Proactively responding to earthquakes is challenging. Researchers have developed methods to reduce structural damage from earthquakes. The Earthquake Early Warning (EEW) service is more effective than seismic design at reducing human casualties. We developed an on-site EEW that can provide alerts faster than the network-based EEW. However, on-site EEW is not used as a public service due to the low alarm accuracy of the existing on-site alarm. Therefore, we applied both methods to improve the alarming accuracy of on-site EEW. First, the developed system increased the accuracy of the identification rate for the initial P-wave using deep learning. Second, the method utilizing a nearby seismometer was added to improve the accuracy of the alarm. We conducted an on-site EEW trial operation and checked the performance from 2020.10.08 to 2022.03.31. We reviewed the warning cases for small and large events during the trial and confirmed the possibility of automated alert decisions.