There are growing concerns that the recently implemented Earthquake Early Warning service is overestimating the rapidly provided earthquake magnitudes (M). As a result, the predicted damages unnecessarily activate earthquake protection systems for critical facilities and lifeline infrastructures that are far away. This study is conducted to improve the estimation accuracy of M by incorporating the observed S-wave seismograms in the near source region after removing the site effects of the seismograms in real time by filtering in the time domain. The ensemble of horizontal S-wave spectra from at least five seismograms without site effects is calculated and normalized to a hypocentric target distance (21.54 km) by using the distance attenuation model of Q(f)=348f0.52 and a cross-over distance of 50 km. The natural logarithmic mean of the S-wave ensemble spectra is then fitted to Brune’s source spectrum to obtain the best estimates for M and stress drop (SD) with the fitting weight of 1/standard deviation. The proposed methodology was tested on the 18 recent inland earthquakes in South Korea, and the condition of at least five records for the near-source region is sufficiently fulfilled at an epicentral distance of 30 km. The natural logarithmic standard deviation of the observed S-wave spectra of the ensemble was calculated to be 0.53 using records near the source for 1~10 Hz, compared to 0.42 using whole records. The result shows that the root-mean-square error of M and ln(SD) is approximately 0.17 and 0.6, respectively. This accuracy can provide a confidence interval of 0.4~2.3 of Peak Ground Acceleration values in the distant range.

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

최근 우리나라 기상청은 국내 지진 피해를 경감하기 위한 목적으로 지진조기경보 시스템의 도입을 추진하고 있다. 지진조기경보 시스템이란 지진 발생 시 피해를 유발하는 지진파 도착 이전에 경보를 발령하여 수신자의 대응시간을 확보하기 위한 시스템으로써, 미국, 일본, 멕시코, 터키, 대만, 루마니아 등에서는 이미 도입되어 활용 중이다. 기본적인 원리는 전자파와 지진파 간, 또는 지진파의 파형 간 속도 차이를 활용하는 것이며, 그 형태에 따라 크게 on-site, network-based, front-detection 방식 등으로 분류된다. 국가별 실정에 따라 관측 네트워크나 경보의 내용 혹은 전달방식 등에 차이가 있는데, 본 연구에서는 이러한 특징들에 주목하여 해외 연구 현황 및 도입 사례를 종합하였다.