Early warnings have been developed to provide rapid earthquake information, allowing people to prepare as much time as possible. However, since it takes several seconds for an earthquake warning to be issued, the blind zone is inevitable. To reduce the blind zone, information from a single observatory is used to operate an on-site earthquake warning. However, false and missed alarms are still high, requiring continued research and validation. This study predicted Peak Ground Acceleration (PGA) using the characteristic data to reduce false and missed alarms in on-site earthquake warnings. A machine learning prediction model was created using the initial P-wave parameters developed from the characteristic data to achieve this. Then, the model was used to predict the maximum ground acceleration in the southeastern region of the Korean Peninsula. The expected results for six target earthquakes were confirmed to have a standard deviation within 0.3 compared to the observed PGA and the values within ±2 sigma. This method is expected to help develop an on-site early warning system for earthquakes.

A B S T R A C T
1. 서 론
2. 연구 방법 및 절차
3. 연구자료 선정
4. 상관성 분석
5. 최대지반가속도 예측
    5.1 모델 설계 및 자료 전처리
    5.2 머신러닝 모델 학습 및 적용
    5.3 연구 결과
6. 결 론
감사의 글
REFERENCES

• 윤여웅(한국지질자원연구원 지진연구센터 책임기술원) | Yun Yeo Woong (Principal Engineer, Earthquake Research Center, Korea Institute of Geoscience and Mineral Resources)
• 임인섭(한국지질자원연구원 지진연구센터 책임기술원) | Lim In Seub (Principal Engineer, Earthquake Research Center, Korea Institute of Geoscience and Mineral Resources)
• 박정호(한국지질자원연구원 지진연구센터 책임연구원) | Park Jung Ho (Principal Researcher, Earthquake Research Center, Korea Institute of Geoscience and Mineral Resources)
• 유성화(한국지질자원연구원 지진연구센터 책임기술원) | Yoo Seong Hwa (Principal Engineer, Earthquake Research Center, Korea Institute of Geoscience and Mineral Resources) Corresponding author