Deep Learning-Based Spatio-Temporal Earthquake Prediction

Kounghoon Nam; Jong-Tae Kim; Seong-Cheol Park; Chang Ju Lee; Soo-Jin Kim; Chang Oh Choo; Gyo-Cheol Jeong

doi:10.9720/kseg.2023.1.001

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2023 Vol.33, Issue 1 Next Page

Research Article

Deep Learning-Based Spatio-Temporal Earthquake Prediction 딥러닝 기반의 시공간 지진 예측

31 March 2023. pp. 1-13

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Abstract

Predicting earthquakes is difficult due to the complexity of the systems underlying tectonic phenomena and incomplete understanding of the interactions among tectonic settings, tectonic stress, and crustal components. The Korean Peninsula is located in a stable intraplate region with a low average seismicity of M 2.3. As public interest in the earthquake grows, we analyzed earthquakes on the Korean Peninsula by attempting to predict spatio-temporal earthquake patterns and magnitudes using Facebook’s Prophet model based on deep learning, and here we discuss seismic distribution zones using DBSCAN, a cluster analysis method. The Prophet model predicts future earthquakes in Chungcheongbuk-do, Gyeonggi-do, Seoul, and Gyeongsangbuk-do.

Keywords

earthquake prediction

deep learning

cluster analysis

Prophet

DBSCAN

지진은 지체 구조, 지구조 응력, 지각 성분 및 구성 요소 간의 상호 작용을 통해 발생하는 복잡한 현상으로 이해하기 매우 어려운 시스템이기 때문에 예측하기가 쉽지 않다. 우리나라는 평균 M 2.3의 비교적 안전한 지역으로 볼 수 있으나 지진에 대한 대중의 관심이 높아짐에 따라, 한반도의 지진현상을 분석하기 위하여 딥러닝 기반의 Facebook’s Prophet 모델을 이용한 시간에 따른 지진패턴의 변화 및 공간과 규모에 따른 지진예측을 시도하였다. 또한, 진앙분포도 군집분석 방법인 DBSCAN과 비교 및 토의하였다. Prophet 지진 예측 모델링 결과 향후 경상북도뿐만 아니라 충청북도, 경기도 및 서울권역에서 지진이 발생할 것으로 예측되었다.

키워드

지진 예측

딥러닝

군집분석

Prophet

DBSCAN

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Information

Publisher :Korean Society of Engineering Geology
Publisher(Ko) :대한지질공학회
Journal Title :The Journal of Engineering Geology
Journal Title(Ko) :지질공학
Volume : 33
No :1
Pages :1-13
Received Date : 2023-02-07
Revised Date : 2023-03-13
Accepted Date : 2023-03-14
DOI :https://doi.org/10.9720/kseg.2023.1.001

The Journal of Engineering Geology ISSN:1226-5268(Print) 2287-7169(Online) 지질공학

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