지구물리와물리탐사 (Geophysics and Geophysical Exploration)

  • 제25권4호
  • /
  • Pages.252-265
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  • 2022
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  • 1229-1064(pISSN)
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  • 2384-051X(eISSN)
한국지구물리·물리탐사학회 (Korean Society of Earth and Exploration Geophysicists)
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가역 도약 마르코프 연쇄 몬테 카를로 방법을 이용한 물성 역산 기술 소개

Introduction to Subsurface Inversion Using Reversible Jump Markov-chain Monte Carlo

  • 전형구 (경북대학교 지질학과) ;
  • 조용채 (서울대학교 에너지시스템공학부)
  • Hyunggu, Jun (Department of Geology, Kyungpook National University) ;
  • Yongchae, Cho (Department of Energy Resources Engineering, Seoul National University)
  • 투고 : 2022.09.30
  • 심사 : 2022.11.23
  • 발행 : 2022.11.30
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초록

지하 매질의 물성 정보는 지층 구조의 정확한 영상화를 위해 필요하며, 예측된 매질 물성 자체도 지하 매질 특성에 대한 중요한 정보를 제공해줄 수 있기 때문에 다양한 종류의 지층 물성 도출 알고리듬들이 개발되고 적용되어왔다. 그 중 마르코프 연쇄 몬테 카를로를 이용한 확률론적인 접근 방법은 기존의 결정론적인 접근 방법과는 달리 지역 최소값 문제를 완화시킬 수 있으며 역산 결과의 불확실성을 정량화할 수 있다는 부분에서 장점을 가진다. 따라서 마르코프 연쇄 몬테 카를로를 이용한 지층 물성 역산 알고리듬이 다양한 지구 물리 자료의 역산에 적용되어 왔으나 그 사례는 결정론적 접근 방법에 비해 매우 적다. 본 논문에서는 여러 형태의 마르코프 연쇄 몬테 카를로 역산 알고리듬 중 가역 도약을 적용한 가역 도약 마르코프 연쇄 몬테 카를로 역산을 탄성파 자료 역산에 적용한 다양한 사례들을 소개하고 각각의 특성을 설명한다. 또한 가역 도역 마르코프 연쇄 몬테 카를로 역산의 장단점에 대해 분석하고 향후 해당 알고리듬의 연구 방향 및 국내의 활용성에 대해 논의한다.

Subsurface velocity is critical for the accurate resolution geological structures. The estimation of acoustic impedance is also critical, as it provides key information regarding the reservoir properties. Therefore, researchers have developed various inversion approaches for the estimation of reservoir properties. The Markov chain Monte Carlo method, which is a stochastic method, has advantages over the deterministic method, as the stochastic method enables us to attenuate the local minima problem and quantify the uncertainty of inversion results. Therefore, the Markov chain Monte Carlo inversion method has been applied to various kinds of geophysical inversion problems. However, studies on the Markov chain Monte Carlo inversion are still very few compared with deterministic approaches. In this study, we reviewed various types of reversible jump Markov chain Monte Carlo applications and explained the key concept of each application. Furthermore, we discussed future applications of the stochastic method.

키워드

과제정보

이 논문은 2022년도 정부(산업통상자원부)의 재원으로 한국에너지기술평가원의 지원(20226A10100030, 고성능 해양 CO2 저장 모니터링 기술개발)과 2022년도 정부(교육부)의 재원으로 한국연구재단의 지원(2022R1I1A3066265)을 받아 수행된 연구입니다. 본 논문은 서울대 신임교수 연구정착금 지원사업(0456-20220039)의 재원을 지원 받아 수행되었습니다.

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