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Fault Detection for Seismic Data Interpretation Based on Machine Learning: Research Trends and Technological Introduction

기계 학습 기반 탄성파 자료 단층 해석: 연구동향 및 기술소개

  • Choi, Woochang (Department of Energy Resources Engineering, Inha University) ;
  • Lee, Ganghoon (Department of Energy Resources Engineering, Inha University) ;
  • Cho, Sangin (Department of Energy Resources Engineering, Inha University) ;
  • Choi, Byunghoon (Department of Energy Resources Engineering, Inha University) ;
  • Pyun, Sukjoon (Department of Energy Resources Engineering, Inha University)
  • 최우창 (인하대학교 에너지자원공학과) ;
  • 이강훈 (인하대학교 에너지자원공학과) ;
  • 조상인 (인하대학교 에너지자원공학과) ;
  • 최병훈 (인하대학교 에너지자원공학과) ;
  • 편석준 (인하대학교 에너지자원공학과)
  • Received : 2020.03.05
  • Accepted : 2020.04.24
  • Published : 2020.05.31

Abstract

Recently, many studies have been actively conducted on the application of machine learning in all branches of science and engineering. Studies applying machine learning are also rapidly increasing in all sectors of seismic exploration, including interpretation, processing, and acquisition. Among them, fault detection is a critical technology in seismic interpretation and also the most suitable area for applying machine learning. In this study, we introduced various machine learning techniques, described techniques suitable for fault detection, and discussed the reasons for their suitability. We collected papers published in renowned international journals and abstracts presented at international conferences, summarized the current status of the research by year and field, and intensively analyzed studies on fault detection using machine learning. Based on the type of input data and machine learning model, fault detection techniques were divided into seismic attribute-, image-, and raw data-based technologies; their pros and cons were also discussed.

최근 과학기술 및 공학 전 분야에서 기계 학습을 적용하는 연구들이 매우 활발하게 수행되고 있다. 탄성파 탐사 분야 또한 해석, 처리, 취득 등 모든 영역에서 기계 학습을 적용한 연구들이 빠르게 증가하는 추세이다. 그 중 단층 해석은 탄성파 자료 해석 분야에 있어 가장 중요한 기술 중 하나이며, 기계 학습을 적용하기에 가장 적합한 분야이기도 하다. 이 논문에서는 다양한 기계 학습 기법들에 대해 소개하고 단층 해석에 적합한 기법들과 그 이유를 기술하였다. 물리탐사 분야의 저명한 국제 학술지에 게재된 논문과 국제 학술대회 발표 사례들을 조사하여 연도별, 분야별 연구 현황을 정리하였으며, 그 중 기계 학습을 사용한 단층 해석 연구들을 집중적으로 분석하였다. 단층 해석 기술은 입력 자료 및 기계 학습 모델의 형태에 따라 탄성파 속성 기반 기술, 탄성파 이미지 기반 기술, 원시자료 기반 기술로 나누어 그 장단점을 기술하였다.

Keywords

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