Geophysics and Geophysical Exploration (지구물리와물리탐사)

Korean Society of Earth and Exploration Geophysicists (한국지구물리·물리탐사학회)
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Research Trend analysis for Seismic Data Interpolation Methods using Machine Learning

머신러닝을 사용한 탄성파 자료 보간법 기술 연구 동향 분석

  • Bae, Wooram (Department of Energy Resources Engineering, Pukyong National University) ;
  • Kwon, Yeji (Department of Energy Resources Engineering, Pukyong National University) ;
  • Ha, Wansoo (Department of Energy Resources Engineering, Pukyong National University)
  • 배우람 (부경대학교 에너지자원공학과) ;
  • 권예지 (부경대학교 에너지자원공학과) ;
  • 하완수 (부경대학교 에너지자원공학과)
  • Received : 2020.06.10
  • Accepted : 2020.08.24
  • Published : 2020.08.31
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Abstract

We acquire seismic data with regularly or irregularly missing traces, due to economic, environmental, and mechanical problems. Since these missing data adversely affect the results of seismic data processing and analysis, we need to reconstruct the missing data before subsequent processing. However, there are economic and temporal burdens to conducting further exploration and reconstructing missing parts. Many researchers have been studying interpolation methods to accurately reconstruct missing data. Recently, various machine learning technologies such as support vector regression, autoencoder, U-Net, ResNet, and generative adversarial network (GAN) have been applied in seismic data interpolation. In this study, by reviewing these studies, we found that not only neural network models, but also support vector regression models that have relatively simple structures can interpolate missing parts of seismic data effectively. We expect that future research can improve the interpolation performance of these machine learning models by using open-source field data, data augmentation, transfer learning, and regularization based on conventional interpolation technologies.

탄성파 탐사를 수행할 때 경제적, 환경적 제약 또는 탐사 장비의 문제 등에 의해 탄성파 자료의 일부가 규칙적 또는 불규칙적으로 손실되는 경우가 발생하게 된다. 이러한 자료 손실은 탄성파 자료 처리와 해석 결과에 부정적인 영향을 주기 때문에 사라진 탄성파 자료를 복원할 필요가 있다. 탄성파 자료 복원을 위해 재탐사 또는 추가적인 탐사를 진행하는 경우 시간적, 경제적 비용이 발생하기 때문에, 많은 연구자들이 사라진 탄성파 자료를 정확히 복원하기 위한 보간 기법 연구를 진행해왔다. 최근에는 머신러닝 기술 발달에 따라 머신러닝 기법을 활용한 연구들이 진행되고 있고, 다양한 머신러닝 기술들 중에서도 서포트 벡터 회귀, 오토인코더, 유넷, 잔차넷, 생성적 적대 신경망 등의 알고리즘을 활용한 탄성파 자료의 보간 연구가 활발하게 진행되고 있다. 이 논문에서는 이러한 연구들을 조사하고 분석하여 복잡한 신경망 모델뿐 아니라 상대적으로 구조가 간단한 서포트 벡터 회귀 모델을 통해서도 뛰어난 보간 결과를 얻을 수 있다는 것을 확인했다. 추후 머신러닝 기법들을 사용하는 탄성파 자료 보간 연구들에서 오픈소스로 공개된 실제 자료를 이용하며 데이터 증식, 전이학습, 기존 기법을 이용한 규제 등의 기술을 활용하면 탄성파 자료 보간 성능을 향상시킬 수 있을 것으로 기대된다.

Keywords

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