Geophysics and Geophysical Exploration (지구물리와물리탐사)

Korean Society of Earth and Exploration Geophysicists (한국지구물리·물리탐사학회)
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Improvements in Patch-Based Machine Learning for Analyzing Three-Dimensional Seismic Sequence Data

3차원 탄성파자료의 층서구분을 위한 패치기반 기계학습 방법의 개선

  • Lee, Donguk (Marine Active Fault Research Unit, Korea Institute of Ocean Science and Technology) ;
  • Moon, Hye-Jin (Marine Active Fault Research Unit, Korea Institute of Ocean Science and Technology) ;
  • Kim, Chung-Ho (Marine Active Fault Research Unit, Korea Institute of Ocean Science and Technology) ;
  • Moon, Seonghoon (Marine Active Fault Research Unit, Korea Institute of Ocean Science and Technology) ;
  • Lee, Su Hwan (Marine Active Fault Research Unit, Korea Institute of Ocean Science and Technology) ;
  • Jou, Hyeong-Tae (Marine Active Fault Research Unit, Korea Institute of Ocean Science and Technology)
  • 이동욱 (해저활성단층연구단, 한국해양과학기술원) ;
  • 문혜진 (해저활성단층연구단, 한국해양과학기술원) ;
  • 김충호 (해저활성단층연구단, 한국해양과학기술원) ;
  • 문성훈 (해저활성단층연구단, 한국해양과학기술원) ;
  • 이수환 (해저활성단층연구단, 한국해양과학기술원) ;
  • 주형태 (해저활성단층연구단, 한국해양과학기술원)
  • Received : 2022.01.19
  • Accepted : 2022.03.29
  • Published : 2022.05.31
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Abstract

Recent studies demonstrate that machine learning has expanded in the field of seismic interpretation. Many convolutional neural networks have been developed for seismic sequence identification, which is important for seismic interpretation. However, expense and time limitations indicate that there is insufficient data available to provide a sufficient dataset to train supervised machine learning programs to identify seismic sequences. In this study, patch division and data augmentation are applied to mitigate this lack of data. Furthermore, to obtain spatial information that could be lost during patch division, an artificial channel is added to the original data to indicate depth. Seismic sequence identification is performed using a U-Net network and the Netherlands F3 block dataset from the dGB Open Seismic Repository, which offers datasets for machine learning, and the predicted results are evaluated. The results show that patch-based U-Net seismic sequence identification is improved by data augmentation and the addition of an artificial channel.

최근의 연구들을 통해 기계학습은 탄성파 해석 분야에 그 적용 범위를 확장하고 있으며, 탄성파 해석에서 중요한 탄성파 층서 구분을 수행하는 합성곱 신경망들의 개발도 수행되었다. 하지만 지도 학습의 경우 대량의 학습 자료가 필요하며, 비용과 시간의 한계로 탄성파 층서구분의 지도학습은 학습 자료의 부족이 문제가 될 수 있다. 이번 연구에서는 자료 부족 문제를 보완하기위해 탄성파 단면에 패치 분할과 자료증강을 적용하였다. 또한 패치 분할로 손실될 수 있는 공간정보를 제공하기 위해 깊이를 고려할 수 있는 인공 채널을 생성하여 추가하였다. 실험을 위한 학습 모델로 U-Net을 사용하였으며, 층서 구분을 위한 학습 자료가 제공되는 F3 block 자료를 이용하여 학습과 예측 결과에 대한 평가를 수행하였다. 분석 결과 자료증강과 인공 채널의 추가로 패치 기반의 층서 구분 학습 모델을 개선할 수 있음을 확인하였다.

Keywords

Acknowledgement

이 연구는 한국해양과학기술원 주요사업인 '해양방위 및 안전기술 개발(PEA0041)'과 산업통상자원부의 '대심도 해양 탐사시추를 통한 대규모 CO2 지중저장소 확보(PN90810)'의 지원을 받아 수행되었습니다.

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