지구물리와물리탐사 (Geophysics and Geophysical Exploration)

  • 제24권2호
  • /
  • Pages.53-66
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  • 2021
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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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심층 신경망을 이용한 탄성파 속도 모델 구축 사례 분석

Case Analysis of Seismic Velocity Model Building using Deep Neural Networks

  • 조준현 (부경대학교 에너지자원공학과) ;
  • 하완수 (부경대학교 에너지자원공학과)
  • Jo, Jun Hyeon (Department of Energy Resources Engineering, Pukyong National University) ;
  • Ha, Wansoo (Department of Energy Resources Engineering, Pukyong National University)
  • 투고 : 2021.05.10
  • 심사 : 2021.05.27
  • 발행 : 2021.05.31
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⟨ 이전 논문 다음 논문 ⟩

초록

속도 모델 구축은 탄성파 탐사 자료처리에서 필수적인 절차이다. 주시 토모그래피나 속도 분석과 같은 기존 기법들은 하나의 속도 모델을 예측하는 데 계산 시간이 오래 걸리며 역산 결과의 품질이 전문가의 판단에 크게 의존한다. 전파형 역산 또한 초기 속도 모델에 크게 의존한다는 문제가 있다. 최근 심층 신경망 기법이 복잡하고 비선형적인 문제를 푸는데 적용되는 사례가 많아지면서 널리 보급되고 있다. 이 논문에서는 심층 신경망 기법을 이용한 탄성파 속도 모델 구축 사례들을 각 연구에 사용한 신경망에 따라 분류하며 조사하였다. 또한 훈련용 인공 속도 모델 생성 사례도 포함하였다. 심층 신경망은 대량의 데이터로부터 신경망을 훈련함으로써 모델 매개변수를 자동으로 최적화한다. 따라서 기존 기법들에 비해 역산 결과에 사람의 판단이 개입될 여지가 적으며 훈련을 마친 후 하나의 속도 모델을 예측하는 비용은 무시할 수 있다. 또한, 심층 신경망은 전파형 역산과 달리 초기 속도 모델이 필요하지 않다. 여러 연구에서 계산 비용뿐만 아니라 역산 결과에서도 심층 신경망 기법이 뛰어난 성과를 달성하는 것을 보여주었다. 연구 결과들을 바탕으로 속도 모델 구축에 사용된 심층 신경망 기법의 특징에 대해 분석하고 논의하였다.

Velocity model building is an essential procedure in seismic data processing. Conventional techniques, such as traveltime tomography or velocity analysis take longer computational time to predict a single velocity model and the quality of the inversion results is highly dependent on human expertise. Full-waveform inversions also depend on an accurate initial model. Recently, deep neural network techniques are gaining widespread acceptance due to an increase in their integration to solving complex and nonlinear problems. This study investigated cases of seismic velocity model building using deep neural network techniques by classifying items according to the neural networks used in each study. We also included cases of generating training synthetic velocity models. Deep neural networks automatically optimize model parameters by training neural networks from large amounts of data. Thus, less human interaction is involved in the quality of the inversion results compared to that of conventional techniques and the computational cost of predicting a single velocity model after training is negligible. Additionally, unlike full-waveform inversions, the initial velocity model is not required. Several studies have demonstrated that deep neural network techniques achieve outstanding performance not only in computational cost but also in inversion results. Based on the research results, we analyzed and discussed the characteristics of deep neural network techniques for building velocity models.

키워드

과제정보

논문은 부경대학교 자율창의학술연구비(2019년)에 의하여 연구되었음.

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