Geophysics and Geophysical Exploration (지구물리와물리탐사)

Korean Society of Earth and Exploration Geophysicists (한국지구물리·물리탐사학회)
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Introduction to Geophysical Exploration Data Denoising using Deep Learning

심층 학습을 이용한 물리탐사 자료 잡음 제거 기술 소개

  • Caesary, Desy (Department of Energy & Mineral Resources Engineering, Sejong University) ;
  • Cho, AHyun (Department of Energy & Mineral Resources Engineering, Sejong University) ;
  • Yu, Huieun (Department of Energy & Mineral Resources Engineering, Sejong University) ;
  • Joung, Inseok (Department of Energy & Mineral Resources Engineering, Sejong University) ;
  • Song, Seo Young (Department of Energy & Mineral Resources Engineering, Sejong University) ;
  • Cho, Sung Oh (Department of Energy & Mineral Resources Engineering, Sejong University) ;
  • Kim, Bitnarae (Department of Energy & Mineral Resources Engineering, Sejong University) ;
  • Nam, Myung Jin (Department of Energy & Mineral Resources Engineering, Sejong University)
  • ;
  • 조아현 (에너지자원공학과, 세종대학교) ;
  • 유희은 (에너지자원공학과, 세종대학교) ;
  • 정인석 (에너지자원공학과, 세종대학교) ;
  • 송서영 (에너지자원공학과, 세종대학교) ;
  • 조성오 (에너지자원공학과, 세종대학교) ;
  • 김빛나래 (에너지자원공학과, 세종대학교) ;
  • 남명진 (에너지자원공학과, 세종대학교)
  • Received : 2020.06.10
  • Accepted : 2020.08.27
  • Published : 2020.08.31
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Abstract

Noises can distort acquired geophysical data, leading to their misinterpretation. Potential noises sources include anthropogenic activity, natural phenomena, and instrument noises. Conventional denoising methods such as wavelet transform and filtering techniques, are based on subjective human investigation, which is computationally inefficient and time-consuming. Recently, many researchers attempted to implement neural networks to efficiently remove noise from geophysical data. This study aims to review and analyze different types of neural networks, such as artificial neural networks, convolutional neural networks, autoencoders, residual networks, and wavelet neural networks, which are implemented to remove different types of noises including seismic, transient electromagnetic, ground-penetrating radar, and magnetotelluric surveys. The review analyzes and summarizes the key challenges in the removal of noise from geophysical data using neural network, while proposes and explains solutions to the challenges. The analysis support that the advancement in neural networks can be powerful denoising tools for geophysical data.

지구물리탐사 자료의 잡음은 물리탐사 자료를 왜곡시켜 잘못된 결과 해석을 유도한다. 잡음을 만들어내는 원인으로는 인간의 활동으로 인하며 만들어지는 잡음과 자연 현상 및 기기 소음 등이 있으며 이러한 잡음을 제거하기 위한 다양한 연구들이 진행되고 있다. 하지만, 전통적인 잡음제거 방법들은 요소파 변환이나 필터링 과정에서 개인의 주관과 높은 계산 비용 그리고 많은 시간이 소모된다는 단점이 있으며 이런 문제를 해결하기 위해 영상 전처리 및 잡음제거를 위한 개선된 신경망을 구현하고자 하였다. 이 연구는 인공신경망, 합성곱 신경망, 오토인코더, 잔차 및 파형신경망의 다양한 유형의 신경망과 탄성파, 시간영역 전자탐사, 지표투과레이더 및 자기지전류의 잡음을 분석하고, 훈련 과정에 실제로 이용한 인공 신경망과 제시된 핵심 해결책을 분석 정리하였다. 이러한 분석을 통해 개선된 신경망이 지구물리탐사 자료의 잡음제거에 유용한 기법임을 알 수 있었다.

Keywords

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