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


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.


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