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2024 Vol.27, Issue 4 Preview Page

Research Article

Development of a Recurrent Neural Network Algorithm for the Attenuation of Surface-Related Multiples in Pohang Field Data Applications

포항 현장자료 적용을 위한 순환 신경망 기반 해수면 기인 다중반사파 억제 알고리즘 개발

Seoje Jeong1
,
Jiho Ha2
,
Hyeji Chae3
,
Sung-Ryul Shin4
,
Wookeen Chung4*
정 서제1
,
하 지호2
,
채 혜지3
,
신 성렬4
,
정 우근4*
1Department of Convergence Study on the Ocean Science and Technology, Ocean Science and Technology (OST) School, National Korea Maritime & Ocean University
2Pohang Branch, Korea Institute of Geoscience and Mineral Resources
3Department of Ocean Energy and Resources Engineering, National Korea Maritime & Ocean University
4Department of Energy and Resources Engineering, National Korea Maritime & Ocean University
1국립한국해양대학교 해양과학기술융합학과
2한국지질자원연구원 포항지질자원실증연구센터
3국립한국해양대학교 해양에너지자원공학과
4국립한국해양대학교 에너지자원공학과
*Corresponding Author
30 November 2024. pp. 211-223
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Abstract

In marine seismic exploration, surface-related multiples create artificial boundaries that hinder accurate geological interpretation, making their removal essential. Conventional multiple removal methods and seismic processing software vary in effectiveness based on physical properties and are sensitive to parameter settings. Thus, this study applied a stacked bidirectional long short-term memory (BiLSTM) model, known for its high performance in seismic noise suppression and signal classification, along with the fractional Fourier transform to attenuate surface-related multiples. The study also analyzed training results based on the range of rotation angles in the fractional Fourier transform (FrFT), and select the optimal angle using root mean squared error and peak signal-to-noise ratio. The optimized network was applied to field data from Pohang, and its performance was compared with seismic processing software, confirming its effectiveness and practical applicability for field data.

Keywords
Deep-Learning
Recurrent Neural Network
Surface-Related Multiple
Fractional Fourier Transform

해양 탄성파 탐사에서 해수면 기인 다중반사파는 실제 지층에 없는 경계면을 생성하여 지질학적 해석을 방해하므로 제거가 필수적이다. 기존의 다중반사파 제거 기법과 탄성파탐사 자료처리 소프트웨어를 적용하는 경우, 물리적 특성에 기반하여 탐사환경에 따라 적용 효과가 달라지거나 자료처리 변수에 민감한 영향을 받는다는 한계점이 있다. 본 연구에서는 잡음 억제 및 신호 분류에서 우수한 성능을 보이는 순환 신경망 기반의 stacked BiLSTM 네트워크와 분수 푸리에 변환을 적용하여 해수면 기인 다중반사파를 억제하였다. 또한, 분수 푸리에 변환의 회전 각도 범위에 따른 학습 결과를 분석하고, 평균 제곱근 오차와 최대 신호 대 잡음비를 통해 최적의 회전 각도를 설정하였다. 최적화된 네트워크를 포항 현장자료에 적용하여 탄성파탐사 자료처리 소프트웨어 결과와 비교한 결과, 현장자료 적용 가능성을 검증하였다.

키워드
딥러닝
순환 신경망
해수면 기인 다중반사파
분수 푸리에 변환
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Information
  • Publisher :Korean Society of Earth and Exploration Geophysicists
  • Publisher(Ko) :한국지구물리물리탐사학회
  • Journal Title :Geophysics and Geophysical Exploration
  • Journal Title(Ko) :지구물리와 물리탐사
  • Volume : 27
  • No :4
  • Pages :211-223
  • Received Date : 2024-11-11
  • Revised Date : 2024-11-19
  • Accepted Date : 2024-11-26
  • DOI :https://doi.org/10.7582/GGE.2024.27.4.211
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