Deep Learning in Geophysics: Current Status,  Challenges,  and Future Directions

Jiyun Yu; Jongchan Oh; Shinhye Kong; Changhoon Lee; Jiwon Lim; Daeung Yoon

doi:10.32390/ksmer.2024.61.1.061

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2024 Vol.61, Issue 1 Preview Page

General Remarks

Deep Learning in Geophysics: Current Status, Challenges, and Future Directions 물리탐사 분야에서의 딥러닝 기술: 현황, 도전 과제 및 미래 방향

28 February 2024. pp. 61-81

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Abstract

With the rapid development of artificial intelligence technology, machine learning and deep learning techniques have been actively applied in the field of exploration geophysics. In this study, we extensively investigated the application of deep learning in geophysical techniques including seismic, gravity, magnetic, electromagnetics, ground penetrating radar, and joint inversion. Recent research has confirmed that deep learning techniques optimized for geophysical problems are being developed that transcend traditional theory-based approaches, suggesting that deep learning is becoming a new paradigm in the field of geophysics. Despite these technological advances, there are still challenges to be overcome, such as a lack of training data, model opacity, and heterogeneity between training data and field data, and this study suggests future research directions to address these aspects.

Keywords

exploration geophysics

machine learning

deep learning

research trends

인공지능 기술의 급속한 발전과 함께, 물리탐사 분야 또한 기계학습 및 딥러닝 기술이 활발히 적용되고 있다. 이 논문에서는 탄성파, 중력, 자력, 전기 및 전자, 지표투과 레이다 탐사 그리고 복합역산을 포함한 여러 물리탐사 기법에서 딥러닝의 적용 사례를 광범위하게 조사하였다. 이를 통해, 최근 연구들에서 전통적인 이론 기반 접근법을 뛰어넘는 물리탐사 분야에 최적화된 딥러닝 기술이 개발되고 있음을 확인하였고, 이는 딥러닝이 물리탐사 분야에서 새로운 패러다임으로 자리 잡고 있음을 시사한다. 하지만, 이러한 기술적 진보에도 불구하고 학습자료의 부족, 모델의 불투명성, 학습 자료와 현장자료 간의 이질성 등 여전히 극복해야 할 도전 과제가 존재하고, 이 논문에서는 이를 해결하기 위한 앞으로의 연구 방향을 제시하였다.

키워드

물리탐사

기계학습

딥러닝

연구 동향

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Information

Publisher :The Korean Society of Mineral and Energy Resources Engineers
Publisher(Ko) :한국자원공학회
Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
Journal Title(Ko) :한국자원공학회지
Volume : 61
No :1
Pages :61-81
Received Date : 2024-02-13
Revised Date : 2024-02-23
Accepted Date : 2024-02-27
DOI :https://doi.org/10.32390/ksmer.2024.61.1.061

Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

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