An Overview Study of Deep Learning in Geophysics: Cross-Cutting Research to Advance Geoscience
In recent years, Artificial Intelligence technology (AI) has driven rapid advances across various sciences. As a new data-driven technology, Deep Learning (DL) is widely utilized for data processing and adaptive tasks in multiple fields due to its high automation, accuracy, and scalability. DL has g...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11072451/ |
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| author | Zhao Wenxue Dai Shikun Tian Hongjun Zhu Dexiang Zhang Ying Jiang Fan |
| author_facet | Zhao Wenxue Dai Shikun Tian Hongjun Zhu Dexiang Zhang Ying Jiang Fan |
| author_sort | Zhao Wenxue |
| collection | DOAJ |
| description | In recent years, Artificial Intelligence technology (AI) has driven rapid advances across various sciences. As a new data-driven technology, Deep Learning (DL) is widely utilized for data processing and adaptive tasks in multiple fields due to its high automation, accuracy, and scalability. DL has garnered widespread attention and developed rapidly in geophysics. DL provides a new power for geophysical exploration and is becoming an essential tool for geophysical data processing, modeling, and analysis. With the proposal and effective application of various new deep learning-based technologies and methods for geophysical data processing, the efficiency and accuracy of geophysical exploration have been significantly improved. This advancement is accelerating the rapid development of geophysics toward intelligent interpretation. This paper reviews the latest research and application status of DL in geophysics, including seismic exploration, electrical prospecting, earthquake science, remote sensing, and other fields. Through systematic analysis of recent literature, it summarizes mainstream technical approaches of DL for addressing diverse geophysical challenges, and the limitations of these technologies in specific application scenarios are discussed. In addition, this paper analyses and prospects for research trends of DL in geophysics. This paper serves as a relevant reference for hobbyists and researchers to understand the latest advances, unresolved issues, and future trends in related fields. |
| format | Article |
| id | doaj-art-4b3aee544f5b40a6a7ff5bb8d1b05659 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-4b3aee544f5b40a6a7ff5bb8d1b056592025-08-20T03:55:48ZengIEEEIEEE Access2169-35362025-01-011312436412438810.1109/ACCESS.2025.358669311072451An Overview Study of Deep Learning in Geophysics: Cross-Cutting Research to Advance GeoscienceZhao Wenxue0Dai Shikun1https://orcid.org/0009-0005-5267-0078Tian Hongjun2Zhu Dexiang3Zhang Ying4Jiang Fan5Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education, Central South University, Changsha, ChinaIn recent years, Artificial Intelligence technology (AI) has driven rapid advances across various sciences. As a new data-driven technology, Deep Learning (DL) is widely utilized for data processing and adaptive tasks in multiple fields due to its high automation, accuracy, and scalability. DL has garnered widespread attention and developed rapidly in geophysics. DL provides a new power for geophysical exploration and is becoming an essential tool for geophysical data processing, modeling, and analysis. With the proposal and effective application of various new deep learning-based technologies and methods for geophysical data processing, the efficiency and accuracy of geophysical exploration have been significantly improved. This advancement is accelerating the rapid development of geophysics toward intelligent interpretation. This paper reviews the latest research and application status of DL in geophysics, including seismic exploration, electrical prospecting, earthquake science, remote sensing, and other fields. Through systematic analysis of recent literature, it summarizes mainstream technical approaches of DL for addressing diverse geophysical challenges, and the limitations of these technologies in specific application scenarios are discussed. In addition, this paper analyses and prospects for research trends of DL in geophysics. This paper serves as a relevant reference for hobbyists and researchers to understand the latest advances, unresolved issues, and future trends in related fields.https://ieeexplore.ieee.org/document/11072451/Deep learninggeophysicsseismic explorationearthquake scienceremote sensing |
| spellingShingle | Zhao Wenxue Dai Shikun Tian Hongjun Zhu Dexiang Zhang Ying Jiang Fan An Overview Study of Deep Learning in Geophysics: Cross-Cutting Research to Advance Geoscience IEEE Access Deep learning geophysics seismic exploration earthquake science remote sensing |
| title | An Overview Study of Deep Learning in Geophysics: Cross-Cutting Research to Advance Geoscience |
| title_full | An Overview Study of Deep Learning in Geophysics: Cross-Cutting Research to Advance Geoscience |
| title_fullStr | An Overview Study of Deep Learning in Geophysics: Cross-Cutting Research to Advance Geoscience |
| title_full_unstemmed | An Overview Study of Deep Learning in Geophysics: Cross-Cutting Research to Advance Geoscience |
| title_short | An Overview Study of Deep Learning in Geophysics: Cross-Cutting Research to Advance Geoscience |
| title_sort | overview study of deep learning in geophysics cross cutting research to advance geoscience |
| topic | Deep learning geophysics seismic exploration earthquake science remote sensing |
| url | https://ieeexplore.ieee.org/document/11072451/ |
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