Electromagnetic imaging and deep learning for transition to renewable energies: a technology review
Electromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Earth Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2023.1159910/full |
| _version_ | 1797741497211158528 |
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| author | Octavio Castillo-Reyes Octavio Castillo-Reyes Xiangping Hu Bochen Wang Bochen Wang Bochen Wang Yanyi Wang Yanyi Wang Yanyi Wang Zhenwei Guo Zhenwei Guo Zhenwei Guo |
| author_facet | Octavio Castillo-Reyes Octavio Castillo-Reyes Xiangping Hu Bochen Wang Bochen Wang Bochen Wang Yanyi Wang Yanyi Wang Yanyi Wang Zhenwei Guo Zhenwei Guo Zhenwei Guo |
| author_sort | Octavio Castillo-Reyes |
| collection | DOAJ |
| description | Electromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization of geothermal energy reservoirs, capture and storage of carbon dioxide, water prospecting, and monitoring of hazardous-waste deposits). The development of new numerical schemes, algorithms, and easy access to supercomputers have supported innovation throughout the geo-electromagnetic community. In particular, deep learning solutions have taken electromagnetic imaging technology to a different level. These emerging deep learning tools have significantly contributed to data processing for enhanced electromagnetic imaging of the Earth. Herein, we review innovative electromagnetic imaging technologies and deep learning solutions and their role in better understanding useful resources for the energy transition path. To better understand this landscape, we describe the physics behind electromagnetic imaging, current trends in its numerical modeling, development of computational tools (traditional approaches and emerging deep learning schemes), and discuss some key applications for the energy transition. We focus on the need to explore all the alternatives of technologies and expertise transfer to propel the energy landscape forward. We hope this review may be useful for the entire geo-electromagnetic community and inspire and drive the further development of innovative electromagnetic imaging technologies to power a safer future based on energy sources. |
| first_indexed | 2024-03-12T14:27:18Z |
| format | Article |
| id | doaj.art-5f1defb646e449d19677d46fd5820fd2 |
| institution | Directory Open Access Journal |
| issn | 2296-6463 |
| language | English |
| last_indexed | 2024-03-12T14:27:18Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj.art-5f1defb646e449d19677d46fd5820fd22023-08-18T00:33:55ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632023-08-011110.3389/feart.2023.11599101159910Electromagnetic imaging and deep learning for transition to renewable energies: a technology reviewOctavio Castillo-Reyes0Octavio Castillo-Reyes1Xiangping Hu2Bochen Wang3Bochen Wang4Bochen Wang5Yanyi Wang6Yanyi Wang7Yanyi Wang8Zhenwei Guo9Zhenwei Guo10Zhenwei Guo11Barcelona Supercomputing Center, Barcelona, SpainDepartment of Computer Architecture, Universitat Politècnica de Catalunya, Barcelona, SpainDepartment of Energy and Process Engineering, Faculty of Engineering, Norwegian University of Science and Technology, Trondheim, Sør-Trøndelag, NorwaySchool of Geosciences and Info Physics, Central South University, Changsha, Hunan Province, ChinaHunan Key Laboratory of Nonferrous Resources and Geological Hazard Exploration, Central South University, Changsha, Anhui Province, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Changsha, Hunan Province, ChinaSchool of Geosciences and Info Physics, Central South University, Changsha, Hunan Province, ChinaHunan Key Laboratory of Nonferrous Resources and Geological Hazard Exploration, Central South University, Changsha, Anhui Province, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Changsha, Hunan Province, ChinaSchool of Geosciences and Info Physics, Central South University, Changsha, Hunan Province, ChinaHunan Key Laboratory of Nonferrous Resources and Geological Hazard Exploration, Central South University, Changsha, Anhui Province, ChinaKey Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Changsha, Hunan Province, ChinaElectromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization of geothermal energy reservoirs, capture and storage of carbon dioxide, water prospecting, and monitoring of hazardous-waste deposits). The development of new numerical schemes, algorithms, and easy access to supercomputers have supported innovation throughout the geo-electromagnetic community. In particular, deep learning solutions have taken electromagnetic imaging technology to a different level. These emerging deep learning tools have significantly contributed to data processing for enhanced electromagnetic imaging of the Earth. Herein, we review innovative electromagnetic imaging technologies and deep learning solutions and their role in better understanding useful resources for the energy transition path. To better understand this landscape, we describe the physics behind electromagnetic imaging, current trends in its numerical modeling, development of computational tools (traditional approaches and emerging deep learning schemes), and discuss some key applications for the energy transition. We focus on the need to explore all the alternatives of technologies and expertise transfer to propel the energy landscape forward. We hope this review may be useful for the entire geo-electromagnetic community and inspire and drive the further development of innovative electromagnetic imaging technologies to power a safer future based on energy sources.https://www.frontiersin.org/articles/10.3389/feart.2023.1159910/fullelectromagnetic imagingnumerical modelinghigh-performance computingdeep learningclean energiesexpertise transfer |
| spellingShingle | Octavio Castillo-Reyes Octavio Castillo-Reyes Xiangping Hu Bochen Wang Bochen Wang Bochen Wang Yanyi Wang Yanyi Wang Yanyi Wang Zhenwei Guo Zhenwei Guo Zhenwei Guo Electromagnetic imaging and deep learning for transition to renewable energies: a technology review Frontiers in Earth Science electromagnetic imaging numerical modeling high-performance computing deep learning clean energies expertise transfer |
| title | Electromagnetic imaging and deep learning for transition to renewable energies: a technology review |
| title_full | Electromagnetic imaging and deep learning for transition to renewable energies: a technology review |
| title_fullStr | Electromagnetic imaging and deep learning for transition to renewable energies: a technology review |
| title_full_unstemmed | Electromagnetic imaging and deep learning for transition to renewable energies: a technology review |
| title_short | Electromagnetic imaging and deep learning for transition to renewable energies: a technology review |
| title_sort | electromagnetic imaging and deep learning for transition to renewable energies a technology review |
| topic | electromagnetic imaging numerical modeling high-performance computing deep learning clean energies expertise transfer |
| url | https://www.frontiersin.org/articles/10.3389/feart.2023.1159910/full |
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