Deep-TEMNet: A Hybrid U-Net–2D LSTM Network for Efficient and Accurate 2.5D Transient Electromagnetic Forward Modeling
Αποθηκεύτηκε σε:
| Εκδόθηκε σε: | Remote Sensing vol. 17, no. 2 (2025), p. 264 |
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| Κύριος συγγραφέας: | |
| Άλλοι συγγραφείς: | , , |
| Έκδοση: |
MDPI AG
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| Διαθέσιμο Online: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17020264 |2 doi | |
| 035 | |a 3159535658 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Qu, Zhijie |u Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; <email>quzhijie20@mails.ucas.ac.cn</email> (Z.Q.); <email>gaoyuan21@mails.ucas.ac.cn</email> (Y.G.); Key Laboratory of Electromagnetic Radiation and Sensing Technology, Chinese Academy of Sciences, Beijing 100190, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China | |
| 245 | 1 | |a Deep-TEMNet: A Hybrid U-Net–2D LSTM Network for Efficient and Accurate 2.5D Transient Electromagnetic Forward Modeling | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The transient electromagnetic (TEM) method is a crucial tool for subsurface exploration, providing essential insights into the electrical resistivity structures beneath the Earth’s surface. Traditional forward modeling approaches, such as the finite-difference time-domain (FDTD) method and the finite-element method (FEM), are computationally intensive, limiting their practicality for real-time, high-resolution, or large-scale investigations. To address these challenges, we present Deep-TEMNet, an advanced deep learning framework specifically designed for two-dimensional TEM forward modeling. Deep-TEMNet integrates the U-Net architecture with a tailored two-dimensional long short-term memory (2D LSTM) module, allowing it to effectively capture complex spatial-temporal relationships in TEM data. The U-Net component enables high-resolution spatial feature extraction, while the 2D LSTM module enhances temporal modeling by processing spatial sequences in two dimensions, thereby optimizing the representation of electromagnetic field dynamics over time. Trained on high-fidelity FEM-generated datasets, Deep-TEMNet achieves exceptional accuracy in reproducing electromagnetic field distributions across diverse geological scenarios, with a mean squared error of 0.00000134 and a root mean square percentage error of 0.002373019. The framework offers over 150 times the computational speed of traditional FEMs, with an average inference time of just 3.26 s. Extensive validation across varied geological conditions highlights Deep-TEMNet’s robustness and adaptability, establishing its potential for efficient, large-scale subsurface mapping and real-time data processing. By combining U-Net’s spatial resolution capabilities with the sequential processing strength of the 2D LSTM module, Deep-TEMNet significantly advances computational efficiency and accuracy, positioning it as a valuable tool for geophysical exploration, environmental monitoring, and other applications requiring scalable, real-time TEM analyses that are easily integrated into remote sensing workflows. | |
| 653 | |a Finite element method | ||
| 653 | |a Environmental monitoring | ||
| 653 | |a Geophysical exploration | ||
| 653 | |a Accuracy | ||
| 653 | |a Geological mapping | ||
| 653 | |a Data processing | ||
| 653 | |a Deep learning | ||
| 653 | |a Datasets | ||
| 653 | |a Modelling | ||
| 653 | |a Magnetic fields | ||
| 653 | |a Finite difference time domain method | ||
| 653 | |a Electromagnetic fields | ||
| 653 | |a Remote sensing | ||
| 653 | |a Spatial discrimination | ||
| 653 | |a Computer applications | ||
| 653 | |a Modules | ||
| 653 | |a Electrical resistivity | ||
| 653 | |a Long short-term memory | ||
| 653 | |a Python | ||
| 653 | |a Adaptability | ||
| 653 | |a Efficiency | ||
| 653 | |a Geology | ||
| 653 | |a Machine learning | ||
| 653 | |a Simulation | ||
| 653 | |a Exploration | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Fourier transforms | ||
| 653 | |a Spatial memory | ||
| 653 | |a Spatial resolution | ||
| 653 | |a Neural networks | ||
| 653 | |a High resolution | ||
| 653 | |a Methods | ||
| 653 | |a Finite element analysis | ||
| 653 | |a Information processing | ||
| 653 | |a Algorithms | ||
| 653 | |a Real time | ||
| 653 | |a Subsurface mapping | ||
| 653 | |a Software | ||
| 700 | 1 | |a Gao, Yuan |u Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; <email>quzhijie20@mails.ucas.ac.cn</email> (Z.Q.); <email>gaoyuan21@mails.ucas.ac.cn</email> (Y.G.); Key Laboratory of Electromagnetic Radiation and Sensing Technology, Chinese Academy of Sciences, Beijing 100190, China; School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China | |
| 700 | 1 | |a Kang, Xing |u State Key Laboratory of Space-Earth Integrated Information Technology, Beijing 100095, China; <email>xingkang19@mails.ucas.ac.cn</email>; Beijing Institute of Satellite Information Engineering, Beijing 100095, China | |
| 700 | 1 | |a Zhang, Xiaojuan |u Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China; <email>quzhijie20@mails.ucas.ac.cn</email> (Z.Q.); <email>gaoyuan21@mails.ucas.ac.cn</email> (Y.G.); Key Laboratory of Electromagnetic Radiation and Sensing Technology, Chinese Academy of Sciences, Beijing 100190, China | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 2 (2025), p. 264 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159535658/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3159535658/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159535658/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |