Reconstruction of Induced Polarization Information from Electromagnetic Data Using Neural Network Approach
Electromagnetic responses arising from the interaction of electromagnetic fields with the electrical properties of the subsurface structures provide useful information about such structures. Furthermore, if polarizable materials are present within the investigated structure, the observed data from t...
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| Format: | Article |
| Language: | English |
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The Korean Institute of Electromagnetic Engineering and Science
2025-07-01
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| Series: | Journal of Electromagnetic Engineering and Science |
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| Online Access: | https://www.jees.kr/upload/pdf/jees-2025-4-r-301.pdf |
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| _version_ | 1849393189761318912 |
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| author | Mohamed Elkattan |
| author_facet | Mohamed Elkattan |
| author_sort | Mohamed Elkattan |
| collection | DOAJ |
| description | Electromagnetic responses arising from the interaction of electromagnetic fields with the electrical properties of the subsurface structures provide useful information about such structures. Furthermore, if polarizable materials are present within the investigated structure, the observed data from the electromagnetic measurements will contain inherently induced polarization responses, thereby introducing additional parameters for distinguishing subsurface materials. In this regard, the concept of introducing an inversion framework to extract information about induced polarization characteristics from electromagnetic measurements has recently attracted considerable attention. In this paper, an inversion setting is presented to estimate the induced polarization parameters of a stratified, polarizable, and layered medium from scattered electromagnetic fields. The proposed setting handles the inverse problem through a learning procedure that employs a neural network design. Several neural network design factors were tuned to achieve optimal performance. The proposed neural network with tuned design factors was also evaluated under noisy conditions. Error analysis verified the effectiveness of the proposed neural network design in inverting electromagnetic data to derive induced polarization parameters. |
| format | Article |
| id | doaj-art-973fc49f413e4868a2cf9d48cdf2383a |
| institution | Kabale University |
| issn | 2671-7255 2671-7263 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | The Korean Institute of Electromagnetic Engineering and Science |
| record_format | Article |
| series | Journal of Electromagnetic Engineering and Science |
| spelling | doaj-art-973fc49f413e4868a2cf9d48cdf2383a2025-08-20T03:40:30ZengThe Korean Institute of Electromagnetic Engineering and ScienceJournal of Electromagnetic Engineering and Science2671-72552671-72632025-07-0125430731710.26866/jees.2025.4.r.3013749Reconstruction of Induced Polarization Information from Electromagnetic Data Using Neural Network ApproachMohamed ElkattanElectromagnetic responses arising from the interaction of electromagnetic fields with the electrical properties of the subsurface structures provide useful information about such structures. Furthermore, if polarizable materials are present within the investigated structure, the observed data from the electromagnetic measurements will contain inherently induced polarization responses, thereby introducing additional parameters for distinguishing subsurface materials. In this regard, the concept of introducing an inversion framework to extract information about induced polarization characteristics from electromagnetic measurements has recently attracted considerable attention. In this paper, an inversion setting is presented to estimate the induced polarization parameters of a stratified, polarizable, and layered medium from scattered electromagnetic fields. The proposed setting handles the inverse problem through a learning procedure that employs a neural network design. Several neural network design factors were tuned to achieve optimal performance. The proposed neural network with tuned design factors was also evaluated under noisy conditions. Error analysis verified the effectiveness of the proposed neural network design in inverting electromagnetic data to derive induced polarization parameters.https://www.jees.kr/upload/pdf/jees-2025-4-r-301.pdfelectromagneticinduced polarizationinversionneural network |
| spellingShingle | Mohamed Elkattan Reconstruction of Induced Polarization Information from Electromagnetic Data Using Neural Network Approach Journal of Electromagnetic Engineering and Science electromagnetic induced polarization inversion neural network |
| title | Reconstruction of Induced Polarization Information from Electromagnetic Data Using Neural Network Approach |
| title_full | Reconstruction of Induced Polarization Information from Electromagnetic Data Using Neural Network Approach |
| title_fullStr | Reconstruction of Induced Polarization Information from Electromagnetic Data Using Neural Network Approach |
| title_full_unstemmed | Reconstruction of Induced Polarization Information from Electromagnetic Data Using Neural Network Approach |
| title_short | Reconstruction of Induced Polarization Information from Electromagnetic Data Using Neural Network Approach |
| title_sort | reconstruction of induced polarization information from electromagnetic data using neural network approach |
| topic | electromagnetic induced polarization inversion neural network |
| url | https://www.jees.kr/upload/pdf/jees-2025-4-r-301.pdf |
| work_keys_str_mv | AT mohamedelkattan reconstructionofinducedpolarizationinformationfromelectromagneticdatausingneuralnetworkapproach |