Electromagnetic Subsurface Imaging in the Presence of Metallic Structures: A Review of Numerical Strategies
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| Izdano u: | Surveys in Geophysics vol. 45, no. 5 (Oct 2024), p. 1627 |
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| Glavni autor: | |
| Daljnji autori: | , , , |
| Izdano: |
Springer Nature B.V.
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| Teme: | |
| Online pristup: | Citation/Abstract Full Text - PDF |
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| 045 | 2 | |b d20241001 |b d20241031 | |
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| 100 | 1 | |a Castillo-Reyes, Octavio |u Universitat Politècnica de Catalunya - BarcelonaTech (UPC), Department of Computer Architecture, Barcelona, Spain (GRID:grid.6835.8) (ISNI:0000 0004 1937 028X); Barcelona Supercomputing Center (BSC), Barcelona, Spain (GRID:grid.10097.3f) (ISNI:0000 0004 0387 1602) | |
| 245 | 1 | |a Electromagnetic Subsurface Imaging in the Presence of Metallic Structures: A Review of Numerical Strategies | |
| 260 | |b Springer Nature B.V. |c Oct 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Electromagnetic (EM) imaging aims to produce large-scale, high-resolution soil conductivity maps that provide essential information for Earth subsurface exploration. To rigorously generate EM subsurface models, one must address both the forward problem and the inverse problem. From these subsurface resistivity maps, also referred to as volumes of resistivity distribution, it is possible to extract useful information (lithology, temperature, porosity, permeability, among others) to improve our knowledge about geo-resources on which modern society depends (e.g., energy, groundwater, and raw materials, among others). However, this ability to detect electrical resistivity contrasts also makes EM imaging techniques sensitive to metallic structures whose EM footprint often exceeds their diminutive stature compared to surrounding materials. Depending on target applications, this behavior can be advantageous or disadvantageous. In this work, we review EM modeling and inverse solutions in the presence of metallic structures, emphasizing how these structures affect EM data acquisition and interpretation. By addressing the challenges posed by metallic structures, our aim is to enhance the accuracy and reliability of subsurface EM characterization, ultimately leading to improved management of geo-resources and environmental monitoring. Here, we consider the latter through the lens of a triple helix approach: physics behind metallic structures in EM modeling and imaging, development of computational tools (conventional strategies and artificial intelligence schemes), and configurations and applications. The literature review shows that, despite recent scientific advancements, EM imaging techniques are still being developed, as are software-based data processing and interpretation tools. Such progress must address geological complexities and metallic casing measurements integrity in increasing detail setups. We hope this review will provide inspiration for researchers to study the fascinating EM problem, as well as establishing a robust technological ecosystem to those interested in studying EM fields affected by metallic artifacts. | |
| 653 | |a Imaging techniques | ||
| 653 | |a Data acquisition | ||
| 653 | |a Environmental monitoring | ||
| 653 | |a Lithology | ||
| 653 | |a Electrical resistivity | ||
| 653 | |a Soil temperature | ||
| 653 | |a Raw materials | ||
| 653 | |a Forward problem | ||
| 653 | |a Soil conductivity | ||
| 653 | |a Environmental management | ||
| 653 | |a Groundwater | ||
| 653 | |a Inverse problems | ||
| 653 | |a Soil permeability | ||
| 653 | |a Target detection | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Permeability | ||
| 653 | |a Information processing | ||
| 653 | |a Software | ||
| 653 | |a Data processing | ||
| 653 | |a Image resolution | ||
| 653 | |a Porosity | ||
| 653 | |a Data analysis | ||
| 653 | |a Physics | ||
| 653 | |a Literature reviews | ||
| 653 | |a Computer program integrity | ||
| 653 | |a Modelling | ||
| 653 | |a Soil porosity | ||
| 653 | |a Structural reliability | ||
| 653 | |a Structures | ||
| 653 | |a Geophysics | ||
| 653 | |a Algorithms | ||
| 653 | |a Numerical analysis | ||
| 653 | |a Geology | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Queralt, Pilar |u Universitat de Barcelona (UB), Departament de Dinàmica de la Terra i de l’Oceà, Institut Geomodels, Barcelona, Spain (GRID:grid.5841.8) (ISNI:0000 0004 1937 0247) | |
| 700 | 1 | |a Piñas-Varas, Perla |u Universitat de Barcelona (UB), Departament de Dinàmica de la Terra i de l’Oceà, Institut Geomodels, Barcelona, Spain (GRID:grid.5841.8) (ISNI:0000 0004 1937 0247) | |
| 700 | 1 | |a Ledo, Juanjo |u Complutense University of Madrid (UCM), Department of Physics of the Earth and Astrophysics, Madrid, Spain (GRID:grid.4795.f) (ISNI:0000 0001 2157 7667) | |
| 700 | 1 | |a Rojas, Otilio |u Barcelona Supercomputing Center (BSC), Barcelona, Spain (GRID:grid.10097.3f) (ISNI:0000 0004 0387 1602) | |
| 773 | 0 | |t Surveys in Geophysics |g vol. 45, no. 5 (Oct 2024), p. 1627 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3122865859/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3122865859/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |