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.

电磁 (EM) 成像旨在生成大规模、高分辨率的土壤电导率图，为地球地下勘探提供重要信息。为了严格生成电磁地下模型，必须同时解决正问题和逆问题。从这些地下电阻率图（也称为电阻率分布体）中，可以提取有用的信息（岩性、温度、孔隙度、渗透率等），以提高我们对现代社会所依赖的地质资源的了解（例如，能源、地下水和原材料等）。然而，这种检测电阻率对比的能力也使得电磁成像技术对金属结构敏感，与周围材料相比，金属结构的电磁足迹通常超过其微小的尺寸。根据目标应用程序，这种行为可能是有利的，也可能是不利的。在这项工作中，我们回顾了存在金属结构的情况下的电磁建模和逆解，强调这些结构如何影响电磁数据采集和解释。通过解决金属结构带来的挑战，我们的目标是提高地下电磁表征的准确性和可靠性，最终改善地质资源和环境监测的管理。在这里，我们通过三螺旋方法的视角来考虑后者：电磁建模和成像中金属结构背后的物理原理、计算工具的开发（传统策略和人工智能方案）以及配置和应用。 文献综述表明，尽管最近科学取得了进步，但电磁成像技术以及基于软件的数据处理和解释工具仍在开发中。这种进步必须在增加细节设置中解决地质复杂性和金属套管测量的完整性。我们希望这篇综述能为研究人员研究令人着迷的电磁问题提供灵感，并为那些对研究受金属制品影响的电磁场感兴趣的人建立一个强大的技术生态系统。