Abstract Frequency domain electromagnetic induction (EMI) methods have had a long history of qualitative mapping for environmental applications. More recently, the development of multi-coil and multi-frequency instruments is such that the focus has shifted toward inverting data to obtain quantitative models of electrical conductivity. However, whilst the collection of EMI data is relatively straightforward, inverse modeling is more complicated. Furthermore, although several commercial and open-source inversion codes exist, there is still a need for user-friendly software that can bring EMI inversion to a non-specialist audience. Here the open-source EMagPy software is presented as an intuitive approach to modeling EMI data. It comprises a graphical user interface (GUI) and a Python application programming interface (API) that is more suitable for specialized tasks. EMagPy implements both cumulative sensitivity and Maxwell-based forward operators and can model data for 1D and quasi-2D/3D cases using either deterministic or probabilistic methods. The EMagPy GUI has a logical ‘tab-based’ layout to lead the user through data importing, data filtering, inversion, and plotting of raw and inverted data. Additionally, a dedicated forward modeling tab is presented that allows the generation of synthetic data. In this publication, necessary considerations, and background, of EMI theory are described before EMagPy's capabilities are presented through a series of synthetic and field-based case studies. Firstly, the performance of cumulative sensitivity and Maxwell-based forward models, and the influence of measurement noise are assessed for synthetic cases. Then the importance of data calibration for a riparian wetland dataset, the ability to include a priori information for a river-borne survey, and the potential for monitoring soil moisture in a time-lapse example are all investigated. It is anticipated that EMagPy offers a user-friendly tool suitable for novice and experienced practitioners alike, and its intuitive nature means it can provide a useful tool for teaching purposes.

中文翻译：

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EMagPy：用于电磁感应数据处理、正演建模和反演的开源独立软件

摘要 频域电磁感应 (EMI) 方法在环境应用的定性映射方面有着悠久的历史。最近，多线圈和多频率仪器的发展使得重点转向反演数据以获得电导率的定量模型。然而，虽然 EMI 数据的收集相对简单，但逆向建模更为复杂。此外，尽管存在多种商业和开源反演代码，但仍然需要能够将 EMI 反演带给非专业观众的用户友好软件。在这里，开源 EMagPy 软件是一种直观的 EMI 数据建模方法。它包括一个图形用户界面 (GUI) 和一个更适合专门任务的 Python 应用程序编程接口 (API)。EMagPy 实现了累积灵敏度和基于 Maxwell 的前向算子，并且可以使用确定性或概率方法为 1D 和准 2D/3D 案例的数据建模。EMagPy GUI 具有“基于选项卡”的逻辑布局，可引导用户完成数据导入、数据过滤、反演以及原始和反演数据的绘制。此外，还提供了一个专用的正向建模选项卡，允许生成合成数据。在本出版物中，在通过一系列综合和基于现场的案例研究介绍 EMagPy 的功能之前，描述了 EMI 理论的必要考虑因素和背景。首先，累积灵敏度和基于麦克斯韦的前向模型的性能以及测量噪声的影响在合成情况下进行评估。然后，对河岸湿地数据集的数据校准的重要性、包括河运调查的先验信息的能力以及在延时示例中监测土壤湿度的潜力都进行了调查。预计 EMagPy 提供了一个适合新手和有经验的从业者的用户友好工具，其直观性意味着它可以为教学目的提供有用的工具。以及在延时示例中监测土壤水分的潜力都得到了研究。预计 EMagPy 提供了一个适合新手和有经验的从业者的用户友好工具，其直观性意味着它可以为教学目的提供有用的工具。以及在延时示例中监测土壤水分的潜力都得到了研究。预计 EMagPy 提供了一个适合新手和有经验的从业者的用户友好工具，其直观性意味着它可以为教学目的提供有用的工具。