EMagPy: Open-source standalone software for processing, forward modeling and inversion of electromagnetic induction data

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.