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    Rights statement: This is the author’s version of a work that was accepted for publication in Computers & Geosciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Geosciences, ??, ??, 2020 DOI: 10.1016/j.cageo.2020.104561

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    Embargo ends: 2/08/21

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

Research output: Contribution to journalJournal article

E-pub ahead of print
<mark>Journal publication date</mark>2/08/2020
<mark>Journal</mark>Computers and Geosciences
Publication statusE-pub ahead of print
Early online date2/08/20
Original languageEnglish

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 collection of EMI data is relatively straightforward, the inverse modeling is more complicated. Furthermore, although several commercial and open-source inversion codes, exist, there is still a need for a user-friendly software that can bring EMI inversion to non-specialist audience. Here the open-source EMagPy software is presented as an intuitive approach to modeling EMI data. It comprises a graphical user (GUI) interface and a Python application programming interface (API). 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. In addition, a dedicated forward modeling tab is presented to generate 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 mean it can provide a useful tool for teaching purposes.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Computers & Geosciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Geosciences, ??, ??, 2020 DOI: 10.1016/j.cageo.2020.104561