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Journal Articles Fast TIMES Year : 2019

Akvo: a Surface NMR Workbench


Akvo is a software project for processing, modelling, and inversion of surface nuclear magnetic resonance (sNMR) data which is being released as a resource for the community. The signal processing, inversion, front-end, and Qt5-based graphical user interface code are written in Python 3 and released under the GNU v3.0 public license. Akvo seamlessly interfaces with Merlin, an open source (Mozilla Public License) sNMR modelling API written in C++-14 for high-performance back-end functionality. Precompiled binaries and Python wrappers for Merlin are provided through pip as part of the Akvo package, allowing for simplified installation on Linux, macOS, and Windows 10 platforms. The installer also provides a standalone executable entry point, meaning that users can bypass direct interaction with the Python interpreter, which can be helpful for newcomers to Python. Akvo is entirely open source. There are no external dependencies on proprietary runtime environments. The code is managed in a public git repository (https://akvo.lemmasoftware.org). Anyone can anonymously access, read, edit, and use the code. Akvo is flexible-Akvo processing, modelling, and inversion supports multiple channel data and arbitrary transmitter configuration. All processing steps are configurable so that they can be adapted to varying survey conditions. Akvo processing is reproducible and transparent. The processing workflow is documented along with the data in self-describing human readable YAML files. This encourages reproducible publications and reports. Akvo seeks community engagement. We are looking for new users and collaborators. This runs the gamut from code development, to testing and reporting of tickets for enhancements or bugs reports, and documentation.
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hal-02481575 , version 1 (23-10-2020)


  • HAL Id : hal-02481575 , version 1


Trevor Irons, Benjamin Bloss, Thomas Kremer, Andy Kass. Akvo: a Surface NMR Workbench. Fast TIMES, 2019, Near-Surface Geophysics Software, 24 (4), pp.101-115. ⟨hal-02481575⟩
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