Abstract
Micromagnetic tomography (MMT) is an emerging technique in rock and paleomagnetism to determine individual magnetic moments of tomographically defined magnetic source regions within a natural sample by means of surface scans of the magnetic field above the sample. MMT relies on combining large high-resolution data sets from X-ray tomography and magnetic scanning devices, like quantum diamond magnetometers, together with advanced inversion algorithms potentially capable to solve for millions of individual magnetic moment vectors. We here provide an overview of existing algorithms that have been developed to tackle different aspects of MMT-related problems and discuss recent advances and future challenges of MMT.
| Original language | English |
|---|---|
| Article number | 105555 |
| Number of pages | 14 |
| Journal | Computers & Geosciences |
| Volume | 185 |
| DOIs | |
| Publication status | Published - Mar 2024 |
Bibliographical note
Publisher Copyright:© 2024 The Authors
Funding
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 851460 to LVdG). D.C.-O. acknowledges the support by DGIIE (UTFSM ) through the Postdoctoral initiative. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 851460 to LVdG). D.C.-O. acknowledges the support by DGIIE (UTFSM ) through the Postdoctoral initiative. All the codes are publicly available and hosted in the Github repository at the Micromagnetic Tomography organization website https://github.com/Micromagnetic-Tomography. Every code has a Zenodo DOI that can be cited according to the library version. The codes are written in the Python, Cython, C, and CUDA programming languages. The codes utilize robust Python numerical libraries which include Numpy (Harris et al. 2020), Scipy (Virtanen et al. 2020), Pyvista (Sullivan and Kaszynski, 2019), Meshio (Schlömer, 2021), matplotlib (Hunter, 2007), Jupyter notebooks (Kluyver et al. 2016), Cython (Behnel et al. 2011) and Numba (Lam et al. 2015). All the codes and results from this study are published in the public GitHub repository (Cortés-Ortuño et al. 2022d).
| Funders | Funder number |
|---|---|
| CUDA | 2020 |
| DGIIE | |
| Universidad Técnica Federico Santa María | |
| Horizon 2020 Framework Programme | |
| European Research Council | |
| Horizon 2020 | 851460 |
Keywords
- Inversion theory
- Magnetic potential
- Micromagnetic Tomography
- Rock magnetism