Micromagnetic tomography: Numerical libraries

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-...

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Veröffentlicht in:Computers & geosciences Jg. 185; S. 105555
Hauptverfasser: Cortés-Ortuño, David, Out, Frenk, Kosters, Martha E., Fabian, Karl, de Groot, Lennart V.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Ltd 01.03.2024
Schlagworte:
algorithms
computed tomography
computers
data collection
diamonds
Inversion theory
magnetic fields
Magnetic potential
Micromagnetic Tomography
Rock magnetism
rocks
sampling
surfaces
Inversion theory
Rock magnetism
Magnetic potential
Micromagnetic Tomography
ISSN:0098-3004
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Zusammenfassung: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. •Micromagnetic Tomography MMT computes paleomagnetic data from rock magnetic grains.•Forward modeling and numerical inversions of MMT are coded in open Python libraries.•MMT can infer the internal state from single grains using micromagnetic modeling.•MMT codes are optimized for high performance computing using both CPUs and GPUs.•Proposed benchmark problems using experimental data support MMT results.
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ISSN:0098-3004
DOI:10.1016/j.cageo.2024.105555