Robust directional analysis of magnetic microscopy images using non-linear inversion and iterative Euler deconvolution

by Gelson F. Souza-Junior, Leonardo Uieda, Ricardo I. F. Trindade, Roger R. Fu, Ualisson D. Bellon, Yago M. Castro.

This repository contains the data and source code used to produce the results presented in:

Souza-Junior, G. F., Uieda, L., Trindade, R. I. F., Fu, R. R., Bellon, U. D., and Castro, Y. M. (2025). Robust directional analysis of magnetic microscopy images using non-linear inversion and iterative Euler deconvolution. EarthArXiv. doi:10.31223/X5N42F

	Info
Version of record	https://doi.org/JOURNAL_DOI
Open-access preprint on EarthArXiv	https://doi.org/10.31223/X5N42F
Archive of this repository	https://doi.org/10.5281/zenodo.15132658
Reproducing our results	REPRODUCING.md

Abstract

Scientists often study entire samples to understand their overall properties, but this approach can miss important details. To get a clearer picture, researchers are improving methods that focus on smaller regions of a sample. In paleomagnetism, a field that studies the Earth's ancient magnetic field, magnetic microscopy allows scientists to examine tiny areas with high precision. In this study, we use magnetic microscopy data to determine the direction of magnetization in samples. To do this, we apply a mathematical method called Euler deconvolution, which helps solve complex calculations and reduce uncertainty. We also refine our results with an additional step that improves accuracy and removes unwanted signals. We tested this approach on both simulated and real data. Our results show that this new method can detect weaker magnetic sources and accurately determine the direction of magnetization. When applied to real samples, it successfully identified their original magnetic direction. This represents an important step in using magnetic microscopy for paleomagnetic research.

License

All Python source code (including .py and .ipynb files) is made available under the MIT license. You can freely use and modify the code, without warranty, so long as you provide attribution to the authors. See LICENSE-MIT.txt for the full license text.

The manuscript text (including all LaTeX files), figures, and data/models produced as part of this research are available under the Creative Commons Attribution 4.0 License (CC-BY). See LICENSE-CC-BY.txt for the full license text.