five

Supplementary material for "Robust directional analysis of magnetic microscopy images using non-linear inversion anditerative Euler deconvolution"

收藏
DataCite Commons2026-05-04 更新2026-05-07 收录
下载链接:
https://zenodo.org/doi/10.5281/zenodo.15132657
下载链接
链接失效反馈
官方服务:
资源简介:
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., & Castro, Y. M. (2026). Robust Directional Analysis of Magnetic Microscopy Images Using Non‐Linear Inversion and Iterative Euler Deconvolution. Journal of Geophysical Research: Solid Earth, 131(4). doi:10.1029/2025jb031725.   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.
提供机构:
Zenodo
创建时间:
2025-04-03
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作