MagNet: Automated Magnetic Mineral Grain Morphometry Using Convolutional Neural Network
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Publication Date
2022Journal Title
Geophysical Research Letters
ISSN
0094-8276
Publisher
American Geophysical Union (AGU)
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Pei, Z., Chang, L., Xue, P., & Harrison, R. (2022). MagNet: Automated Magnetic Mineral Grain Morphometry Using Convolutional Neural Network. Geophysical Research Letters https://doi.org/10.1029/2022GL099118
Abstract
Morphometry (i.e., the quantitative determination of size and shape information) is an essential component of all rock and environmental magnetic studies. Electron microscopy is often used to image magnetic mineral grains, but the current lack of systematic image processing tools makes it challenging to quantify key morphological features of magnetic minerals in natural samples. Here, we present an easy-to-use machine learning framework MagNet for automated morphological recognition of magnetic mineral grains in microscopic images. This framework, based on convolutional neural network (CNN), performs well in the recognition and classification of magnetofossil nanoparticles in TEM images after training and testing. MagNet is open-source and can easily be extended to process different types of mineral images. This tool has the potential, therefore, to extract key quantitative information of magnetic mineral populations within heterogeneous terrestrial and meteoritic samples for the interpretations of Earth and planetary processes.
Keywords
Bioengineering, Networking and Information Technology R&D (NITRD)
Relationships
Is supplemented by: https://doi.org/10.5281/zenodo.6448124
Sponsorship
Royal Society RS NAF/R1/201096
Funder references
Royal Society (NAF\R1\201096)
Identifiers
External DOI: https://doi.org/10.1029/2022GL099118
This record's URL: https://www.repository.cam.ac.uk/handle/1810/337716
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