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MagNet: Automated Magnetic Mineral Grain Morphometry Using Convolutional Neural Network

Published version
Peer-reviewed

Type

Article

Change log

Abstract

jats:titleAbstract</jats:title>jats:pMorphometry (i.e., the quantitative determination of grain 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 jats:italicMagNet</jats:italic> for automated morphological recognition of magnetic mineral grains in microscopic images. This framework, based on a convolutional neural network, performs well in the recognition and classification of magnetofossil nanoparticles in transmission electron microscopy images after training and testing. jats:italicMagNet</jats:italic> 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.</jats:p>

Description

Keywords

37 Earth Sciences, 3705 Geology, Machine Learning and Artificial Intelligence, Bioengineering, Networking and Information Technology R&D (NITRD)

Journal Title

Geophysical Research Letters

Conference Name

Journal ISSN

0094-8276
1944-8007

Volume Title

Publisher

American Geophysical Union (AGU)
Sponsorship
Royal Society (NAF\R1\201096)
Royal Society RS NAF/R1/201096
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