MagNet: Automated Magnetic Mineral Grain Morphometry Using Convolutional Neural Network
Abstract Morphometry (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 make...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-06-01
|
| Series: | Geophysical Research Letters |
| Online Access: | https://doi.org/10.1029/2022GL099118 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Morphometry (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 MagNet 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. 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. |
|---|---|
| ISSN: | 0094-8276 1944-8007 |