Geochronology
Geochronology
GChron
Articles | Volume 5, issue 1
Articles | Volume 5, issue 1
https://doi.org/10.5194/gchron-5-109-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gchron-5-109-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 5, issue 1
Short communication/technical note
 | 
10 Mar 2023
Short communication/technical note |  | 10 Mar 2023

Technical note: colab_zirc_dims: a Google Colab-compatible toolset for automated and semi-automated measurement of mineral grains in laser ablation–inductively coupled plasma–mass spectrometry images using deep learning models

Michael C. Sitar and Ryan J. Leary

Data sets

colab_zirc_dims: full results, datasets, and replication code repository Michael C. Sitar and Ryan J. Leary https://doi.org/10.5281/zenodo.7434851

Model code and software

MCSitar/colab_zirc_dims: v1.0.10 Michael C. Sitar https://doi.org/10.5281/zenodo.7425633

colab_zirc_dims: full results, datasets, and replication code repository Michael C. Sitar and Ryan J. Leary https://doi.org/10.5281/zenodo.7434851

Video supplement

colab_zirc_dims v1.0.10 Video Tutorial & Demo Michael C. Sitar https://www.youtube.com/watch?v=ZdO6B-dvHm0

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Short summary
We developed code to automatically and semi-automatically measure dimensions of detrital mineral grains in reflected-light images saved at laser ablation–inductively coupled plasma–mass spectrometry facilities that use Chromium targeting software. Our code uses trained deep learning models to segment grain images with greater accuracy than is achievable using other segmentation techniques. We implement our code in Jupyter notebooks which can also be run online via Google Colab.
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