Comment on gchron-2022-12

Abstract

Collecting grain measurements for large detrital zircon age datasets is time-consuming, but a growing number of studies suggest such data are essential to understanding complex roles of grain size and morphology in grain transport and as indicators for grain provenance. We developed the colab_zirc_dims Python package to automate deep-learning-based segmentation and measurement of mineral grains from scaled images captured during laser ablation at facilities that use Chromium targeting software. The colab_zirc_dims package is implemented in a collection of freely accessible, ready-to-run Google Colab notebooks with additional functionalities for dataset preparation and semi-automated grain segmentation and measurement using a simple graphical user interface. Our automated grain measurement algorithm approaches human measurement accuracy when applied to a manually measured n = 5,004 detrital zircon dataset, but persistent errors necessitate semi-automated measurement for production of publication-quality datasets. We estimate that our semi-automated grain segmentation workflow will enable users to collect grain measurements for large (n ≥ 5,000), applicable datasets in under a day of work, and we hope that the colab_zirc_dims toolset allows more researchers to augment their detrital geochronology datasets with grain measurements.