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

Abstract. Collecting grain measurements for large detrital zircon age datasets is a time-consuming task, but a growing number of studies suggest such data are essential to understanding the 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 highly interactive Jupyter notebooks that can be run either on a local computer or installation-free via Google Colab. These notebooks also provide additional functionalities for dataset preparation and for 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=5004 detrital zircon dataset. Errors and uncertainty related to variable grain exposure necessitate semi-automated measurement for production of publication-quality measurements, but we estimate that our semi-automated grain segmentation workflow will enable users to collect grain measurement datasets for large (n≥5000) applicable image datasets in under a day of work. We hope that the colab_zirc_dims toolset allows more researchers to augment their detrital geochronology datasets with grain measurements.