143 A Showcase of Student Research in Geoinformatics and Data Science (Posters)

Students are invited to present original research posters relevant to geoinformatics and data science, including geospatial analysis, machine learning, big data, modeling, and visualization. Posters will be judged for divisional awards.

Monday, 20 October 2025
8:00 AM - 5:30 PM
HBGCC, Hall 1

Automating Modal Abundance Analysis Methodologies for Fast and Accurate Phase Classification

Paper number: 143-8
Program: Technical Sessions
Time: 8:00 AM – 8:00 AM

Authors

Abstract

As more methodologies in the geosciences are digitized, it is possible to digitize and automate observation-based methodologies (e.g., point counting) for faster and more streamlined analysis, especially for large datasets. In the classification of plutonic and extrusive felsic rocks (e.g., IUGS QAPF), the relative modal abundances of quartz (Q), anorthite (A), plagioclase (P), and foids (F) are generally calculated in the field by eye, and in the lab by point counting. This project uses the accessible and easy to use platform Jupyter Notebook/Google Colab to automate the process of point counting minerals in polished rock slabs and thin sections. The automation of point counting uses a methodology similar to GIS raster reclassification in which photographs are first rasterized and cropped to a preferred field of view. Then, pixel values are reclassified into the target number of phases (e.g., four main phases for granite classification). The program subsequently bins the pixels into the nearest-neighbor color classification and gives a percent modal abundance of the entire rock slab. The use of this program is currently limited to the modal abundance analysis and classification of rocks with high-contrast phases in the form of different minerals (e.g., granite) or textures (e.g., high contrast phenocrysts). Uncertainties increases when there are similar color phases (e.g., light pink orthoclase versus off-white plagioclase, or cloudy quartz versus white or translucent white plagioclase), or if there is significant alteration or fractures in the sample. While more work needs to be done to expand the automated modal abundance analysis to other rock types and uses, this program has the potential to accelerate the categorization and identification of rocks and their respective modal abundances for quicker and more streamlined preliminary data collection and analysis. Future development will focus on methods to classify low-contrast phases in rocks, and modal abundance calculation for economic minerals in thin section and hand samples, with the goal of creating an open-source package that can be used and improved upon by the wider geoscience community.

Geological Society of America Abstracts with Programs. Vol. 57, No. 6

doi: 10.1130/abs/2025AM-414696

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