Machine learning for point counting and segmentation of arenite in thin section

• Published in: Marine and petroleum geology 2020-10, Vol.120, p.104518, Article 104518
• Main Authors: Tang, David G., Milliken, Kitty L., Spikes, Kyle T.
• Format: Article
• Language: English
• Subjects: Machine learning; Point counting; Segmentation; Thin sections
• ISSN: 0264-8172; 1873-4073
• DOI: 10.1016/j.marpetgeo.2020.104518

Thin sections provide geoscientists with a wealth of information about composition and diagenetic history of sedimentary rocks. From a practical perspective, the quantity of detrital clay minerals or percentage of porosity can play a large role in the quality of a reservoir. However, the quantitative analysis of thin sections often requires many hours of manual labor, which limits the number of samples a single person can analyze in a reasonable time frame. Here we apply a supervised machine-learning method that requires only traced grains as inputs, which eliminates the need for an expert to hand design input features. We also present a data-augmentation method to reduce the amount of tracing required. The traced grains form a multi-channel input that takes into account plane- and cross-polarized images, and a segmented image is output. Using a simplified grain categorization (quartz-feldspar-rock fragments-dense minerals) the statistical error for results on grain composition is comparable to a point count with 350 points. Once the model is trained, it can be applied quickly to additional images. In addition to providing component percentages, a segmented thin section can be used further to describe the morphology of grains (e.g., angularity, ellipticity) or serve as the basis for digital rock-physics experiments. This test of supervised machine learning does not reproduce the level of detailed component identification that is typical of manual point-counting, but it provides a clear indication that a diverse and fully representative data set will be required to achieve automated component identification that is both accurate and precise. •Machine learning concepts are applied in the analysis of arenite thin sections.•A technique for faster generation of training data is introduced.•Analysis of errors shows that a diverse training set is needed for high accuracy and precision.