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

Abstract 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.