Application of deep learning for semantic segmentation of sandstone thin sections

Abstract Sedimentary petrology is the basis for most mineral and textural identification in sandstones. Automating mineralogical interpretation of an entire thin section image has many practical applications, including improved geological understanding, input of spatial distribution of mineralogy and grain size for petrophysical evaluations, and integration with 3D imaging modalities (micro-CT, nano-CT). We investigate the application of Convolutional Neural Network (CNN) based supervised semantic segmentation methods for predicting pixel-scale mineralogy using 2D RGB images of sandstones acquired by transmission light microscopy. Models were trained to interpret a simple binary pore-mineral (grain) segmentation and a 10-class segmentation (porosity, quartz, feldspar, rock fragments, carbonate grains, opaque grains, quartz cement, carbonate cement, clay cement, and hydrocarbons filling pores). For the 2-class classification framework to distinguish between pores and minerals, most models lead to satisfactory results with acceptable accuracy. For the 10-class classification framework, models trained with Deeplab V3+ Resnet-18 network yield more continuous results compared to those based on VGG networks. We conclude that the effectiveness of the models, in predicting a petrology class in a thin section, strongly correlates with the amount of labeled data available to train the model to interpret the class in question. Semantic segmentation models, based on CNNs, can produce encouraging results for a 10-class petrological classification framework of an entire thin section image and thus provide a complete scene understanding which is difficult to produce manually.