PoreViT: Automated pore typing in carbonate rocks using vision transformers and neighborhood features

The classification of pores into their intrinsic depo-diagenetic or petrophysical morphotypes is a fundamental practice within carbonate petrography, providing linkage between pore-scale textures and their associated petrophysical signatures and/or paragenetic histories. Typically, pore classification is performed manually in a qualitative/semi-quantitative manner, which is hampered by inefficiency, subjectivity, and a lack of scalability. Though aimed at addressing the limitations of manual pore classification, efforts to automate petrographic pore-typing through artificial intelligence and computer vision techniques are limited by the inability of models to classify pores into genetic classes solely based upon simplistic size and shape features, which have been the focus of the existing literature. To address this nuanced classification problem, we present PoreViT: a Vision Transformer (ViT) model used to classify macropores observed in thin-sections into their respective Lucia classes (interparticle, touching vug, separate vug). The core novelty of PoreViT lies in its Feature Fusion block, which integrates ViT features, enhanced by a Global Token Addition layer, with spatial features extracted from a Convolutional Neural Network (CNN). Critically, our classifier leverages neighborhood information to provide the model with localized pore system topology, recognizing that pore types need to be identified not just by shape but also by their local spatial context. Trained and tested using 4115 labels obtained from 25 high-resolution thin-section scans, PoreViT provides an accurate, automated classification of carbonate macropores, achieving precision and recall values of 0.92 and 0.93 (macro-F1 0.92) corresponding to absolute improvements of ＋4.0% and ＋4.0%, and relative gains of ＋4.54% and ＋4.5%, respectively, over the best-performing CNN model (DenseNet121). The high throughput pore-textural classification capabilities demonstrated herein offer unprecedented opportunities in the integrated quantitative characterization of carbonates.