Feature Extraction and Classification of Digital Rock Images via Pre-trained Convolutional Neural Network and Unsupervised Machine Learning

Understanding the microstructure of porous media is crucial in various fields-particularly in petroleum engineering, hydrogeology, and materials science-because it directly influences the properties of porous materials and the behavior of fluids within their pores. Traditional characterization methods often struggle to capture the complex, heterogeneous micro-scale features of rock structures. To address this challenge, this study presents a novel approach for the classification and visualization of rock microstructure from micro-CT images, leveraging pre-trained convolutional neural network (CNN) models (AlexNet, GoogLeNet, Inception v3 Net, ResNet, and DenseNet) combined with unsupervised machine learning (USML) techniques (PCA (principal component analysis), MDS (multidimensional scaling), Isomap (isometric mapping), t-SNE (t-Distributed Stochastic Neighbor Embedding), and UMAP (Uniform Manifold Approximation Projection)). Using pre-trained CNNs allows us to extract rich feature representations without the need for large, specialized training datasets, effectively capturing intricate patterns in the microstructures. The application of USML methods enables us to reduce dimensionality and uncover latent structures in the data without supervision. We tested the effectiveness of our method through three distinct case studies