Image-Based Lithology Classification Using Hybrid Deep Learning Architectures and Diffusion-Based Data Augmentation: Application to Brazilian Pre-Salt Carbonate Reservoirs

Visual inspection remains widely used for lithology classification, but manual approaches are prone to delays, errors, and subjectivity. To address these challenges, we propose a deep-learning pipeline tailored for a complex, imbalanced dataset of core images from offshore pre-salt carbonate reservoirs in Brazil. Our methodology integrates diffusion-based data augmentation and a hybrid architecture combining DINOv2 with Central Difference Convolution (CDC). A novel loss function, merging Focal and Center Loss, is employed to improve performance on hard-to-classify samples and sharpen decision boundaries. Diffusion augmentation introduces controlled variability, while dual-transformer encoding captures rich image features. The experimental setup involved evaluating the model across eight dataset partitions, with balanced accuracy as the primary performance metric. Human volunteers achieved a mean balanced accuracy of 57%, highlighting the difficulty of visual lithology classification. State-of-the-art deep learning methods in the literature reached up to 63% balanced accuracy on this complex dataset. In contrast, our proposed approach consistently achieved 70% balanced accuracy, demonstrating a substantial improvement over both manual classification and existing automated techniques. This performance improvement is notable given the dataset's complexity, particularly in contrast to small gains typically seen at higher accuracy levels. In high-stakes offshore environments, where classification errors can lead to wellbore instability or blowouts, even modest accuracy gains offer substantial operational value. Additionally, top-2 accuracy reached 91.86%, providing reliable support for expert decision-making in real-time lithology classification.