Image-Based Lithology Classification Using Hybrid Deep Learning Architectures and Diffusion-Based Data Augmentation: Application to Brazilian Pre-Salt Carbonate Reservoirs
Why this work is in the frame
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Bibliographic record
Abstract
Abstract 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it