Geological Layer-Aware Cross-Modal Learning for Multi-Label Drill Cuttings Classification

Multi-label drill cuttings classification reveals the current lithologies during drilling, which is crucial for guiding oil drilling operations. Many existing methods rely on annotated images for feature extraction, making their accuracy highly dependent on dataset size. However, due to equipment and manpower constraints, datasets in this field are generally small, posing a significant challenge for improving traditional methods for accurate multi-label classification. Notably, we observed that geological layers influence the distribution of drill cuttings, highlighting the importance of effectively leveraging geological priors for classification. In this paper, we propose a geological layer-aware cross-modal learning framework, which explicitly leverages local layer-wise information and global label co-occurrence patterns for multi-label drill cuttings classification. Unlike conventional end-to-end models, our framework first estimates the geological layer of a given image and derives a corresponding cuttings proportion vector. These priors are then employed to guide the alignment between visual and textual features, leading to more precise visual representations. Furthermore, we introduce a global co-occurrence matrix that captures label dependencies and enhances enhances the learning of visual representations through a graph convolutional network GCN , resulting in more accurate label predictions. Experiments on our dataset demonstrate that our approach significantly outperforms state-of-the-art methods, achieving a mean average precision mAP of 98.8 .