Fracture Identification in Electrical Image Logs with Limited Samples by Incorporating Outcrop Priors

Micro-resistivity imaging logging is one of the primary techniques for subsurface fracture identification. However, conventional manual interpretation is time-consuming and highly subjective, while existing deep learning-based methods generally require large-scale, well-annotated datasets, resulting in substantial labeling costs and limited applicability in data-scarce scenarios. To address these challenges, this study proposes a fracture identification method for electrical image logs under limited-sample conditions by incorporating prior knowledge derived from outcrop fractures. Leveraging the morphological and statistical similarities between surface outcrop fractures and subsurface electrical image responses, a two-stage training strategy based on a Multi-scale Attention Network (MANet) backbone is developed. In the first stage, optical images of outcrop fractures are preprocessed through grayscale transformation and noise injection to approximate the feature distribution of electrical image logs, enabling the network to learn generalizable edge and texture features. In the second stage, outcrop–electrical image pairs with similar fracture morphology and texture characteristics are generated through similarity matching, and the model is fine-tuned using a composite loss function incorporating Correlation Alignment (CORAL), thereby accelerating domain adaptation to subsurface logging environments. Experimental results from basement reservoirs in the Dongping area of the Qaidam Basin demonstrate that the proposed method significantly improves fracture identification performance under limited-sample conditions. Compared with baseline models, the proposed approach achieves improvements of 13.05% in accuracy and 12.88% in Intersection over Union (IoU), reaching 81.13% and 75.74%, respectively. These results indicate that the proposed method effectively alleviates data scarcity issues in electrical image log interpretation and provides robust technical support for fracture characterization and hydrocarbon resource evaluation.