Shale-pore Semantic Segmentation Network Based on Pseudo-labeling

Shale pore structures contribute significantly to shale gas reservoirs, with their shape, size, connectivity, and development directly affecting storage. To achieve intelligent recognition and classification of shale pores, this study proposes a shale-pore semantic segmentation network based on a pseudo-label method. A total of 251 scanning electron microscopy images of the Longmaxi Formation shale reservoir in Chongqing are used, and the Pyramid Scene Parsing Network is utilized for training. Additionally, pseudo-label generation is employed, which involves annotating only a few images and using the model’s segmentation results on unlabeled images for iterative training. Subsequently, ensemble learning is conducted to improve the model’s accuracy and generalizability. The iteratively trained model has a mean intersection-over-union (MIoU) score exceeding 0.70. Comparative experiments show that employing pseudo-labeling and ensemble learning increases the model’s MIoU by approximately 0.07. Furthermore, pseudo-labeling enhances the generalizability of neural networks while addressing the high time cost of manual annotation required in previous deep-learning segmentation methods, whereas ensemble learning stably increases the model’s accuracy.