| 刊名 | Agricultural Biotechnology |
| 作者 | Zhaowei WANG1*, Limin SUO1, Hailong LIU1, Wenlong SU1, Xianda SUN2, Likai CUI2, Yangdong CAO2, Tao LIU2, Wenjie YANG2, Wenying SUN2 |
| 作者单位 | 1. College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, China; 2. National Key Laboratory of Continental Shale Oil, Northeast Petroleum University, Daqing 163318, China |
| DOI | DOI:10.19759/j.cnki.2164-4993.2024.06.020 |
| 年份 | 2024 |
| 刊期 | 6 |
| 页码 | 99-101 |
| 关键词 | StyleGAN2-ADA; Generative adversarial network; Adaptive data augmentation; CT scanning; Sandstone pore structure |
| 摘要 | In this study, cylindrical sandstone samples were imaged by CT scanning technique, and the pore structure images of sandstone samples were analyzed and generated by combining with StyleGAN2-ADA generative adversarial network (GAN) model. Firstly, nine small column samples with a diameter of 4 mm were drilled from sandstone samples with a diameter of 2.5 cm, and their CT scanning results were preprocessed. Because the change between adjacent slices was little, using all slices directly may lead to the problem of pattern collapse in the process of model generation. In order to solve this problem, one slice was selected as training data every 30 slices, and the diversity of slices was verified by calculating the LPIPS values of these slices. The results showed that the strategy of selecting one slice every 30 slices could effectively improve the diversity of images generated by the model and avoid the phenomenon of pattern collapse. Through this process, a total of 295 discontinuous two-dimensional slices were generated for the generation and segmentation analysis of sandstone pore structures. This study can provide effective data support for accurate segmentation of porous medium structures, and simultaneously improves the stability and diversity of generative adversarial network under the condition of small samples. |
