Abstract
Karst reservoir Computed Tomography (CT) images exhibit blurred boundaries, scale variations, and complex structures. Existing 3D U-Net-based segmentation methods are inadequate in both detail recognition and overall structural representation. Therefore, this paper proposes an improved 3D U-Net architecture to adapt to the multi-scale and low-contrast characteristics of karst reservoirs. This paper introduces a multi-scale input path at the encoder end, extracting volumetric features at different resolutions in parallel to capture both fine-grained holes and large-scale channels. A spatial attention module is embedded in the skip connections to weight the encoded features to highlight boundaries and key regions. Multi-scale features are fused during the decoding phase to gradually reconstruct the three-dimensional space. Furthermore, the Dice loss is combined with the gradient-based boundary-aware loss during training. The latter enhances boundary sensitivity by calculating the 3D gradient difference between the predicted image and the label image. Experimental results show that the improved complete model achieves an 87.8% Dice coefficient and a 1.9-pixel boundary error in karst reservoir CT image segmentation, improving both regional overlap and boundary accuracy. This method effectively identifies karst structures at different scales, providing reliable data support for complex reservoir modeling and analysis.