## 1 Introduction  
Logging technology plays a pivotal role in the field of oil and gas exploration and development by unveiling lithological characteristics, as well as the distribution and structure of fractures surrounding the wellbore. Fractures, being micro-geological structures, exert a significant influence on the distribution and seepage properties of oil, gas, and water. Consequently, they provide invaluable insights for oil and gas field exploration and development activities. In recent years, semantic segmentation has emerged as a prominent topic in computer vision research, with widespread application in fracture segmentation within logging images. Among various approaches used for this purpose, the UNet network stands out as the most commonly employed method. However, existing fracture segmentation methods possess certain limitations that need to be addressed. The current challenges revolve around designing a more lightweight version of the UNet network suitable for mobile devices and on-site deployment while also developing more efficient variants to enhance accuracy, speed, robustness, and interpretability in fracture segmentation. With the rapid advancement of deep learning technology, segmentation methods based on deep learning have demonstrated significant potential in fracture segmentation tasks. Among these methods, the UNet architecture has gained widespread application due to its simplicity, ease of implementation, and excellent segmentation performance. Originally proposed by [PERSON] et al. (2015) [1], the UNet architecture employs an encoder-decoder structure that effectively separates the feature extraction and decoding processes of images. By incorporating skip connections, it successfully integrates low-level and high-level features to achieve pixel-level segmentation [2]. In recent years, UNet has been widely utilized for fracture segmentation tasks in imaging logging [3] and has achieved remarkable outcomes. Researchers commonly combine UNet with various feature extraction modules and loss functions to enhance segmentation accuracy and robustness. Feature extraction modules, such as ResNet [4], VGG [5], and EfficientNet [6], are employed to extract more comprehensive image features. Loss functions like Dice Loss [7], Jaccard Loss [8], and Focal Loss [9] are applied to improve segmentation precision. [PERSON] et al. (2022) [10] proposed a fracture detection and characterization method based on Mask R-CNN that achieves a 95% accuracy rate in identifying and analyzing fractures from unwrapped core images. Nevertheless, this method may not fully encompass the complexity and diversity of authentic fractures. [PERSON] et al. (2020) [11] proposed a novel method for automatic detection of fractures and breakout patterns in acoustic borehole image logs using fast-region convolutional neural networks (fast-RCNNs). In real images, a fast-RCNN can also effectively detect fractures and parts of breakouts. However, the method has limitations in detecting real breakouts, such as failing to recognize a pair of breakouts as a single event. [PERSON] et al. (2024) [12] proposed a deep learning method based on transfer learning for intelligent identification and segmentation of fractures in ultrasonic logging images. [PERSON] et al. (2024) [13] proposed a deep learning-based method for fracture identification from logging images, effectively overcoming the limitations of low efficiency in manual detection and poor accuracy of previous automated methods. [PERSON] et al. (2024) [14] proposed a U-shaped network architecture known as the fusion-channel-Transformer network (FCT-Net), which comprises the channel atrous spatial pyramid pooling (CASPP) module, Transformer module, and attention weight cross fusion (AWCF) module. This network integrates local and global context features through a dual-branch encoding structure that effectively extracts multi-scale information from fracture images while progressively recovering image details during the decoding process.