## 1 Introduction
However, Transformer models generally require significant computational resources, limiting their real-time fracture detection capabilities and posing challenges for deployment on portable devices. Furthermore, [PERSON] et al. (2024) [15] introduced a multi-scale dual-encoding fusion network (DefNet) based on the UNet architecture that extracts global and local features of fracture images using the stacked convolutional distillation module (CDM) and dual-branch attention Transformer (DBAT) modules. They also introduced a multi-scale information distillation mechanism and a large separable kernel attention (LSKA) module to construct the CDM encoder, surpassing the limitations of the traditional convolutional neural network (CNN) receptive field. [PERSON] et al. (2024) [16] proposed a dual-encoder crack segmentation network (DECS-Net), which combines the advantages of CNN and Transformer to address the issue of complex background interference in fracture segmentation. DECS-Net utilizes a high-low-frequency attention mechanism and a locally enhanced feedforward network to extract local and global features from images, respectively, subsequently fusing them through a feature fusion module and effectively enhancing the accuracy and recall rate of crack segmentation. [PERSON] et al. (2024) [17] proposed an innovative CNN-based pavement crack segmentation network (CSNet), integrating multi-scale context features with an attention mechanism for improved detection accuracy. CSNet demonstrates exceptional performance on various datasets, outperforming other segmentation models. However, it still faces challenges in micro-crack detection as well as requiring substantial labeled data for training purposes. Despite these advancements, there is still room for improvement when dealing with low-quality imaging, significant noise interference, and complex shapes in logging images. Additionally, there are significant challenges remaining regarding segmentation speed, model lightweighting, and interpretability. In summary, this paper integrates SimAM with slicing (SWS) attention [18] and deformable strip convolution (DSCN) [19] into the UNet architecture, effectively enhancing the model's performance in fracture segmentation tasks for imaging logging. Traditional UNet attention mechanisms primarily focus on channel and spatial dimensions. However, SWS introduces a fully 3D attention mechanism that more accurately learns the weights of each neuron in the feature map, thereby capturing fracture features with greater precision. Additionally, we have introduced a slicing operation to ensure equitable attention and enhancement for both large and small objects. Through SWS, the model can better concentrate on key regions of fractures while suppressing background noise, thus improving segmentation accuracy. Furthermore, the original UNet convolution has a fixed receptive field, which limits its adaptability to diverse fracture shapes. DSCN possesses deformable properties that enable adaptive sampling based on fracture shapes, resulting in more effective extraction of fracture features and addressing challenges posed by varying fracture shapes while enhancing segmentation robustness. By utilizing the improved UNet model, fractures in imaging logging can be segmented more accurately, providing reliable data for oil and gas exploration and development. This promotes the application of deep learning technology in industrial fields while serving as a reference for other domains.