## 4 Conclusions  
The proposed paper presents an end-to-end fracture segmentation algorithm for logging images, aiming to address the challenges of low segmentation accuracy in current algorithms when dealing with poor image quality, high noise interference, and complex shapes. Furthermore, it tackles the lack of lightweight models suitable for on-site deployment. The success of SWSDS-Net can be attributed to replacing standard convolution in UNet with the lightweight DSCN. By utilizing deformable convolution operations, DSCN enhances adaptability to target object shapes and achieves a global receptive field, enabling better capture of relationships between target objects. Additionally, the introduction of the SWS module after upsampling improves the network's ability to capture global fracture information while maintaining a lightweight structure. This enhancement significantly boosts detection performance for small targets [36].  
The experimental results demonstrate that, in comparison to currently prevalent fracture segmentation algorithms, the proposed SWSDS-Net exhibits superior overall performance and is better suited for fracture segmentation scenarios in imaging logging. Moreover, the lightweight model effectively addresses the challenge of on-site deployment on mobile devices. This paper holds significant practical value, as it provides a more dependable foundation for oil and gas exploration and development while promoting the application of deep learning technology in industrial domains. Furthermore, it enables deep learning to seamlessly facilitate traditional industrial scenarios, thereby reducing labor costs [37].  
However, the proposed algorithm for logging fracture segmentation still has limitations in processing multimodal logging data. Multimodal logging data encompass diverse types of logging curves that reflect distinct rock physical properties and exhibit different feature representations. Furthermore, the fusion of multimodal data significantly amplifies the complexity of the model, resulting in a substantial increase in training time and computational costs. Therefore, future work will focus on incorporating multimodal feature fusion techniques to facilitate better integration of features from different modalities and extract more comprehensive feature information. Additionally, a modality-adaptive learning module will be devised to enable the model to learn appropriate strategies for feature extraction and fusion based on the characteristics of each modality. Building upon this foundation, lightweight model design will be considered by employing lighter feature extraction modules or attention mechanisms to reduce model complexity.  
Conceptualization, Q.Y., A.L. and L.Z.; software, A.L. and L.Z.; validation, Z.X. and Y.Q.; writing--original draft preparation, Q.Y.; writing--review and editing, Q.Y. and L.Z. All authors have read and agreed to the published version of the manuscript.  
This article was supported by the Xinjiang Natural Science Foundation Outstanding Youth Science Foundation Project (2024D01E08) \"Research on Ultra-Deep Seismic Imaging Mechanism and All-round Velocity Modeling Method in Central Tarm Basin\", the Research Foundation of China University of Petroleum-Beijing at Karamay \"Study on analysis and correction method of wide azimuth seismic azimuth anisotropy in shale oil exploration\" (No. XQZX20240029), \"Evaluation of shale formation compressibility based on seismic petrophysics\" (No. XQZX20240015), and the \"Tianchi Talent\" Introduction Plan Project of Xinjiang Uygur Autonomous Region, China.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors would like to thank the anonymous reviewers and members of the editorial team for their comments and contributions.  
The authors declare no conflict of interest.