石油物探2026，Vol.65Issue(1)：21-30,10.DOI:10.12431/issn.1000-1441.2025.0018
基于改进U-Net网络和知识蒸馏的三维断层识别方法
Three-D fault identification based on improved U-Net and knowledge distillation
摘要
Abstract
Deep learning is a powerful tool for fault identification based on seismic data.However,traditional methods are plagued by poor dataset quality,excessive resource consumption,and lengthy training cycles.To address these challenges,we propose a 3D fault identification method integrating an improved U-Net and knowledge distillation.An enhanced U-Net model,working as the teacher model,is integrated with the atrous spatial pyramid pooling(ASPP)structure to construct a lightweight student model.The model is then optimized through knowledge distillation.By adjusting network training hyperparameters and knowledge distillation loss parameters,the model acquires richer fault information and thereby enhances its network performance.Through transferring knowledge from the complex teacher model to the lightweight student model,this approach significantly reduces computational complexity while maintaining high recognition accuracy.Synthetic and field data tests demonstrate that the knowledge-distilled student model outperforms both the undistilled student model and the independently trained teacher model in terms of recognition accuracy and fault continuity,fully verifying the feasibility and effectiveness of this method.关键词
断层识别/知识蒸馏/U-Net/教师模型/学生模型Key words
fault identification/knowledge distillation/U-Net/teacher model/student model分类
能源科技引用本文复制引用
王莉利,梁云虎,高新成..基于改进U-Net网络和知识蒸馏的三维断层识别方法[J].石油物探,2026,65(1):21-30,10.基金项目
国家自然科学基金项目(42474158)和大庆市指导性科技计划项目(zd-2025-001)共同资助.This research is financially supported by the National Natural Science Foundation of China(Grant No.42474158)and Daqing City Guiding Science and Technology Plan Project(Grant No.zd-2025-001). （42474158）
