吉林大学学报（信息科学版）2025，Vol.43Issue(2)：355-367,13.
基于双属性并行融合网络的断层智能识别方法
Fault Intelligent Identification Method Based on Parallel Fusion Network with Dual Attributes
摘要
Abstract
Deep learning methods have improved the efficiency and accuracy of fault identification,but current research often relies on extracting fault features from single attributes such as seismic amplitude,which leads to issues like poor fault continuity and missed detections.These problems limit the exploration and development of oil and gas reservoirs in complex areas.An intelligent fault identification method based on deep learning technology is proposed,which adopts a multi-level fusion strategy to construct a dual-attribute parallel fusion network PE-Net(Parallel Elements Network).Firstly,the ant body attributes and amplitude attributes are input into the ant body feature extraction network and the amplitude feature extraction network respectively,capturing the fault features of different angles from both paths using the AIFM(Attribute Intensive Feature Module).Secondly,two attribute feature modules are used to integrate cross-layer features of the output of each branch,mining multi-scale information and mitigating scale changes.Finally,the FFM(Feature Fusion Module)is used to integrate the two parallel branches,reducing the limitation of a single attribute.Synthetic data experiments demonstrate that the PE-Net model achieves an accuracy of 97.95％,with a 1.33％improvement compared to the U-Net model.The fault identification results on the Kerry3D dataset and ablation experiments confirm that the proposed method is capable of capturing more contextual fault features,reducing missed and false detections,thereby improving the accuracy of complex fault identification and enhancing the detection of small faults.关键词
断层识别/卷积神经网络/特征提取/特征融合Key words
fault identification/convolutional neural network/feature extraction/feature fusion分类
信息技术与安全科学引用本文复制引用
曾丽丽,牛艺晓,任伟建,刘小双,代利民,魏志远..基于双属性并行融合网络的断层智能识别方法[J].吉林大学学报（信息科学版）,2025,43(2):355-367,13.基金项目
河北省自然科学基金资助项目(D2022107001) （D2022107001）
