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.

Key words: fault identification, convolutional neural network, feature extraction, feature fusion