海相油气地质2025，Vol.30Issue(3)：277-288,12.DOI:10.3969/j.issn.1672-9854.2025.03.008
基于U-CNNformer网络的地震断层智能识别方法
Intelligent seismic fault identification method based on U-CNNformer network
摘要
Abstract
Fault interpretation is one of the core tasks in oil and gas exploration and development.However,with the increase of exploration scale,traditional artificial fault interpretation and conventional fault detection methods are unable to meet practical needs.Deep learning methods provide an important approach for intelligent seismic fault recognition,among which deep network models represented by Unet have achieved many successful cases in this type of task.However,due to the particularity of convolution operations,this method loses some information in the feature extraction process,resulting in the need for further improvement in the accuracy and robustness of fault recognition.In this paper,we design a CNN-Transformer hybrid module and embed it into the Unet network framework,proposing a hybrid network model based on U-CNNformer.The hybrid network model improves the mining ability of both global features and local details in the sample set,overcomes the limitations of the conventional Unet network in weak information correlation in fault recognition,and improves the robustness of the model while ensuring the accuracy of fault recognition.Testing on the publicly available North Sea F3 data and applying with actual data in a certain area of Sichuan Basin in China demonstrate that the proposed hybrid network model not only accurately detects fault features but also provides a more detailed characterization of fault distribution,achieving high-precision intelligent fault recognition with excellent application effectiveness.关键词
断层识别/深度学习/U形网络/卷积神经网络/自注意力机制/模型训练/数据测试Key words
fault recognition/deep learning/Unet/CNN/Transformer/model training/data test分类
石油、天然气工程引用本文复制引用
安虹伊,文馨,李居正,张惊喆,张琳智,房平超,杜天玮,张奎,王群武..基于U-CNNformer网络的地震断层智能识别方法[J].海相油气地质,2025,30(3):277-288,12.基金项目
本文受中国石油西南油气田项目《勘探事业部2024年探井随钻地震跟踪技术服务(北京普瑞斯安)》(编号:XNS勘探部YT2023-35)资助 （北京普瑞斯安）
