Faults identification of seismic data based on Transformer
Faults identification of seismic data plays an important role in oil and gas exploration.At present,the application of machine learning and deep learning techniques has enhanced faults identification precision and effi-cacy.However,the fracture prediction outcomes remain challenging to meet the production needs.Therefore,a Transformer-based seismic fault identification method,namely 3D SwinTrans-U-Net,is proposed.The net-work consists of Swin Transformer module and convolution module.Among the aforementioned modules,the Swin Transformer module employs the attention mechanism of the Transformer to extract global information,and transforms global attention computing into window attention computing,resulting in less computational complexity compared to the Transformer.The convolution module,with the property of inductive bias,avoids the Swin Transformer's defect of weak inductive bias.Finally,the U-Net structure is utilized to combine the Swin Transformer and the convolutional layer.Thus,the structure can achieve deep and shallow information fu-sion,relevant feature extraction,as well as full learning of global and local dependency information.All these improve computational efficiency while ensuring fault identification accuracy,and enable end-to-end seismic fault deep learning.Synthetic and field seismic data tests have proven that the 3D SwinTrans-U-Net network can further improve the accuracy of fault identification.
deep learning3D SwinTrans-U-Netfault identificationTransformerSwin Transformerconvolution
武庭润、高建虎、常德宽、王海龙、陶辉飞、李沐阳
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中国科学院西北生态环境资源研究院,甘肃兰州 730000
中国科学院大学地球与行星科学学院,北京 101400
中国石油勘探开发研究院西北分院,甘肃兰州 730000
深度学习 3D SwinTrans-U-Net 断层识别 Transformer Swin Transformer 卷积
万方数据
维普
