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.
维普
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