The use of blended acquisition technology in marine seismic exploration has the advantages of high acquisition efficiency and low exploration costs.However,during acquisition,the primary source may be disturbed by adj...The use of blended acquisition technology in marine seismic exploration has the advantages of high acquisition efficiency and low exploration costs.However,during acquisition,the primary source may be disturbed by adjacent sources,resulting in blended noise that can adversely affect data processing and interpretation.Therefore,the de-blending method is needed to suppress blended noise and improve the quality of subsequent processing.Conventional de-blending methods,such as denoising and inversion methods,encounter challenges in parameter selection and entail high computational costs.In contrast,deep learning-based de-blending methods demonstrate reduced reliance on manual intervention and provide rapid calculation speeds post-training.In this study,we propose a Uformer network using a nonoverlapping window multihead attention mechanism designed for de-blending blended data in the common shot domain.We add the depthwise convolution to the feedforward network to improve Uformer’s ability to capture local context information.The loss function comprises SSIM and L1 loss.Our test results indicate that the Uformer outperforms convolutional neural networks and traditional denoising methods across various evaluation metrics,thus highlighting the effectiveness and advantages of Uformer in de-blending blended data.展开更多
基金supported by the National Natural Science Foundation of China(Research on Dynamic Location of Receiving Points and Wave Field Separation Technology Based on Deep Learning in OBN Seismic Exploration,No.42074140)the Sinopec Geophysical Corporation,Project of OBC/OBN Seismic Data Wave Field Characteristics Analysis and Ghost Wave Suppression(No.SGC-202206)。
文摘The use of blended acquisition technology in marine seismic exploration has the advantages of high acquisition efficiency and low exploration costs.However,during acquisition,the primary source may be disturbed by adjacent sources,resulting in blended noise that can adversely affect data processing and interpretation.Therefore,the de-blending method is needed to suppress blended noise and improve the quality of subsequent processing.Conventional de-blending methods,such as denoising and inversion methods,encounter challenges in parameter selection and entail high computational costs.In contrast,deep learning-based de-blending methods demonstrate reduced reliance on manual intervention and provide rapid calculation speeds post-training.In this study,we propose a Uformer network using a nonoverlapping window multihead attention mechanism designed for de-blending blended data in the common shot domain.We add the depthwise convolution to the feedforward network to improve Uformer’s ability to capture local context information.The loss function comprises SSIM and L1 loss.Our test results indicate that the Uformer outperforms convolutional neural networks and traditional denoising methods across various evaluation metrics,thus highlighting the effectiveness and advantages of Uformer in de-blending blended data.
