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<p>Seismic data are fundamental for understanding subsurface geological structures and geological events. Previous studies have primarily focused on interpolating missing traces in seismic data, overlooking the reconstruction of complex geological events and patterns in 3D post-stack seismic images. A novel approach for the predictive reconstruction of missing geological events and patterns using our developed U-Net neural network model is proposed in this study. In contrast to existing methods that primarily interpolate traces, our proposed approach aims to predict and reconstruct complex missing geological events, including faults and unconformities, by leveraging the inherent patterns in seismic data. The impact of the different architectural choices, including various downsampling layers (max pooling, strided convolution, and dilated convolution) and loss functions such as SSIM, VGG19 perceptual loss, MSE, SNR, and PSNR, on the reconstruction process was comprehensively investigated in this study. The effectiveness of our proposed approach in generating realistic reconstructions of missing geological events and patterns was demonstrated by the experimental results. Specifically, the validation scores were found to be 0.86 for SSIM, 16.09 dB for SNR, and 16 dB for PSNR. Additionally, the validation losses were 0.0007 for VGG19 perceptual loss and 0.025 for MSE loss. These results underscore the ability of the model to capture the intricate geological and seismic features. The performance of the model in capturing the complexities of seismic data was further highlighted by a qualitative interpretation of the generated images. By addressing the limitations of existing methods and focusing on reconstructing geological events, our work advances the field of seismic data reconstruction. Our findings underscore the critical importance of a predictive reconstruction in seismic imaging and provide valuable insights for optimizing the architectural design choices. This study lays the groundwork for future research in this area, emphasizing the need for a nuanced understanding of architectural choices in seismic data reconstruction.</p>

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Predictive reconstruction of missing geological events and patterns in real-life 3D post-stack seismic images: a novel U-Net based deep learning approach

  • 期刊论文

  • 发表日期：

    2025年1月13日

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  • Matin Mahzad,
  • Majid Bagheri

摘要

Seismic data are fundamental for understanding subsurface geological structures and geological events. Previous studies have primarily focused on interpolating missing traces in seismic data, overlooking the reconstruction of complex geological events and patterns in 3D post-stack seismic images. A novel approach for the predictive reconstruction of missing geological events and patterns using our developed U-Net neural network model is proposed in this study. In contrast to existing methods that primarily interpolate traces, our proposed approach aims to predict and reconstruct complex missing geological events, including faults and unconformities, by leveraging the inherent patterns in seismic data. The impact of the different architectural choices, including various downsampling layers (max pooling, strided convolution, and dilated convolution) and loss functions such as SSIM, VGG19 perceptual loss, MSE, SNR, and PSNR, on the reconstruction process was comprehensively investigated in this study. The effectiveness of our proposed approach in generating realistic reconstructions of missing geological events and patterns was demonstrated by the experimental results. Specifically, the validation scores were found to be 0.86 for SSIM, 16.09 dB for SNR, and 16 dB for PSNR. Additionally, the validation losses were 0.0007 for VGG19 perceptual loss and 0.025 for MSE loss. These results underscore the ability of the model to capture the intricate geological and seismic features. The performance of the model in capturing the complexities of seismic data was further highlighted by a qualitative interpretation of the generated images. By addressing the limitations of existing methods and focusing on reconstructing geological events, our work advances the field of seismic data reconstruction. Our findings underscore the critical importance of a predictive reconstruction in seismic imaging and provide valuable insights for optimizing the architectural design choices. This study lays the groundwork for future research in this area, emphasizing the need for a nuanced understanding of architectural choices in seismic data reconstruction.