We propose an advanced version of nonlinear beamforming assisted by artificial intelligence (NLBF-AI) that includes additional steps of encoding and interpolating of wavefront attributes using inpainting with deep neural network (DNN). Inpainting can efficiently and accurately fill the holes in waveform attributes caused by acquisition geometry gaps and data quality issues. Inpainting with DNN delivers excellent quality of interpolation with the negligible computational effort and performs particularly well for a challenging case of irregular holes where other interpolation methods struggle. Since conventional brute-force attribute estimation is very costly, we can further intentionally create additional holes or masks to restrict expensive conventional estimation to a smaller subvolume and obtain missing attributes with cost-effective inpainting. Using a marine seismic dataset with ocean bottom nodes, we show that inpainting can reliably recover wavefront attributes even with masked areas reaching 50–75 per cent. We validate the quality of the results by comparing attributes and enhanced data from NLBF-AI and conventional NLBF using full-density data without decimation.

中文翻译：

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使用人工智能修复局部波前属性，以增强海量3D叠前地震数据

我们提出了一种由人工智能（NLBF-AI）辅助的非线性波束成形的高级版本，其中包括使用深度神经网络（DNN）修补技术对波前属性进行编码和插值的其他步骤。修复可以有效，准确地填充由采集几何间隙和数据质量问题引起的波形属性中的孔。使用DNN进行喷漆可以以极低的计算量提供出色的插值质量，并且在其他插值方法难以解决的不规则孔具挑战性的情况下表现尤其出色。由于常规的蛮力属性估计非常昂贵，因此我们可以进一步有意创建其他孔或遮罩，以将昂贵的常规估计限制在较小的子体积中，并通过具有成本效益的修补来获得缺失的属性。通过使用具有海底节点的海洋地震数据集，我们显示出即使遮盖面积达到50％至75％，修补也可以可靠地恢复波前属性。我们通过比较不使用抽取的全密度数据比较NLBF-AI和常规NLBF的属性和增强数据来验证结果的质量。