论文标题
地震图像的时空建模，以进行声学阻抗估计
Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation
论文作者
论文摘要
地震反演是指从地震反射数据中估算储层岩石性能的过程。常规和机器学习的基于基于机器的反转工作流通常以逐步痕迹方式在地震数据上工作，几乎没有从地震图像的空间结构中进行信息。我们提出了一个基于学习的地震反转工作流，该工作流不仅在时间上而且在空间上对每个地震痕迹进行建模。这在深度和空间方向上利用了地震痕迹中的信息相关性，以进行有效的岩石属性估计。我们从经验上将我们提出的工作流程与其他基于序列建模的神经网络进行比较，这些神经网络仅在时间上对地震数据进行建模。我们在接缝数据集上的结果表明，与研究中使用的其他架构相比，提出的工作流程能够达到最佳性能，平均$ r^{2} $系数为79.77 \％。
Seismic inversion refers to the process of estimating reservoir rock properties from seismic reflection data. Conventional and machine learning-based inversion workflows usually work in a trace-by-trace fashion on seismic data, utilizing little to no information from the spatial structure of seismic images. We propose a deep learning-based seismic inversion workflow that models each seismic trace not only temporally but also spatially. This utilizes information-relatedness in seismic traces in depth and spatial directions to make efficient rock property estimations. We empirically compare our proposed workflow with some other sequence modeling-based neural networks that model seismic data only temporally. Our results on the SEAM dataset demonstrate that, compared to the other architectures used in the study, the proposed workflow is able to achieve the best performance, with an average $r^{2}$ coefficient of 79.77\%.
