论文标题
自动解释性图像分析
Automatic Interpretative Image-Focusing Analysis
论文作者
论文摘要
地震图像的聚焦直接与速度模型的准确性联系在一起。因此，地震成像工作流程中的关键步骤是对地震图像进行聚焦分析以确定速度误差。虽然偏移/孔角轴经常用于聚焦分析，但地震图像的物理（即中点）轴往往被忽略，因为对地质结构的聚焦分析是高度解释性的，难以自动化。我们使用卷积神经网络开发了一种自动数据驱动方法，以自动化图像对焦分析。使用集中且没有重点的地质断层，我们表明我们的方法可以利用空间和偏移/角度聚焦信息来稳健地估计地震图像中的速度误差。我们证明我们的方法正确地估算了墨西哥二维湾有限劳动图像的速度错误，其中传统的基于Ellblance的方法失败了。我们还表明，我们的方法具有改善图像中故障解释的额外好处。
The focusing of a seismic image is directly linked to the accuracy of the velocity model. Therefore, a critical step in a seismic imaging workflow is to perform a focusing analysis on a seismic image to determine velocity errors. While the offset/aperture-angle axis is frequently used for focusing analysis, the physical (i.e., midpoint) axes of seismic images tend to be ignored as focusing analysis of geological structures is highly interpretative and difficult to automate. We have developed an automatic data-driven approach using convolutional neural networks to automate image-focusing analysis. Using focused and unfocused geological faults, we show that our method can make use of both spatial and offset/angle focusing information to robustly estimate velocity errors within seismic images. We demonstrate that our method correctly estimates velocity errors from a 2D Gulf of Mexico limited-aperture image where a traditional semblance-based approach fails. We also show that our method has the added benefit of improving the interpretation of faults within the image.
