论文标题
通过深度学习的延时表面重力数据倒入3D CO $ _2 $羽
Inversion of Time-Lapse Surface Gravity Data for Detection of 3D CO$_2$ Plumes via Deep Learning
论文作者
论文摘要
我们引入了三种算法，将模拟重力数据倒入3D地下岩石/流属性。第一种算法是一种基于数据驱动的，基于深度学习的方法，第二种算法将深度学习方法与物理建模混合到单个工作流程中，第三个考虑了表面重力监测的时间依赖性。这些提出的算法的目标应用是地下CO $ _2 $李子作为监视CO $ _2 $固结部署的补充工具的预测。每种提出的算法的表现都优于传统的反转方法，并在实时几乎实时产生高分辨率的3D地下重建。我们提出的方法以$μ$ gals的形式获得了预测的羽状几何形状和几乎完美数据失误的骰子得分。这些结果表明，将4D表面重力监测与深度学习技术相结合代表了一种低成本，快速和非侵入性的方法，用于监测CO $ _2 $存储站点。
We introduce three algorithms that invert simulated gravity data to 3D subsurface rock/flow properties. The first algorithm is a data-driven, deep learning-based approach, the second mixes a deep learning approach with physical modeling into a single workflow, and the third considers the time dependence of surface gravity monitoring. The target application of these proposed algorithms is the prediction of subsurface CO$_2$ plumes as a complementary tool for monitoring CO$_2$ sequestration deployments. Each proposed algorithm outperforms traditional inversion methods and produces high-resolution, 3D subsurface reconstructions in near real-time. Our proposed methods achieve Dice scores of up to 0.8 for predicted plume geometry and near perfect data misfit in terms of $μ$Gals. These results indicate that combining 4D surface gravity monitoring with deep learning techniques represents a low-cost, rapid, and non-intrusive method for monitoring CO$_2$ storage sites.
