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 $\mu$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.
翻译:我们引入了三种算法,将模拟重力数据倒置到3D地下岩/流特性中。第一种算法是数据驱动的深学习法,第二种算法是将深学习法与物理建模混合到单一工作流程中,第三种算法是考虑地表重力监测的时间依赖性。这些拟议算法的目标应用是预测地表下CO$-2美元羽流,作为监测CO$-2美元固碳部署的补充工具。每一种拟议的算法都优于传统的反转法,产生高分辨率,在近实时进行3D地表下重建。我们提议的方法在预测的羽流几何测量和几乎完美的数据误差方面达到0.8的骰子分数。这些结果表明,4D表面重力监测与深层学习技术相结合是一种低成本、快速和非侵入性的方法,用于监测CO$-2美元的储存地点。