Abstract
We introduce two algorithms that invert simulated gravity data to 3D subsurface rock/flow properties. The first algorithm is a data-driven, deep learning-based approach, and the second is also data-driven but considers the temporal evolution of surface gravity events. The target application of these proposed algorithms is the prediction of subsurface CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> plumes as a complementary tool for monitoring CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> sequestration deployments. Each proposed algorithm outperforms traditional inversion methods and produces high-resolution, 3D subsurface reconstructions in near real-time. In addition, our proposed methods achieve Dice scores of up to 0.8 for predicted plume geometry and near-perfect data misfit in terms of μGals. These results indicate that combining 4D surface gravity monitoring (low-cost acquisition) with deep learning techniques represents an effective and non-intrusive method for monitoring CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> storage sites.
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