We introduce a fully 3D, deep learning-based approach for the joint inversion
of time-lapse surface gravity and seismic data for reconstructing subsurface
density and velocity models. The target application of this proposed inversion
approach is the prediction of subsurface CO2 plumes as a complementary tool for
monitoring CO2 sequestration deployments. Our joint inversion technique
outperforms deep learning-based gravity-only and seismic-only inversion models,
achieving improved density and velocity reconstruction, accurate segmentation,
and higher R-squared coefficients. These results indicate that deep
learning-based joint inversion is an effective tool for CO$_2$ storage
monitoring. Future work will focus on validating our approach with larger
datasets, simulations with other geological storage sites, and ultimately field
data.

我们引入了一种完全基于深度学习的三维方法，用于同时反演时间序列表面重力和地震数据，以重建地下密度和速度模型，该方法的目标应用是预测地下 CO₂羽流，作为监测 CO₂封存部署的补充工具，我们的联合反演技术优于基于深度学习的仅有重力或仅有地震的反演模型，实现了改进的密度和速度重建，准确的分割以及更高的 R-squared 系数，这些结果表明基于深度学习的联合反演是一种有效的 CO₂储存监测工具，未来的工作将侧重于使用更大的数据集验证我们的方法，进行其他地质封存地点的模拟，最终验证现场数据。

通过深度学习联合反演时间变化的地表重力和地震数据以监测三维 CO$_2$ 气体云层

Joint inversion of Time-Lapse Surface Gravity and Seismic Data for  Monitoring of 3D CO$_2$ Plumes via Deep Learning

To combat global warming and mitigate the risks associated with climate
change, carbon capture and storage (CCS) has emerged as a crucial technology.
However, safely sequestering CO2 in geological formations for long-term storage
presents several challenges. In this study, we address these issues by modeling
the decision-making process for carbon storage operations as a partially
observable Markov decision process (POMDP). We solve the POMDP using belief
state planning to optimize injector and monitoring well locations, with the
goal of maximizing stored CO2 while maintaining safety. Empirical results in
simulation demonstrate that our approach is effective in ensuring safe
long-term carbon storage operations. We showcase the flexibility of our
approach by introducing three different monitoring strategies and examining
their impact on decision quality. Additionally, we introduce a neural network
surrogate model for the POMDP decision-making process to handle the complex
dynamics of the multi-phase flow. We also investigate the effects of different
fidelity levels of the surrogate model on decision qualities.

本研究使用部分可观察马尔可夫决策过程（POMDP）对碳封存操作的决策过程进行建模，并使用信念状态规划来优化注入器和监测井的位置以实现最大 CO2 存储同时保持安全性。我们展示了该方法的灵活性，引入了三种不同的监视策略并学习其对决策质量的影响。此外，我们引入了一个神经网络代理模型用于处理多相流的复杂动态，并研究了代理模型不同的保真度水平对决策质量的影响。