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

We build a new model of landscape videos that can be trained on a mixture of
static landscape images as well as landscape animations. Our architecture
extends StyleGAN model by augmenting it with parts that allow to model dynamic
changes in a scene. Once trained, our model can be used to generate realistic
time-lapse landscape videos with moving objects and time-of-the-day changes.
Furthermore, by fitting the learned models to a static landscape image, the
latter can be reenacted in a realistic way. We propose simple but necessary
modifications to StyleGAN inversion procedure, which lead to in-domain latent
codes and allow to manipulate real images. Quantitative comparisons and user
studies suggest that our model produces more compelling animations of given
photographs than previously proposed methods. The results of our approach
including comparisons with prior art can be seen in supplementary materials and
on the project page this https URL

本文利用模型 StyleGAN 对风景视频进行训练，生成真实的时间变化风景视频，并通过拟合到静态风景图像中实现再现，同时提出简单的 StyleGAN 反演过程改进，生成比以前更引人注目的风景动画。