  We introduce a new approach using computer vision to predict the land surface
displacement from subsurface geometry images for Carbon Capture and
Sequestration (CCS). CCS has been proved to be a key component for a carbon
neutral society. However, scientists see there are challenges along the way
including the high computational cost due to the large model scale and
limitations to generalize a pre-trained model with complex physics. We tackle
those challenges by training models directly from the subsurface geometry
images. The goal is to understand the respons of land surface displacement due
to carbon injection and utilize our trained models to inform decision making in
CCS projects.
  We implement multiple models (CNN, ResNet, and ResNetUNet) for static
mechanics problem, which is a image prediction problem. Next, we use the LSTM
and transformer for transient mechanics scenario, which is a video prediction
problem. It shows ResNetUNet outperforms the others thanks to its architecture
in static mechanics problem, and LSTM shows comparable performance to
transformer in transient problem. This report proceeds by outlining our dataset
in detail followed by model descriptions in method section. Result and
discussion state the key learning, observations, and conclusion with future
work rounds out the paper.
