  Monitoring stored CO2 in carbon capture and storage projects is crucial for
ensuring safety and effectiveness. We introduce DeepNRMS, a novel noise-robust
method that effectively handles time-lapse noise in seismic images. The
DeepNRMS leverages unsupervised deep learning to acquire knowledge of
time-lapse noise characteristics from pre-injection surveys. By utilizing this
learned knowledge, our approach accurately discerns CO2-induced subtle signals
from the high-amplitude time-lapse noise, ensuring fidelity in monitoring while
reducing costs by enabling sparse acquisition. We evaluate our method using
synthetic data and field data acquired in the Aquistore project. In the
synthetic experiments, we simulate time-lapse noise by incorporating random
near-surface effects in the elastic properties of the subsurface model. We
train our neural networks exclusively on pre-injection seismic images and
subsequently predict CO2 locations from post-injection seismic images. In the
field data analysis from Aquistore, the images from pre-injection surveys are
utilized to train the neural networks with the characteristics of time-lapse
noise, followed by identifying CO2 plumes within two post-injection surveys.
The outcomes demonstrate the improved accuracy achieved by the DeepNRMS,
effectively addressing the strong time-lapse noise.
