De-risking GCS projects with explainable CO$_2$ leakage detection in time-lapse seismic images

With the global deployment of Carbon, capture and storage (CCS) technology to combat climate change, there is an associated risk of contamination with CO2 leaking back to the atmosphere. Thus, it requires continuous monitoring of CO2 after the injection stops at the storage site. In this work, we generated synthetic CO2 plume development data with both leakage and no leakage scenarios. We trained a convolutional neural network (CNN) discriminative classifier and also a generative classifier and compared their performances in CO2 leakage detection. The accuracy of our discriminative classifier on the test data is 85% and that of the generative classifier is 90%. The Class Activation Mapping (CAM) results of the discriminative classifier and the latent space representation of our dataset in the case of generative classifier strengthens our claims about trustworthy leakage classification.