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With the growing global deployment of carbon capture and sequestrationtechnology to combat climate change, monitoring and detection of potential CO2leakage through existing or storage induced faults are critical to the safe andlong-term viability of the technology. Recent work on time-lapse seismicmonitoring of CO2 storage has shown promising results in its ability to monitorthe growth of the CO2 plume from surface recorded seismic data. However, due tothe low sensitivity of seismic imaging to CO2 concentration, additionaldevelopments are required to efficiently interpret the seismic images forleakage. In this work, we introduce a binary classification of time-lapseseismic images to delineate CO2 plumes (leakage) using state-of-the-art deeplearning models. Additionally, we localize the leakage region of CO2 plumes byleveraging Class Activation Mapping methods.

De-risking Carbon Capture and Sequestration with Explainable CO2 Leakage Detection in Time-lapse Seismic Monitoring Images