Deep Learning for CO₂ Leakage Detection and Risk Assessment Rachel Mice
Keywords:
deep learning; carbon capture and storage; CO₂ leakage; seismic monitoring; risk assessment; physics-informed neural networks; anomaly detection; explainable AI; uncertainty quantificationAbstract
The viability of geological carbon dioxide (CO₂) storage as a climate mitigation strategy hinges on the reliable detection
of subsurface leakage and the quantitative assessment of associated risks. Conventional monitoring techniques — seismic
imaging, wellbore instrumentation, geochemical sampling, and remote sensing — produce voluminous, multi-modal datasets
whose manual interpretation is increasingly impractical. Deep learning, leveraging hierarchical feature extraction through
multi-layered neural architectures, has demonstrated significant promise in automating leakage detection, accelerating risk
quantification, and enabling real-time decision support. This review critically examines the application of deep learning to
CO₂ leakage monitoring and risk assessment within carbon capture and storage (CCS) projects.
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