Deep learning-assisted THM-integrated InSAR modeling for CO2 storage characterization and surface deformation forecasting

Accurate characterization of subsurface reservoirs in geological carbon storage (GCS) is essential for ensuring long-term storage security and mitigating leakage risks. This study proposes a novel CO₂ reservoir characterization framework that integrates InSAR-based surface deformation data with a deep learning-based method (pix2pix) to predict subsurface properties, such as rock facies and porosity. To assess the surface deformation before and after CO₂ injection, a THM (thermal-hydrological-mechanical) simulation is employed, and their corresponding results are used as input for the suggested pix2pix-based model. To reveal the robustness of the suggested workflow, sensitivity analysis is conducted by varying signal-to-noise ratio (SNR) of InSAR data and observation time periods, assessing their impact on characterization performance. Furthermore, the model is applied for long-term CO₂ plume and surface deformation predictions, enabling uncertainty quantification of future behavior.

The results show that early-stage observation data provide rich subsurface information but are highly sensitive to noise, whereas later observations exhibit greater tolerance to noise but reduced information content. The suggested workflow effectively predicts long-term CO₂ plume migration and surface deformation trends, demonstrating its applicability for reservoir monitoring. This study demonstrates that integrating InSAR-based surface deformation data with deep learning significantly improves CO₂ reservoir characterization. The findings highlight the importance of optimizing InSAR acquisition frequency and noise-handling strategies to enhance monitoring accuracy. The proposed approach provides a foundation for developing time-series-based reservoir characterization models using surface deformation data.