Deep learning for seismic velocity estimation and mapping from sparse data in underground environment

Characterizing rocks around underground engineered structures without drilling requires weakly intrusive sensing and methods able to exploit sparse, non-conventional seismic data. This constraint is particularly strong in High Level Waste (HLW) cells, where sensor placement is limited by design requirements and by the need to preserve the surrounding rock, resulting in reduced illumination and data that are not well suited to standard data processing. To explore alternatives, a learning-based inversion framework was developed using stochastic velocity models, finite-difference simulations and spectral conditioning. A U-Net architecture trained on numerous synthetic datasets reconstructs coherent velocity maps despite sparse geometries, illustrating both the potential and the current limitations of deep learning approaches for such constrained underground acquisitions.