We develop a sensitivity-guided, surrogate-assisted Bayesian framework to infer fracture network parameters from elastic waves. Synthetic fracture networks characterized by power-law length exponent (Formula presented.), fracture density (Formula presented.), and percolation parameter (Formula presented.) are constructed. Elastic wave propagation through these fracture networks is then simulated across a range of dimensionless specific stiffness values (Formula presented.). From 2560 Monte Carlo simulation runs, we extract two wave transport metrics: the inverse quality factor (Formula presented.) and the normalized transmitted energy (Formula presented.). Distance-based generalized sensitivity analysis in the (Formula presented.) space reveals stiffness-dependent wave transport regimes (propagation, superdiffusion, normal diffusion, subdiffusion, and localization) and quantifies the contributions of parameters (Formula presented.), (Formula presented.), (Formula presented.), and (Formula presented.) in each regime. A random forest surrogate for the mapping of (Formula presented.) is then embedded in a Metropolis-Hastings scheme to perform Bayesian inversion of fracture network parameters. When the dimensionless stiffness (Formula presented.) is near 1, both (Formula presented.) and (Formula presented.) are reliably recovered, with posterior probabilities for the true values well above their uniform priors. For large (Formula presented.) (corresponding to the propagation and superdiffusion regimes), the fracture stiffness itself becomes highly identifiable, and complementary inversions in which (Formula presented.) is treated as unknown show that it can be robustly recovered from (Formula presented.) in these regimes. For small (Formula presented.) (subdiffusion and localization regimes), multiple scattering prevails, and all parameters become more weakly resolved. Our results demonstrate that wave attenuation and energy metrics can be used to jointly invert fracture stiffness and network geometry, provided that the inversion targets wavefield regimes where these parameters are most sensitive.

Geothermal energy extraction through deep mine systems offers the potential to reduce the cost of geothermal systems while meeting the cooling needs of deep mines. However, the injection of cold water into the subsurface triggers strongly coupled thermo-hydro-mechanical (THM) processes that can affect the stability of underground excavations. This study evaluates the impact of geothermal energy extraction on the temperature and stability of a deep mine. By quantifying the sensitivity of the mine temperature and stability to various parameters, we propose a scheme to optimize geothermal energy production, while achieving rapid mine cooling and maintaining stability. We first evaluate the impact of geothermal operations on mine temperature and stability through THM numerical modeling. The simulations show that poro-elastic stress quickly affects mine stability, while thermal stress has a more significant impact on the long-term stability. We then use Distance-based Generalized Sensitivity Analysis (DGSA) to quantify parameter sensitivity. The analysis identifies the distance between the mine system and the geothermal system as the most influential factor. Other important parameters include the injection rate, injection temperature, well spacing, coefficient of thermal expansion, permeability, Young's modulus, and heat capacity. Finally, we propose a DGSA-based optimization framework that accounts for subsurface uncertainty and validate the optimized results. Our results indicate that, with favorable geological conditions, a rational selection of system design parameters can enhance geothermal energy production while ensuring rapid mine cooling and stability. This study provides essential insights for the optimization of deep mine geothermal systems and supports effective decision-making.