985 Table 1 — Prior uncertainty distributions for matrix permeability by stratigraphic unit # Stratigraphic Unit Base (mD) Distribution Min (mD) Max (mD) 1 Petit Conglomerate 100 Uniform 10 1000 2 Nguba Undifferentiated 120 Uniform 12 1200 3 Mwashya Member 2.25 Uniform 1.3 8.06 4 Upper Roan Breccia 1.2 Uniform 0.12 12 5 Bancroft Dolostone 16.5 Uniform 1.65 165 6 Bancroft SWG 76 Uniform 26 126 7 Konkola Conglomerate 126 Uniform 12.6 1260 8 Chambishi Member 0.009 Uniform 0.0009 0.09 9 Nchanga Member 0.115 Uniform 0.0003 0.23 10 Rhokana Member 130 Uniform 1.8 1000 11 Copperbelt Orebody Member 12 Uniform 1.2 120 12 Mindola Clastics Formation 4 Uniform 0.02 35 Table 2 — Prior uncertainty distributions for regional aquifer recharge boundaries # Recharge Source Base (m³/d) Distribution Min (m³/d) Max (m³/d) 1 Lubambe analogue (NW) 150,000 Uniform 100,000 200,000 2 Konkola analogue (SE) -300,000 Uniform -500,000 -100,000 3 Distributed (Elsewhere) 30,000 Uniform 10,000 80,000 3.3 Monte Carlo simulation and dewatering scenario An initial ensemble of one hundred Monte Carlo realisations has been generated by independently sampling each parameter from its prior distribution. For each realisation, a transient groundwater flow simulation was run over a five-year horizon representing the planned dewatering scenario. The dewatering layout consists of eight wells positioned on a 300 m radius around the planned shaft location, with operating conditions held identical across all realisations so that variability in predicted pressure response can be attributed entirely to parameter uncertainty rather than to operational variability. Predicted pore pressure has been extracted at multiple monitoring locations distributed across the deposit and at multiple time steps spanning the full simulation horizon. The resulting high-dimensional ensemble of simulated pressure responses provides the basis for the planned global sensitivity analysis. The current ensemble of one hundred realisations is intended as a preliminary stage to validate the workflow and confirm computational feasibility; the formal analysis will scale to a larger ensemble (on the order of one thousand realisations) consistent with established practice in subsurface uncertainty quantification studies (Fang et al., 2022). 3.4 Distance-based generalised sensitivity analysis The next stage of the workflow will apply distance-based generalised sensitivity analysis (DGSA; Park et al., 2016) to the simulated ensemble to identify which uncertain parameters most strongly control predicted pore pressure evolution. DGSA partitions parameter samples by clustering the corresponding response variables and computes the distance between conditional and prior cumulative distributions for each parameter. The method accommodates uniform, scenario-based, and high-dimensional response variables that conventional variance-based sensitivity methods handle poorly. Sensitivity values above unity will
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