53 DGSA analysis is complete; until then, the workflow has established the framework within which those recommendations will be made. More broadly, this study contributes to the World Mining Congress 2026 theme of delivering critical minerals faster, smarter, and more responsibly. Conventional mine hydrogeological practice produces single deterministic models that risk underestimating uncertainty, and that may fail when operational conditions diverge from the calibrated case. The Popper-Bayes approach applied here is intended to be faster because it directs site characterisation toward the parameters that matter most; smarter because it tests competing model conceptualisations explicitly rather than relying on a single calibrated base case; and more responsible because it quantifies and communicates uncertainty to decision makers rather than concealing it within a deterministic forecast. The methodology is transferable to other preoperational fractured-rock mining contexts where dewatering data does not yet exist but where some form of pre-development hydrogeological monitoring is available. The immediate next steps are application of DGSA to the existing 100-realisation ensemble, scaling to a larger ensemble for the formal analysis, and execution of the falsification test against observed piezometric data. The findings of those stages will be reported in subsequent work. ACKNOWLEDGEMENTS We gratefully acknowledge KoBold Metals for access to site data and ongoing collaboration on the Mingomba project. REFERENCES Athens, N. D., & Caers, J. K. (2019). A Monte Carlo-based framework for assessing the value of information and development risk in geothermal exploration. Applied Energy, 256, 113932. Caers, J. (2018). Bayesianism in the geosciences. In B. S. Daya Sagar, Q. Cheng, & F. Agterberg (Eds.), Handbook of Mathematical Geosciences (pp. 527-566). Springer. Fang, J., Gong, B., & Caers, J. (2022). Data-driven model falsification and uncertainty quantification for fractured reservoirs. Engineering, 18, 116-128. Park, J., Yang, G., Satija, A., Scheidt, C., & Caers, J. (2016). DGSA: A Matlab toolbox for distance-based generalized sensitivity analysis of geoscientific computer experiments. Computers & Geosciences, 97, 15-29. Scheidt, C., Li, L., & Caers, J. (2018). Quantifying Uncertainty in Subsurface Systems. Wiley.
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