Resource Assessment with Uncertainty Quantification of Intrusive Orebodies Using Level Sets with Stochastic Motion: Application to a Shear-Hosted Copper Deposit

Mining project evaluation depends on geological resource models, yet these models remain inherently uncertain because they are constructed from sparse drillhole data, indirect geophysical observations, and incomplete geological knowledge. Conventional workflows typically treat this uncertainty only partially by defining deterministic orebody wireframes and then interpolating or simulating grades within these fixed boundaries. This separation neglects the propagation of geometric uncertainty into grade continuity, resource tonnage, and economic forecasts. The challenge is particularly significant where drillholes do not fully intersect the orebody, leaving its extent at depth unconstrained and forcing boundary placement to rely on extrapolation rather than data-supported inference. To overcome this limitation, we present a sequential uncertainty quantification framework that integrates level-set implicit geological modeling within a Markov Chain Monte Carlo (MCMC) sampler with Sequential Gaussian Simulation (SGSIM) grades. Orebody geometry is represented using a signed distance function perturbed by Gaussian random fields and constrained by drillhole, outcrop, and geological interpretation data. The resulting ensemble captures plausible geometric variability, particularly in poorly constrained regions. Conditional grade simulations are then generated for each accepted geometry, producing paired realizations of geometry and grade uncertainty. Applied to the Tarmante copper deposit in Morocco, the framework demonstrates that deterministic models overestimate tonnage, whereas the joint ensemble provides realistic grade–tonnage uncertainty, enabling more reliable resource evaluation and risk-informed decision-making.