Document Type
Article
Publication Date
8-18-2026
Department
Department of Geological and Mining Engineering and Sciences
Abstract
Spatial variability and uncertainty associated with grades and rock types significantly impact mineral resource estimation and reserve determination. Conventional approaches generally simulate grades within deterministic geological domains, which may inadequately capture uncertainty and the gradual transition of grades across lithological contacts. Although joint simulation of categorical and continuous variables improves geological realism and uncertainty characterization, reserve estimation is still commonly performed using deterministic ultimate pit optimization methods that do not explicitly propagate geological uncertainty into mine planning decisions. This study presents an integrated framework that combines pluri-Gaussian simulation for joint modeling of rock types and grades with stochastic pit limit optimization for reserve estimation. Multiple equiprobable realizations are generated to capture uncertainty associated with both geological domains and grade distributions. The primary contribution of this work is the incorporation of these stochastic realizations directly into reserve calculations, allowing geological uncertainty to be propagated into economic mine planning decisions. Resource classification is performed using relative conditional variance, while Proven and Probable reserves are estimated through stochastic pit optimization. The methodology is demonstrated using a heterogeneous gold deposit in Alaska. Average grades for Measured and Indicated resources are 0.6142 and 0.5197 g/t, respectively. Stochastic pit optimization results show recoverable reserves with average grades of 0.6303 g/t for Proven reserves and 0.5442 g/t for Probable reserves. The results demonstrate that integrating joint simulation with stochastic reserve estimation provides a more realistic assessment of recoverable reserves and associated uncertainty compared with conventional deterministic approaches.
Publication Title
Mining Metallurgy and Exploration
Recommended Citation
Hlajoane, S.,
&
Chatterjee, S.
(2026).
Integrating Joint Geostatistical Simulation with Stochastic Pit Optimization for Mineral Resource and Reserve Estimation.
Mining Metallurgy and Exploration.
http://doi.org/10.1007/s42461-026-01672-3
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/2923
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© The Author(s) 2026. Publisher’s version of record: https://doi.org/10.1007/s42461-026-01672-3