Abstract
Mining tailings storage facilities (TSFs) contain significant residual mineral resources due to past processing limitations. As reprocessing becomes economically viable, accurately modelling spatial variability is crucial. Traditional cosimulation struggles with computational inefficiency and limitations of the Linear Model of Coregionalization. This study integrates machine learning-based imputation with Minimum/Maximum Autocorrelation Factor and turning bands simulation (MAF-TBSIM) to address uneven sampling. Using an Au-Cu TSF case study, Gaussian Process Regression yielded superior imputation performance. Compared to traditional methods, MAF-TBSIM offers improved spatial representation and efficiency, supporting favourable resource estimates and highlighting the reprocessing potential of TSFs