Hybrid machine learning and factor-based simulation for geostatistical modelling of residual resources in a tailings storage facility
Tileugabylov A. Madani N. Maleki M. Parviainen A.
2025Taylor and Francis Ltd.
International Journal of Mining, Reclamation and Environment
2025
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.
gaussian process regression , machine learning imputation , mineral resource evaluation , minimun/maximum autocorrelation factor , Tailings storage facility
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School of Mining and Geosciences, Nazarbayev University, Astana, Kazakhstan
Department of Metallurgical and Mining Engineering, Universidad Católica Del Norte, Antofagasta, Chile
Andalusian Earth Science Institute (IACT-CSIC), Spanish National Research Council, Armilla, Spain
School of Mining and Geosciences
Department of Metallurgical and Mining Engineering
Andalusian Earth Science Institute (IACT-CSIC)
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