Nazarbayev University’s School of Mining and Geosciences (SMG) was pleased to host Dr. Mohammad Maleki from Universidad Católica del Norte (Chile), who visited the School and delivered a guest lecture on the application of geostatistics and machine learning in ore body modelling and drillhole optimization.
During the lecture, Dr. Maleki presented modern approaches to integrating geostatistical methods with data-driven algorithms to improve the reliability of orebody models and support more efficient drillhole planning. He demonstrated how combining classical geostatistical tools with machine learning can reduce uncertainty, optimize sampling strategies, and ultimately enhance decision-making in mineral resource evaluation.
In addition to the public lecture, Dr. Maleki met with SMG faculty and researchers to discuss joint research directions. The discussions focused in particular on:
Developing advanced geostatistical methods for mine tailings characterization and resource estimation
Modeling uncertainty and spatial variability in tailings deposits
Exploring opportunities for sustainable re-use and re-processing of mine waste using data-centric approaches
These conversations highlighted the growing importance of responsible and sustainable mining, where geostatistics and machine learning play a key role in optimizing resource use and minimizing environmental impact.
SMG expresses its sincere gratitude to Dr. Mohammad Maleki for his visit, his insightful lecture, and the valuable exchange of ideas. The School looks forward to further collaboration in the fields of geostatistics, mine tailings research, and data-driven mining engineering.