Integrating artificial neural networks and geostatistics for optimum 3D geological block modeling in mineral reserve estimation: A case study

Abu Bakarr Jalloh, Kyuro Sasaki, Yaguba Jalloh, Abubakarr Karim Barrie

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

In this research, a method called ANNMG is presented to integrate Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation. The word ANNMG simply means Artificial Neural Network Model integrated with Geostatiscs. In this procedure, the Artificial Neural Network was trained, tested and validated using assay values obtained from exploratory drillholes. Next, the validated model was used to generalize mineral grades at known and unknown sampled locations inside the drilling region respectively. Finally, the reproduced and generalized assay values were combined and fed to geostatistics in order to develop a geological 3D block model. The regression analysis revealed that the predicted sample grades were in close proximity to the actual sample grades. The generalized grades from the ANNMG show that this process could be used to complement exploration activities thereby reducing drilling requirement. It could also be an effective mineral reserve evaluation method that could produce optimum block model for mine design.

Original languageEnglish
Pages (from-to)581-585
Number of pages5
JournalInternational Journal of Mining Science and Technology
Volume26
Issue number4
DOIs
Publication statusPublished - Jul 1 2016

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All Science Journal Classification (ASJC) codes

  • Geotechnical Engineering and Engineering Geology

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