4th WCSET-2015 at Japan

 

Civil Engineering:

Title:           Integrating artificial neural networks and geostatistics for optimum 3D geological block modelling in mineral reserve estimation – a case study
Authors:      Abu Bakarr Jalloh, Kyuro Sasaki
Abstract:     In this research, a procedure integrating Artificial Neural Networks and Geostatistics for optimum mineral reserve evaluation is presented. The method called ANNMG (Artificial Neural Network Model integrated with Geostatiscs) adopted the following approach: firstly, the Artificial Neural Net was trained, tested and validated using assay values obtained from exploratory drillholes from the studied area. The validated model was then used to generalize mineral grades at known sampled and unknown locations inside the drilling region respectively. Lastly, the reproduced and generalized assay values were combined and fed to geostatistics for developing a geological 3D block model. Regression analysis reveals that the reproduced and predicted grades by the trained neural network were in close proximity to the actual samples. The generalized grades from the ANNMG show that this procedure could be used to complement exploration activities thereby reducing drilling requirement.

Keywords:   Artificial Neural Network Model with Geostatistics (ANNMG); 3D geological block modelling; and Mine Design; Kriging
Pages:          201-205

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