摘要
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.
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 oroduce optimum block model for mine design.
基金
the management of Sierra Rutile Company for providing the drillhole dataset used in this study
the Japanese Ministry of Education Science and Technology (MEXT) Scholarship for academic funding
