Title :
Application of neural network model for ore boundary delineation based on geophysical logging data
Author :
Huang, Yi ; Wanstedt, Stefan
Author_Institution :
Div. of Min. Eng., Lulea Univ. of Technol., Sweden
Abstract :
In a mining operation, knowledge regarding the ore boundary is extremely important. Mining cost and ore quality largely depend on this information. The conventional technique to get this information is diamond core drilling. The disadvantages of this technique are that it is very expensive and time consuming. In recent years, geophysical logging has been introduced to the mining industry to get this ore boundary information. However, effective interpretation to delineate the ore boundary from the geophysical logging data is still a problem. In this paper, a back propagation neural network model is applied to delineation of the ore boundary based on borehole 4 geophysical parameters logging data in a Swedish underground mine. Three boreholes geophysical logging data was tested for ore boundary delineation purpose. The result from the neural network model about the ore boundary delineation is encouraging and much better than the existing geophysical logging data interpretation techniques
Keywords :
minerals; Swedish underground mine; back propagation neural network model; backpropagation; borehole 4 geophysical parameters logging data; diamond core drilling; geophysical logging data; mining cost; ore boundary delineation; ore quality; Costs; Data engineering; Data mining; Drilling; Electronic mail; Knowledge engineering; Mining industry; Neural networks; Nonlinear dynamical systems; Ores;
Conference_Titel :
Neural Networks, 1996., IEEE International Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-3210-5
DOI :
10.1109/ICNN.1996.549234
