Application of BP neural network in the classification of geo-chemical survey data

As an calculation model with high non-linear mapping ability, BP neural network has excellent non-linear approximation. When dealing with geo-chemical survey data and mineral resources potential assessment, many problems have non-linear features. In forecast, ANN can establish a non-linear reflection relation between the input and output, then automatically simulate the natural relation between the various mineralization factors and carry out the whole optimal searching, thus reducing the human intervention and improving the accuracy of the resource forecast. BP neural network is easy to implement with strong concurrent characteristic. At present, it is the first choice in terms of forward network. In the paper, geo-chemical survey data of East Tianshan Mountain was applied to be tested. We use the typical gold deposit and copper deposit of East Tianshan Mountain to classify the deposit scale and deposit type. The result shows that improved BP network has good convergence and high study efficiency and it can ideally classify the geo-chemical survey data.