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《Progress in Geophysics》 2012-01
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Mineral target prediction based on Boltzmann machines

CHEN Yong-liang1,ZHOU Bin2,LI Xue-bin1(1.Institute of Mineral Resources Prognosis on Synthetic Information,Jilin University,Changchun 130026,China 2.College of Computer Science and Technology,Jilin University,Changchun 130012,China)  
Mineral target prediction is a nonlinear pattern recognition procedure for differentiating mineral target cells from a geological statistical cell set.This kind of nonlinear pattern recognition procedure can be implemented with Boltzmann machines due to that Boltzmann machines can encode and reconstruct external stimuli.In view of this,the authors construct a three-layered Boltzmann machine model for mineral target prediction.The three layers are called input layer,hidden layer and output layer,respectively.The number of neural cells in the input layer is same as the number of evidential map patterns.The output layer has only one neural cells.The number of neural cells in the hidden layer is defined by a user according to the resolution of mineral target prediction.A stochastic learning rule integrating Hebbian encoding and simulated anealing algorithms is applied to train the model.The connecting weight coefficients between the input layer and hidden layer of a trained model are used to compute the weights of evidential map patterns.The ore-bearing favorability of a statistical cell can be determined according to the weight coefficients of map patterns and the map pattern combination in the cell.The mineral target cells can be delineated according to their ore-bearing favorabilities.A VC++ program for raster data oriented mineral target prediction with Boltzmann machines has been developed on the basis of GDAL,a C++ library for the input and output of digital image data.The model has been experimentally applied to the mineral target prediction in Altay,northern Xinjiang.The experimental result illustrates that the predicted areas with high ore-bearing favourabilities coincide with the known mineral occurrences in the study area.Thus,the nonlinear prediction model based on Boltzmann machines is feasible for mineral target prediction.
【Fund】: 国家自然科学基金项目(41072244)资助
【CateGory Index】: P612
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