Application Research On Quantitative Prediction Of Mineral Resources Of Non-Negative Matrices Factorization Algorithm Based On GIS

Quantitative prediction of mineral resources is the product of the combination of geology, mathematics, information and computer technology. It builds the quantitative relationship between mineral resources and geological conditions in order to make the prediction of mineral resources more objective and accurate and efficiency dramatically increased. Meanwhile, quantitative prediction extends and further specifies of qualitative prediction. It has become the trend of predictable resources. With the increasing difficulty of the discovery of mineral resources and the development of modern scientific theories and technological methods, GIS technology application becomes a new evaluation method of mineral resource. Introduction the GIS to geological prospecting industry is an important way to make full use of existing data, extract potential information and develop the level of mineral resources prediction.Theory of non-negative matrix factorization (Non-Negative Matrix Factorization, NMF) is a matrix decomposition method proposed in recent years. It increases the non-negative constraints to ensure that the data matrix decomposition characteristics of non-negative and non-negative result are more easily to be explained. Moreover, NMF algorithm is simple and easy to implement and it has features such as dimension-lowering and sparse convergence. Therefore, it has been widely used in many fields.In this article, the sparse non-negative matrix factorization algorithm is applied to quantitative predict the mineral resources. The principle and application seditions of non-negative matrix factorization algorithm are discussed. Based on the integrated function of GIS space information and 1:200,000 scale information of geology, geophysical and geochemical in the east of Inner Mongolia, the sparse non-negative matrix factorization algorithm is adopt to quantitatively predict 1:200,000 scale silver mineral prediction in the east of Inner Mongolia. The algorithm realizes the sparse of original data and saves the storage space. Main features of the matrix vectors are extracted by using the sparse non-negative matrix factorization algorithm. The results relatively conform to reality. Comparative analysis of the results of cluster analysis, the weighted abundance estimate algorithm and sparse non-negative matrix factorization algorithm is given. The results show that the predictions of the three methods have consistency.Applying the sparse non-negative matrix factorization algorithm to predict the geology and mineral resources can get a better result.