Statistic Analysis of Mineral Source in Data mining Using Hadoop Analysis

Computer engineering

Property of the particular and money related data of mining ventures both are multi-dimensionality and nonlinearity. The business value information of mineral items is a vital financial pointer of mining endeavors, and the geological information is essential specialized information. The examination methodology for specialized and monetary information is inquired about utilizing advances of Hadoop and data mining. There is change design and affecting parameters of the mineral items cost are examined. ANN is use to determine the mineral product prizing based on prediction model. The outcomes show that the practicability of the expectation show is solid, and the forecast accuracy is high. During the process of mineral development, due to the limitation of technical conditions and equipment conditions, lots of geological data have been lost, which reduces the accuracy of the orebody shape and that of reserves approximation. Depend on techniques of geostatistics and artificial neural network, the prediction model of the land or geological missing data is established. By using the forecast model, the regularity of geological data of single borehole, group boreholes and all boreholes s talked about and dissected. This paper depicts the literature analysis of prediction of geological missing data of mineral products and their cost and results of prediction and interpolation are reliable.

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