Geophysical Research of Boreholes: Artificial Neural Networks Data Analysis

被引：0

作者：

Muhamedyev, R. I. ^{[1
]}

Kuchin, Y. I. ^{[1
]}

Muhamedyeva, E. L. ^{[1
]}

机构：

[1] Int IT Univ, Comp Sci Software Engn & Telecommun Dept, Alma Ata, Kazakhstan

来源：

6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS | 2012年

关键词：

geophysical research of boreholes; artificial neural network; uranium deposit; pre-processing data; normalization; smoothing; LOGS; LITHOLOGY;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The economic indicators of the mining process depend on the speed and accuracy of geophysical data interpretation, but the process of logging data interpretation can not be strictly formalized. Therefore, computer interpretation methods on the basis of expert estimates are necessary, such as artificial neural networks (ANN) which have already been used for solving a wide range of recognition problems. The paper analyzes the quality of network's data interpretation essentially depending on its configuration parameters, methods of data preprocessing and learning samples. About 2000 calculation experiments have been made, software and templates for pre-processing of data and interpretation findings have been developed. These experiments showed the effectiveness of neural network approach to solving the problem of geological rocks recognition in stratum-infiltration uranium deposits. Further research in this area will raise the recognition process automation and its accuracy.

引用

页码：825 / 829

页数：5

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