img

Wechat

Adv. Search

Gold Science and Technology ›› 2015, Vol. 23 ›› Issue (5): 53-59.doi: 10.11872/j.issn.1005-2518.2015.05.053

Previous Articles     Next Articles

A BP Neural Network Based Method for Geological Missing Data Processing

ZHANG Lingling1,LI Guoqing1,KANG Kuangsong1,2,LI Wei3,HU Nailian1   

  1. 1.School of Civil and Environment Engineering,University of Science and Technology Beijing,Beijing    100083,China;
    2.Kim Chaek University of  Technology,Pyongyang    999093,D.P.R.K.;
    3.Sanshandao Gold Mine,Shandong Gold Mining (Laizhou) Co.,Ltd.,Laizhou    261400,Shandong,China
  • Received:2015-06-10 Revised:2015-07-10 Online:2015-10-28 Published:2015-12-09

Abstract:

In the process of geological exploration,due to the limitation of technical and equipment objective conditions,there are lots of basic geological data missing.It causes that the geological data is not complete and accurate as building the deposit model,and has a direct impact on the accuracy of the orebody shape and reserves estimation.In order to provide the complete and believable data,so that the deposit model will be more realistic.Firstly the generation mechanism of geological missing data is studied to find out the method which geological missing data obeys.By means of comparing and analyzing the features and applicable conditions of Expectation Maximization(EM) algorithm,Markov Chain Monte Carlo(MCMC) method and Back Propagation(BP) Neural Network,then an interpolation method of geological missing data which based on BP neural network is selected and introduced,and the relative model of processing geological missing data is built up.Finally the whole method is applied in a certain gold mine in Shandong.It has been proved that the model can achieve interpolation of most of the geological missing data,and the results are reliable.In short,it is feasible and effective using the model to solve the integrity problem of geological data caused by basic data missing.

Key words: geological missing data, data interpolation, BP Neural Network, EM algorithm, MCMC method

CLC Number: 

  • P628

[1] 陈国旭.传统资源储量估算信息化研究现状及发展方向[J].金属矿山,2013,42(5):105-109.
[2] 周坤,郑立明.最近距离法在贵金属矿体圈定中的应用——以南非某层状铂矿为例[J].黄金科学技术,2013,21(3):55-58.
[3] 王志宏,陈应显.基于三维可视化矿床模型的断层整体插值方法[J].煤炭学报,2003,28(6):569-572.
[4] 于占秋,杨钦,自润才,等.基于多面函的煤层界面插值及其可视化研究[J].露天采矿技术,2012,(4):47-52.
[5] 佟昕,高强.统计学中的数据缺失及解决方法[J].辽宁经济职业技术学院学报,2011,(2):15-16.
[6] 庞新生.缺失数据插补处理方法的比较研究[J].统计与决策,2012,(24):18-22.
[7] 张亚萍,胡学钢,方振国,等.数据缺失条件下的贝叶斯优化算法[J].计算机工程与应用,2012,48(11):111-114.
[8] 庞新生.缺失数据处理中相关问题的探讨[J].统计与信息论坛,2004,19(5):29-32.
[9] 杨利华.缺失数据的处理方法研究及应用[D].景德镇:景德镇陶瓷学院,2011.
[10] Li X Y,Yuan J Q.Empirical likelihood method for quantities with response data missing at random[J].Acta Mathematicae Applicatae Sinica,2012,28(2):265-274.
[11] 沐守宽,周伟.缺失数据处理的期望—极大化算法与马尔可夫蒙特卡洛方法[J].心理科学进展,2011,19(7):1083-1090.
[12] 汪伟,罗周全,王益伟,等.金属矿山采空区危险性辨识的遗传BP模型研究[J].中国安全科学学报,2013,23(2):39-44.
[13] 胡勇辉,刘连生.基于BP神经网络的沉积岩型矿山爆破开采成本的预测与控制模型[J].中国矿业,2013,22(11):75-79,87.
[14] 张钦礼,李谢平,杨伟.基于BP网络的某矿山充填料浆配比优化[J].中南大学学报:自然科学版,2013,44(7):2867-2874.
[15] 黄永恒,曹平,汪亦显.基于BP神经网络的岩土工程预测模型研究[J].科技导报,2009,27(6):61-64.
[16] Zhang Z G.Learning algorithm of wavelet network based on sampling theory[J].Neurocomputing,2007,71(1/3):244-269.
[17] 周福宝,李金海.煤矿火区启封后复燃预测的BP神经网络模型[J].采矿与安全工程学报,2010,27(4):494-498,504.
[18] 苗明义.露天矿最终边坡角的BP神经网络预测及数值模拟验证[J].有色金属(矿山部分),2013,65(3):70-74.
[19] 王晓敏,刘希玉,戴芬.BP神经网络预测算法的改进及应用[J].计算机技术及发展,2009,19(11):64-67.
[20] 陈杰,杨志强,高谦,等.铜渣—棒磨砂混合充填料的胶砂配比试验研究[J].有色金属(矿山部分),2015,67(1):54-58.
[21] 张德丰.MATLAB神经网络仿真与应用[M].北京:电子工业出版社,2009:177.
[22] 朱川曲.回采巷道围岩稳定性分类及松动圈尺寸预测[J].黄金科学技术,1999,7(4/5):63-66.
[23] 鲁挑建.再论甘肃马泉金矿矿石品位分布特征及品位预测[J].黄金科学技术,2011,19(2):1-7.
[24] 刘甸瑞,刘刚.拟合三参数对数正态分布时第三参数的优选方法探讨[J].地质与勘探,1995,31(5):43-47.

Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!