东北大学学报（自然科学版）

东北大学学报:自然科学版 ›› 2016, Vol. 37 ›› Issue (3): 408-412.DOI: 10.12068/j.issn.1005-3026.2016.03.022

• 资源与土木工程 • 上一篇    下一篇

基于多基因遗传规划的矿石品位估计

韩创益1,2， 王恩德1， 夏建明1， 李光秀2   

  1. (1. 东北大学 资源与土木工程学院， 辽宁 沈阳110819; 2. 金策工业综合大学 资源勘探工程学院， 平壤999093)
  • 收稿日期:2015-05-22 修回日期:2015-05-22 出版日期:2016-03-15 发布日期:2016-03-07
  • 通讯作者: 韩创益
  • 作者简介:韩创益(1980-)，男，朝鲜平壤人，东北大学博士研究生; 王恩德(1957-)，男，辽宁盖州人，东北大学教授，博士生导师.
  • 基金资助:
    国家重点基础研究发展计划项目(2012CB416800)； 国家自然科学基金资助项目(41372098).

Ore Grade Estimation Based on Multi-gene Genetic Programming

HAN Chang-ik1,2， WANG En-de1， XIA Jian-ming1， LI Gwang-su2   

  1. 1. School of Resources & Civil Engineering， Northeastern University， Shenyang 110819， China; 2. College of Geoexploration Engineering， Kimchaek University of Technology， Pyongyang 999093， DPRK.
  • Received:2015-05-22 Revised:2015-05-22 Online:2016-03-15 Published:2016-03-07
  • Contact: HAN Chang-ik
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摘要： 由于矿床形成过程复杂、控制因素多，导致估计矿石品位相对困难.尽量降低矿床预测中的估计误差对矿产资源的开发和利用是至关重要的.克立格法被认为是最佳的品位估计方法，其必须满足对于品位空间分布的平稳性和内蕴假设.但实践上，大部分的品位数据具有稀疏、不规则而复杂的空间分布，这有时会导致克立格法违反平稳性和内蕴假设.本文提出基于多基因遗传规划的矿石品位估计方法，并将其与克立格法进行对比.结果显示，基于多基因遗传规划的方法不需要关于空间分布的假设.这样，简化了实施矿体品位预测的条件，并能取得较好的预测结果，可应用于复杂矿体品位的预测.

关键词: 矿石品位估计, 多基因遗传规划, 普通克立格, 矿床预测, 人工智能

Abstract: Ore grade estimation is relatively difficult due to the complexity of ore deposit formation process and numerous control factors. Evaluation of ore deposit with low estimation error is crucial in mineral resources development and usage. So far， Kriging， now known as a best estimation method of grade， is based on intrinsic assumption and stationarity about the underlying grade spatial distribution. However， most of ore grade data are spatially sparse， irregularly spaced and have complex distribution， which could result in the Kriging estimation method violating intrinsic assumption and stationarity. This article presented a new method for ore grade estimation based on multi-gene genetic programming and also compared it with ordinary Kriging. The results show that the proposed method makes no assumptions about the spatial distribution of grade data， the condition of implementing ore body grade prediction is simplified， and it can achieve better prediction effect. So， the proposed method can be used to estimate ore grade for complex ore deposit.

Key words: ore grade estimation, multi-gene genetic programming(MGGP), ordinary Kriging, ore deposit prediction, artificial intelligence

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