• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文学位 >Application of pattern recognition techniques to monitoring-while-drilling on a rotary electric blasthole drill at an open-pit coal mine.
【24h】

Application of pattern recognition techniques to monitoring-while-drilling on a rotary electric blasthole drill at an open-pit coal mine.

机译：模式识别技术在露天煤矿旋转电爆孔钻机随钻监控中的应用。

获取原文
获取原文并翻译 | 示例
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This thesis investigates the application of pattern recognition techniques to rock type recognition using monitoring-while-drilling data. The research is focused on data from a large electric blasthole drill operating in an open-pit coal mine. Pre-processing and normalization techniques are applied to minimize potential misclassification issues. Both supervised and unsupervised learning is employed in the classifier design: back-propagation neural networks are used for the supervised learning, while self-organizing maps are used for unsupervised learning. A variety of combinations of drilling data and geophysical data are investigated as inputs to the classifiers. The outputs from these classifiers are evaluated relative to the rock classification made by a commercially available rock type recognition system, as well as relative to independent labelling by a geologist. Classifier performance is improved when drilling data used as inputs are augmented with geophysical data inputs. By using supervised learning with both drilling and geophysical data as inputs, the misclassification of coal, as well as of the non-coal rock types, is reduced compared to results of current commercial recognition methods. Moreover, rock types which were not detected by the previous methods were successfully classified by the supervised models.
机译：本文研究了模式识别技术在随钻监测数据在岩石类型识别中的应用。这项研究的重点是在露天煤矿中运行的大型电爆孔钻的数据。应用了预处理和规范化技术以最小化潜在的分类错误问题。分类器设计中同时使用了监督学习和无监督学习：反向传播神经网络用于监督学习，而自组织映射则用于无监督学习。研究了钻井数据和地球物理数据的各种组合，作为分类器的输入。这些分类器的输出是相对于由市售岩石类型识别系统进行的岩石分类以及相对于地质学家的独立标记而言的。当使用地球物理数据输入增强用作输入的钻井数据时，可提高分类器的性能。与当前的商业识别方法相比，通过在钻探和地球物理数据作为输入的情况下使用监督学习，可以减少煤以及非煤岩类型的错误分类。此外，通过监督模型成功地分类了先前方法未检测到的岩石类型。

著录项

  • 作者

    Martin Gonzalez, Jorge Eduardo Jose.;

  • 作者单位

    Queen's University (Canada).;

  • 授予单位 Queen's University (Canada).;
  • 学科 Engineering Mining.
  • 学位 M.Sc.
  • 年度 2008
  • 页码 213 p.
  • 总页数 213
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 矿业工程;
  • 关键词

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号