摘要
岩爆的发生受很多因素的影响,K-最近邻是一种简单易行且性能优秀的基于案例的机器学习推理技术.本文提出了基于K-最近邻案例推理的岩爆预测的新方法,该方法通过扫描深部开采中岩爆案例数据库,根据实例近邻性相似度函数,搜索出与预测事件在岩爆发生影响因素上最相似的多个岩爆实例,最后使用投票表决的方式推理预测新的复杂环境下岩爆发生的可能性.南非深部矿山的工程应用研究结果表明,该岩爆预测方法是科学可行的,容易实现且预测精度高,具有良好的应用前景.
Because of poor understanding about the mechanism of rockbust and about the effect factors, it is very difficult to give an accurate prediction using conventional methods. Aiming at this problem, we proposed a new method based on K-Nearest Neighbor case reasoning technology, which is one of the simplest and most effective tools in the field of machine learning. First, the effect factors of rockbust and instance histories were collected and input to the data base. Then, instance histories whose effect factors similar to new instance were selected through scanning the data based on the neighbor similarity function. Finally, rockburst risk of the new instance could be recognized by votes of instance histories selected. The results of prediction for mining induced rockburst at great depth in South African show that this method is feasible and reliable for rockburst prediction with high precision. The method will be attractive for a wide range of application in deep mining engineering.
出处
《采矿与安全工程学报》
EI
北大核心
2008年第1期63-67,共5页
Journal of Mining & Safety Engineering
基金
中国科学院岩土力学重点实验室开放研究基金项目(Z110601)
广西大学科研基金项目(X017019)
关键词
岩爆
采矿
K-最近邻方法
机器学习
案例推理
rockburst
mining
K-Nearest Neighbor method
machine learning
case reasoning
