摘要
论文基于多变量时间序列相空间重构来计算数据的关联维数,以研究冲击地压监测数据的复杂程度。考虑到冲击地压监测数据含有噪声而且长度有限,对传统G-P算法进行了扩展改进,给出了改进算法求解多变量时间序列的关联维数的原理,并用于Lorenz混沌系统检验了改进算法的有效性。收集了不同冲击情况下多种监测类型的冲击地压时间序列数据,用改进G-P算法求解这些监测数据的关联维数值。研究结果表明:冲击地压监测数据具有混沌特性,而且数据关联维数越大,复杂程度越高,对应矿井的冲击破坏性越强。这为基于混沌理论预测冲击危险性提供了新方法和依据。
This paper studies the complexity of the data to monitor Rock burst through computing correlation dimension based on phase-space reconstruction of multivariate time series. Given that Rock burst monitoring data had limited-length and contained noise, traditional G-P algorithm is extended and improved. The principle of improved G-P algorithm of solving correlation dimension of multivariate time series was provided, and algorithm was verified through employing it to Lorenz chaotic system. Then a mass of time- series data to monitor Rock burst were collected by diverse equipment under different burst degree, and their correlation dimensions were computed through improved G-P algorithm. The results demonstrate that the data have chaotic characteristic, and the larger correlation dimension is, the more complex monitoring data is, the stronger Rock-burst damage of corresponding coal mine is. Our achievement can give a novelty approach and basis to predict Rock burst risk based on chaos method.
出处
《中国矿业》
北大核心
2015年第10期118-122,共5页
China Mining Magazine
基金
国家自然科学基金项目"基于多传感器信息融合技术的矿井瓦斯突出预警研究"资助(编号:60974126)
关键词
冲击地压
多变量时间序列
关联维数
混沌特性
rock burst
multivariate time-series
correlation dimension
chaotic characteristic
