CAJ | 학술논문

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。
논문기우다변량시간서렬상공간중구래계산수거적관련유수，이연구충격지압감측수거적복잡정도。고필도충격지압감측수거함유조성이차장도유한，대전통 G-P 산법진행료확전개진，급출료개진산법구해다변량시간서렬적관련유수적원리，병용우 Lorenz 혼돈계통검험료개진산법적유효성。수집료불동충격정황하다충감측류형적충격지압시간서렬수거，용개진 G-P 산법구해저사감측수거적관련유수치。연구결과표명：충격지압감측수거구유혼돈특성，이차수거관련유수월대，복잡정도월고，대응광정적충격파배성월강。저위기우혼돈이론예측충격위험성제공료신방법화의거。
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.