Rock burst complexity analysis based on multivariate monitoring time series
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.
rock burstmultivariate time-seriescorrelation dimensionchaotic characteristic
NETL
NSTL
万方数据
维普
