Chaos And Fractal Characteristic Analysis And Prediction Of Workface Gas Emission Time-series

China is one of the most serious countries in the world where the coal and gasoutburst often happens, with a series of problems such as many outburst wells, widelydistribution, shallow penetration outburst depth, high outburst frequency, hugeoutburst strength etc. However, the coal and gas outburst mechanism still hasn't beenfully understood yet. No country can absolutely prevent the occurrence of outburst.Moreover, at presents, research and methodology on local test gas and getting rid ofdanger is gradually improved at home and abroad, there is a great difficulty in theprevention and control of coal and gas outburst. A large number of data indicate thataccidents which created particularly serious coal and gas outburst mostly take place inmine workface. So seeking a better non-contact dynamic continuous predictionmeasure is quite necessary.Firstly, the relationship between workface gas emission with coal seam gascontent, gas pressure, seam permeability, geological structure factors and other factorshave been proposed according to the flow theory of gas in coal seams. Further, therelated complex features of gas outburst with gas emission time-series be obtained,and it is as studying content in full text.Secondly, the dynamics simulation of gas emission time-series is analyzed byapplying Taken's phase space reconstruction theory. The phase space reconstructionparameters are selected by using the differential entropy minimum principle. Severalembedding dimension of studying instance been ultimately identified are great than8.It indicates that the gas emission dynamics system is a high-dimensional complexsystem. On this basis, the chaotic characteristics of gas emission time-series data isqualitatively analyzed by using power spectrum and Poincare section. LargestLyapunov exponent solution based on nonlinear least squares regression andcorrelation dimension solution based on FCM are proposed in thesis because oflimited actual length of time-series and unknown noise. To further verifying thevalidity of the method, the algorithm has been carried out to test by using identifiedLogistic chaotic time-series and Hénon system. Then, the method is used todistinguish characteristics of the actual time-series, and determination of thecorrelation dimension also further validates the correctness of the embeddingdimension solution mentioned in front.Thirdly, Hurst exponent is appointed to analyze long-range correlation oftime-series in this thesis, and appointed V/S method to solve the long-range trend characteristics for shortcomings of the traditional R/S method. Further, the effectivecorrelation length is quoted to analyze the long-range features. The Hurst exponentrange of several groups of actual data is between0.6301-0.9137, and its effectivecorrelation length range is from101.415to102.134. These data suggest that workface gastime-series with positive long-range trend characteristics, and this characteristics ofno-outburst gas time-series data is significantly stronger than outburst gas time-seriesdata.Finally, the LS-SVM regression model within the Bayesian inference frameworkis adapted to multi-step predict workface gas emission time-series. Further, in order toimprove the robustness of the models, BWLS-SVM model is introduced for theBayesian LS-SVM is sensitive to outliers. In this paper four error indicators areintroduced to evaluate prediction effect of the given model. The updated value of theprediction function variables is used the measured data, so the mentioned methods canachieve better multi-step prediction; the prediction error is also smaller. To furthercompare the proposed multi-step prediction effect, the traditional iterative predictionby using LS-SVM model within Bayesian inference framework is given. The finalresults show that the traditional iterative prediction results are unsatisfactory.The author studies the complex nature of workface gas emission from the pointof view of the chaotic time-series, and on this basis to establish time-series forecastingmodels. The contents of this article has laid a theoretical basis for non-contact coaland gas outburst dynamic forecast, but also open up new ideas for in-depth analysis ofthe gas emission complex.