The Technique Research About Prediction Of Coal And Gas Outburst Based On Non-linear Theory

China is a country with extensive resources, but also is one of the most affected countriesof geological disasters, coal and gas outburst is one of the common natural disasters in mineproduction. Large numbers, wide distribution and the frequent accidents are the characteristicsof coal and gas outburst mine in our country, especially with the increase of mining depth, thegrowing complex geological conditions and the gas pressure increasing constantly, magnitudeof coal and gas outburst is greatly enhanced. Therefore, the research of prediction andpretreatment method of coal and gas outburst contributed to improve the safety of mine.In this paper, based on the nonlinear theory mechanism and prediction method of coaland gas outburst were further studied. By analyzing the theory, the acoustic emission indexwas used to display the ground stress value and the gas concentration index was used tocharacterize the status of gas, both of the two indices were made as the sensitive indicatorsaffecting the coal and gas outburst. And on this basis, acoustic emission and gas index werestudied, the corresponding BP neural network was programmed, ANNS AE model and ANNCprediction model of gas concentration were established.Based on the summary and analysis of gas outburst mechanism and the number land ofinfluencing factors, using rolling forecast method, determined the sample input parameterswith 1 hour acoustic emission frequency value and 10 minutes gas concentration average, anddiscussed the design of network model structure, including the selection of input or outputlayer unit index, the determination of hidden layer unit and training sample, and the process ofinput or output data. Based on these above, the weighting of six indexes including 1 hour and8 hours and so on, were obtained by the analytical hierarchy process (AHP) as well as theacoustic emission comprehensive index. Finally, comprehensive index CI ,used to predict coaland gas outburst, and the critical value of risk were got by synthetically analyzing both the acoustic emission comprehensive index and gas density index.Through analysis of field data to predict the contrast of conventional indicators, we havebeen validated for the front side indicators in forecasting the follow-up time period outburstsituation is accurate and feasible.