Method Of Seeking Senate Probability Integration Under Conditions Of Thick Alluvium

Mining ground displacement and deformation monitoring is essential to the process of coal mine safety production work, therefore, the mining subsidence prediction work is also a very important step. "Three under" coal problem has been more difficult problem of our country.Doing a good job of mining subsidence prediction work and guide the practice of "three under" mining with the accurate results which plays an important role in mining subsidence,mine safety and efficient productions. The premise of mining subsidence prediction work is to solve predicting parameters and the reliability of the estimated results,determined the accuracy of the predicting parameters.The accuracy of the estimated parameters by the probability integral method has satisfied the requirement of mining production in general geological mining condition,but for the special geological conditions,it will take a poor predicted effect to the movement and deformation without considering the influence factors of the thick alluvium. This paper introduces the basic theory and the way to obtain parameters of probability integral method (linear least square method,mode vector method,genetic algorithmand CA-rPSO). Four different methods were used (under thick soil layer mining conditions).The results showed that the edge of ground subsidence basin has the characteristics of fast convergence, which will affect the predicted results. Relatively, residual error of genetic algorithm and CA-rPSO is less than the other two methods,so the predicted model needs to be modified,use the Genetic algorithm and CA-rPSO are compared and analyzed for the edge modified models.Using Matlab programming language to achieve the four algorithms,comparison of predicted and measured data of subsidence and horizontal movement which obtain from 1013 first coal mining face of Wugou Mine of Wanbei Coal,we can know that the result of modified model is better to close to the measured data with taking into account the factor of thick alluvium. CA-rPSO has the better adaptability in the accuracy of solving predicting parameters and operational efficiency under the condition of thick alluvium,and can provide a more effective decision-making help for "three under".