1.Hebei State Key Laboratory of Mine Disaster Prevention，North China Institute of Science and Technology，Beijing ,China; 2.School of Information Engineering，Institute of Disaster Prevention，Beijing ,China;3.School of Energy and Mining Engineering, China University of Mining and Technology-Beijing,Beijing ,China

The water bursting coefficient is not fully applicable to the evaluation of water inrush risk in thick seams in deep mining.In order to solve the problem of water inrush risk assessment of thick water-bearing floor in deep mining above aquifer,the master indicator system of floor water inrush is established according to mine conditions.Based on the interval gray optimal clustering theory,powerful spatial information management and analysis function of GIS are used to complete model creation,calculation,and results display.The weight coefficient of AHP is determined according to the degree of contribution of the main control factors to the risk of water inrush.The K-Means clustering algorithm divides the interval values of different risk categories according to the data distribution characteristics and expert knowledge and experience,which solves the complexity and uncertainty of the main control factors.According to the concept of membership degree,the classical whitenization weight function is improved.The definition of the whitenization weight function of each category is not only related to the adjacent upper and lower intervals,but is related to the standard value of each interval.The whitenization weight function maps the discrete gray numbers to the interval gray numbers,which solves the problem of result distortion caused by the great difference of the physical meaning,dimension and magnitude of the main control factors.The deterministic judgment of the membership degree under the h risk level is 1 as the system characteristic behavior sequence,and the Deng’s gray correlation degree between the evaluation object and each risk category is calculated to form the relevance degree matrix.The weighted generalized distance with the degree of difference between the evaluation object and each risk category is used to describe the proximity of the evaluation object to the h category,and finally the optimal model is established.Taking Xingdong Mine as an example,based on the exploration data and the results of previous water inrush investigations,six main control factors were selected,the weight coefficient was determined by AHP,and the K-Means clustering algorithm was used to determine four level interval values of safe,safe,danger and danger.The partition map of the risk of floor water inrush is obtained by using the grey optimal clustering theory.In the end,data such as the distribution of the various hazard levels and the size of the area were obtained.The results show that the locations where the water inrush accidents occurred are located in the danger zone.Compared with the evaluation results of the water bursting coefficient method,due to the larger amount of information,the absolute control effect of the critical value is weakened by the membership relationship,which makes the result more comprehensive.At the same time,the water bursting coefficient method based on thin plate theory is broken only for the bottom plate，which is less than 50 m.A method for evaluating the water inrush risk of the thick water-storing layer in deep mining above aquifer is formed.

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