Risk Evaluation And Three-dimensional Visualization Of Ground Collapse In Coal Mine Goaf

Ground subsidence in the goaf is one of the common geological disasters in coal mining enterprises.It has a wide range of damage,great impact and long duration.In order to reduce the major economic losses and casualties caused by the fall of the goaf and instability,the study of coal mine goaf The evaluation of the risk of ground collapse in the district is particularly important for the safe production of coal mine enterprises.In this paper,deep learning is applied to the evaluation of the risk of ground collapse in the goaf area of coal mines,and the results are visualized in three dimensions.The specific research work is as follows:(1)Firstly,the mechanism of ground subsidence in the mined-out area is analyzed.Through the extraction and analysis of the geological exploration drilling data and hydrological data of the coal mine,the normality test is carried out with the quantile map method,and the influence of the mined-out area is obtained.The main factor of stability.In order to use the deep learning model,feature selection based on encapsulated evaluation strategy,BP neural network to deal with missing values,and normalization method are selected to preprocess the data.(2)Aiming at the problem that traditional goaf risk assessment methods cannot handle time series data,the Long Short-Term Memory(LSTM)in deep learning is used as the risk evaluation model of the goaf ground collapse.First,the 10 features that affect the collapse of the goaf after preprocessing are input as the LSTM model.According to the amount of input data,the number of samples for each batch is selected as 8 through experiments,and selected by comparing SGD,RMSprop and Adam.Adam is used as an optimization function,the number of hidden layer nodes is set to 30,and the dropout value is 0.5 when the LSTM model can achieve the best performance.The output of the model is divided into four levels of the risk evaluation of the mined-out area,so as to construct the risk evaluation model of the mined-out area ground collapse based on LSTM.In order to verify the excellent performance of the model in solving such problems,the obtained goaf risk evaluation results were compared with the evaluation results of the BP neural network model and SVM support vector machine.The results show that the evaluation accuracy of the model is 5.44%higher than the BP network model,and 4.1%higher than the conventional SVM model,which further illustrates the accuracy of this model in the evaluation of the risk of ground collapse in coal mined areas.(3)In order to visually display the results of the risk assessment of the mined-out area,use GOCAD software to build a geological database,use the method of establishing points,lines,and surfaces to build a three-dimensional geological model.The collapse risk is evaluated,and the prediction results are visualized in a three-dimensional map.