Study on analogy analysis on prediction of surface subsidence parameters in mining area of Chongqing City

The prediction of mine surface subsidence in mining area of Chongqing City is challenging because of lack of field measurements data and particularity of geological and mining conditions in the area. The paper is devoted to develop a prediction method based on analogy analysis which is applicable to the mine area of Chongqing City. The field measured surface subsidence data of three coal mining districts in Chongqing City was analyzed. Then, three analogy analysis methods, the P-Coefficient Method, Fuzzy Cluster Analysis,and Similarity Theory Method, were applied to predict surface subsidence parameters separately. By comparing the predicted results with the field measured data, the authors investigated the advantages and disadvantages and the applicable situations for the three methods. The comprehensive evaluation coefficient of overburden by P-Coefficient Method could not accurately predict subsidence parameters under the influence of different ratio of mining depth and mining thickness and different angle of coal seams. The subsidence phenomena cluster method based on fuzzy cluster analysis or similarity theory were built mainly upon field measured data in the eastern mining area of China, thus the prediction model was not applicable to mines in mining area of Chongqing City. The prediction parameters using comprehensive analogy analysis presented least difference from the field measured subsidence parameters among the three models. It was proved that the comprehensive analogy analysis was an effective method to predict subsidence parameters in mining areas of Chongqing City, which provides practical references for subsidence predictions without field measured data in the area.