Attempted to conduct a dynamic monitoring research on coal mining subsidence in western mining areas by using the method of combining D-InSAR and GPS technology. The observation points were installed on the main secti...Attempted to conduct a dynamic monitoring research on coal mining subsidence in western mining areas by using the method of combining D-InSAR and GPS technology. The observation points were installed on the main section and the three-dimensional coordinates of the points were measured by using the method of dynamic differential GPS. Meanwhile, the radar images of this subsidence area were processed by using the method of interferometry with daris software, and the interferogram of the subsidence area was obtained. Through this study, the GPS monitoring data and the InSAR deformation data were integrated and the dynamic subsidence contours of the experimental area were obtained. GPS/InSAR fusion technology provides a new technological means for large-scale dynamic monitoring of coal mining subsidence in western mountainous mining areas and shows good application prospects in coal mining subsidence monitoring and disaster warning.展开更多
In order to study dynamic laws of surface movements over coal mines due to mining activities,a dynamic prediction model of surface movements was established,based on the theory of support vector machines(SVM) and time...In order to study dynamic laws of surface movements over coal mines due to mining activities,a dynamic prediction model of surface movements was established,based on the theory of support vector machines(SVM) and times-series analysis.An engineering application was used to verify the correctness of the model.Measurements from observation stations were analyzed and processed to obtain equal-time interval surface movement data and subjected to tests of stationary,zero means and normality.Then the data were used to train the SVM model.A time series model was established to predict mining subsidence by rational choices of embedding dimensions and SVM parameters.MAPE and WIA were used as indicators to evaluate the accuracy of the model and for generalization performance.In the end,the model was used to predict future surface movements.Data from observation stations in Huaibei coal mining area were used as an example.The results show that the maximum absolute error of subsidence is 9 mm,the maximum relative error 1.5%,the maximum absolute error of displacement 7 mm and the maximum relative error 1.8%.The accuracy and reliability of the model meet the requirements of on-site engineering.The results of the study provide a new approach to investigate the dynamics of surface movements.展开更多
基金Supported by the Natural Science Foundation of Shannxi Province
文摘Attempted to conduct a dynamic monitoring research on coal mining subsidence in western mining areas by using the method of combining D-InSAR and GPS technology. The observation points were installed on the main section and the three-dimensional coordinates of the points were measured by using the method of dynamic differential GPS. Meanwhile, the radar images of this subsidence area were processed by using the method of interferometry with daris software, and the interferogram of the subsidence area was obtained. Through this study, the GPS monitoring data and the InSAR deformation data were integrated and the dynamic subsidence contours of the experimental area were obtained. GPS/InSAR fusion technology provides a new technological means for large-scale dynamic monitoring of coal mining subsidence in western mountainous mining areas and shows good application prospects in coal mining subsidence monitoring and disaster warning.
基金supported by the Research and Innovation Program for College and University Graduate Students in Jiangsu Province (No.CX10B-141Z)the National Natural Science Foundation of China (No. 41071273)
文摘In order to study dynamic laws of surface movements over coal mines due to mining activities,a dynamic prediction model of surface movements was established,based on the theory of support vector machines(SVM) and times-series analysis.An engineering application was used to verify the correctness of the model.Measurements from observation stations were analyzed and processed to obtain equal-time interval surface movement data and subjected to tests of stationary,zero means and normality.Then the data were used to train the SVM model.A time series model was established to predict mining subsidence by rational choices of embedding dimensions and SVM parameters.MAPE and WIA were used as indicators to evaluate the accuracy of the model and for generalization performance.In the end,the model was used to predict future surface movements.Data from observation stations in Huaibei coal mining area were used as an example.The results show that the maximum absolute error of subsidence is 9 mm,the maximum relative error 1.5%,the maximum absolute error of displacement 7 mm and the maximum relative error 1.8%.The accuracy and reliability of the model meet the requirements of on-site engineering.The results of the study provide a new approach to investigate the dynamics of surface movements.
