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With the rapid economic development in China, coal resources are being exploited greatly, which easily causes geological disasters due to surface subsidence. Fast and accurate surface subsidence monitoring and forecasting in mining regions are important references to analyze surface variation laws and disaster warning. However, differential interferometric synthetic aperture radar (D-InSAR) in mine surface monitoring is highly sensitive to spatiotemporal baseline and atmospheric delay. In addition, traditional machine learning algorithms have complicated network structures and difficulties determining parameters. Small baseline subsets InSAR (SBAS-InSAR) and extreme learning machine (ELM) dynamic prediction were combined for corresponding experimental studies to address these problems. On the basis of SBAS-InSAR, surface subsidence monitoring data in mining areas in Pingdingshan City, China, were collected, and a comparative analysis of D-InSAR monitoring data was performed, which verified the validity of SBAS-InSAR monitoring. On the basis of SBAS-InSAR data, a prediction model was built by ELM. The model results were compared with the prediction results of back propagation (BP) neural network and support vector machine (SVM) through root mean square error (RMSE) and mean relative error (MRE). Results demonstrate that the surface subsidence prediction of SBAS-InSAR in the monitoring mining area can reach millimeter accuracy. The MRE values of ELM, BP, and SVM prediction are maintained within 2%, 5%, and 8%, and the RMSE values are less than 3 mm, 7 mm, and 10 mm, respectively, thereby indicating that ELM prediction has high accuracy and reliability. This study provides an important evidence for safe production and scientific disaster prevention and reduction in mining areas. [ABSTRACT FROM AUTHOR]

The accurate prediction of surface subsidence induced by coal mining is critical to safeguarding the environment and resources. However, the precision of current prediction models is often restricted by the lack of pertinent data or imprecise model parameters. To overcome these limitations, this study proposes an approach to predicting mine subsidence that leverages Interferometric Synthetic Aperture Radar (InSAR) technology and the long short-term memory network (LSTM). The proposed approach utilizes small baseline multiple-master high-coherent target (SBMHCT) interferometric synthetic aperture radar technology to monitor the mine surface and applies the long short-term memory (LSTM) algorithm to construct the prediction model. The Shigouyi coalfield in Ningxia Province, China was chosen as a study area, and time series ground subsidence data were obtained based on Sentinel-1A data from 9 March 2015 to 7 June 2016. To evaluate the proposed approach, the prediction accuracies of LSTM and Support Vector Regression (SVR) were compared. The results show that the proposed approach could accurately predict mine subsidence, with maximum absolute errors of less than 2 cm and maximum relative errors of less than 6%. The findings demonstrate that combining InSAR technology with the LSTM algorithm is an effective and robust approach for predicting mine subsidence. [ABSTRACT FROM AUTHOR]

In the process of developing mine resources, mining subsidence is inevitable. The D-InSAR (differential interferometric synthetic aperture radar) technology has been widely used to monitor large-scale ground subsidence in mining areas in recent years. However, the limitations of this technology mean that large-gradient ground subsidence cannot be monitored. This paper describes a weighted total least-squares method that can be used to determine the piecewise linear mapping between GNSS (global navigation satellite system) data and D-InSAR data. This mapping can be applied to optimize the conventional D-InSAR monitoring results, particularly in large-gradient subsidence areas, enabling the mining subsidence to be comprehensively evaluated. The proposed method is used to extract the surface subsidence information for a certain area of the Sihe mine in Shanxi Province, China. It is found that the optimized data is more reliable and accurate than the conventional D-InSAR monitoring data in the case of large-gradient subsidence. [ABSTRACT FROM AUTHOR]

The theory and method of mining subsidence monitoring based on D-InSAR(differential interferometric synthetic aperture radar) technology is a hot topic in the current field of mine deformation monitoring. At present, three-dimensional monitoring model of mining subsidence has been constructed successfully by some scholars through integrating single line of sight to D-InSAR technology and mining subsidence law. However, the model is only suitable for the researches on mining subsidence of horizontal coal seam rather than inclined coal seam. In view of the above defects，according to the relationship between deformation of line of sight monitored by D-InSAR and three-dimensional deformation, considering the surface subsidence law of inclined coal seam mining, the surface subsidence monitoring method for inclined coal seam based on single line of sight to D-InSAR technology is proposed. The simulation experiment shows that the absolute relative error of the subsidence monitoring method is less than 9. 53%, with an average of 1. 31%. The absolute relative error of the horizontal movement monitoring along North-South (SN) and East-West (EW) is less than 9. 78%，with an ave-rage of 3. 71%, which meets the mining subsidence monitoring accuracy requirements. The surface movement and deformation caused by the mining of the 9310 working face of Nantun coal mine in yanzhou mining area, Shandong province China during 2012-01-27 - 2012-02-07 are successfully monitored by this method in this paper [ABSTRACT FROM AUTHOR]

Mining subsidence is time-dependent and highly nonlinear, especially in the Loess Plateau region in Northwestern China. As a consequence, and mainly in building agglomerations, the structures can be damaged severely during or after underground extraction, with risks to human life. In this paper, we propose an approach based on a combination of a differential interferometric synthetic aperture radar (DInSAR) technique and a support vector machine (SVM) regression algorithm optimized by grid search (GS-SVR) to predict mining subsidence in a timely and cost-efficient manner. We consider five Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) images encompassing the Dafosi coal mine area in Binxian and Changwu counties, Shaanxi Province. The results show that the subsidence predicted by the proposed InSAR and GS-SVR approach is consistent with the Global Positioning System (GPS) measurements. The maximum absolute errors are less than 3.1 cm and the maximum relative errors are less than 14%. The proposed approach combining DInSAR with GS-SVR technology can predict mining subsidence on the Loess Plateau of China with a high level of accuracy. This research may also help to provide disaster warnings. [ABSTRACT FROM AUTHOR]