Application of Gray-Markov Model to Land Subsidence Monitoring of a Mining Area

Abstract

Land subsidence monitoring in mining areas is one of the main applications of surface deformation monitoring, which is of great significance for safety production. Using the IPTA (Interferometric Point Target Analysis) time-series InSAR (Interferometry Synthetic Aperture Radar) method, land subsidence data from the new exploration area in the Weizhou mining area were analyzed and compared with static GPS (Global Positioning System) monitoring data for 2017-2020. Gray-Markov model was established by combining the gray prediction model with the Markov model to predict the surface subsidence of the mining area. The results show that (1) InSAR data have high accuracy and application potential in prediction of long-term surface deformation in mining areas; (2) The Gray-Markov model can better reflect the volatility and practicality of subsidence data in mining areas; (3) The prediction results have high accuracy, and the Gray-Markov model can serve as an effective guide for long-term surface deformation monitoring and safety management.

Review of satellite radar interferometry for subsidence analysis

“…Integration of satellite InSAR data with a deep learning-based method (Li et al, 2021a) and artificial neural network was demonstrated to be an accurate and reliable method for time series analysis and prediction of their near-future displacement (Rahmani and Ahmadi, 2018) and for evaluating the influences of different geology and hydrogeology factors on the subsidence (Dehghani et al, 2013). More recently, the application of the Gray-Markov model to land subsidence prediction has been proposed by Yuan et al (2021) and Deng et al (2017) for areas interested by mining activities and groundwater withdrawal, respectively.…”

“…Assessment of subsidence susceptibility (Hakim et al, 2020), hazard (Choi et al, 2011), vulnerability (Nadiri et al, 2020;Nadiri et al, 2018) and economic loss (Ezquerro et al, 2020a). Prediction (Rahmani and Ahmadi, 2018;Yuan et al, 2021) and scenarios assessment (Catalao et al, 2020;Gao et al, 2021;Miller and Shirzaei, 2021) Simulated InSAR mimicking (Li et al, 2017;Wang et al, 2018) and performance evaluation (Ng et al, 2009) Review Hydrologic applications (Galloway and Burbey, 2011;Hoffmann, 2005;Smith, 2002) and models (Guzy and Malinowska, 2020); mining (Hu et al, 2021;Ishwar and Kumar, 2017) Technical paper PSInSAR for subsidence (Ferretti et al, 2015); polarimetrc analysis ; corner reflectors (Strozzi et al, 2013); free platforms (Galve et al, 2017).…”

Short-term Forecast of EV Ownership in Shanghai Based on Metabolic GM(1,1)-Markov Model

“…Markov model can predict the trend of the current state of the sequence according to the transition probability between the existing states, which is applicable to both time series and spatial series [14] . The basic principle is:…”

Application of Optimized Grey-Markov Model to Land Subsidence Monitoring With InSAR

“…Therefore, the safety production of mining areas has widely concerned all walks of life. Research on monitoring and predicting land subsidence in mining areas has always been a prominent issue [1].In the field of land subsidence monitoring in mining areas, there are many traditional monitoring methods and means, such as theodolite triangle elevation measurement; and highgrade precision leveling to full station measurement; and global positioning navigation system (GPS) positioning measurement, etc. The need to establish monitoring stations along with the mining deformation; through repeated field observation; is time-consuming; with input cost, especially since the labor cost is also higher; furthermore, it is too complex for some terrain, has inaccessible areas, and it is too difficult to establish observation stations and measurement work.…”