Subsidence In Mining Areas Research Articles

Novel Method for Monitoring Mining Subsidence Featuring Co-Registration of UAV LiDAR Data and Photogrammetry

Addressing the problem that traditional methods cannot reliably monitor surface subsidence in coal mining, a novel method has been developed for monitoring subsidence in mining areas using time series unmanned aerial vehicle (UAV) photogrammetry in combination with LiDAR. A dynamic subsidence basin based on the differential digital elevation model (DEM) was constructed and accuracy of the proposed method was verified, with the uncertainty of the DEM of difference (DoD) being quantified via co-registration of a dense matching point cloud of the time series UAV data. The root mean square error calculated for the monitoring points on the subsidence DEM was typically between 0.2 m and 0.3 m with a minimum of 0.17 m. The relative error between the maximum subsidence value of the extracted profile line on the main section after fitting and the measured maximum subsidence value was not more than 20%, and the minimum value was 0.7%. The accuracy of the UAV based method was at the decimeter level, and high accuracy in monitoring the maximum subsidence value was attained, confirming that an innovative strategy for monitoring mining subsidence was realized.

Decision-making fusion of InSAR technology and offset tracking to study the deformation of large gradients in mining areas-Xuemiaotan mine as an example

The multi-level disturbance of underground and surface caused by coal mining activities intensifies the deterioration of the ecological environment in the mining area. Among them, the uneven settlement caused by coal mining is the most intuitive manifestation of surface environmental damage. The uneven settlement in the mining area has the characteristics of large settlement magnitude and severe deformation. Therefore, based on 15 Sentinel-1A image data, this paper uses three methods: SBAS InSAR, continuous D-InSAR and offset tracking technology to monitor the surface deformation of the mining area. The results show that the continuous D-InSAR technology SBAS-InSAR technology is applied to the small deformation in the edge area of the subsidence basin. The mining area with low gradient subsidence of SBAS-InSAR can obtain better performance than continuous D-InSAR technology. The offset tracking technique is used to monitor the large gradient deformation in the center of the subsidence basin. Therefore, this paper proposes to expand the quantitative analysis through the spatial coherence threshold and the accuracy and successful image elements of the interference fringe displacement. Combine the advantages of the three methods and overcome the shortcomings of each method, fuse the deformation information of the three methods, and obtain the deformation law of the whole surface subsidence. The results show that the mean absolute error (MAE1-1) of continuity D-InSAR is 0.92 m, the mean absolute error (MAE2-1) of SBAS-InSAR is 0.94 m, and the mean absolute error (MAE3-1) of Offset-tracking is 0.25 m. The results of this fusion method are in good agreement with the measured data, and the mean absolute error (MAE4-1) of vertical displacement is 7 cm. Therefore, the fusion method has advantages over individual methods and provides a new idea in monitoring the large gradient deformation of coal mining subsidence in mining areas.

Accuracy verification and correction of ascending and descending SBAS- and MSBAS-InSAR in monitoring mining surface subsidence

To address the position offset and insufficient monitoring accuracy of surface subsidence in mining areas using the SBAS-InSAR, this study uses the MSBAS-InSAR method. However, the quality of correction results is poor owing to the ill-posed observation equation of MSBAS. Therefore, the SBAS-based decomposition method is used, which directly decomposes the ascending and descending SBAS monitored LOS results to circumvent the ill-posed problem of the MSBAS and obtain higher-accuracy 2D deformation results. A real mining area was taken as the research site and monitored using the SBAS, MSBAS, and the SBAS-based decomposition method. The accuracy and effectiveness of the surface deformation results obtained by these methods were compared and verified with the levelling data in the same period. The results show that the SBAS-based decomposition method can significantly reduce the position offset and improve the monitoring accuracy of single-track SBAS, and circumvents the ill-posed problem of MSBAS.

Construction of “Space-Sky-Ground” Integrated Collaborative Monitoring Framework for Surface Deformation in Mining Area

Ground deformation measurements in mining areas play a key role in revealing the surface subsidence law, retrieving the subsidence parameters, warning of geological disasters and restoring the surface ecology. With the development of science and technology, there have emerged a great number of monitoring techniques and buildings of diverse protection levels. The diversity of monitoring techniques and the multiplicity of monitoring objects have brought challenges for surface deformation monitoring in the coal industry. Based on the existing deformation monitoring techniques, this paper established a framework of “space-sky-ground” collaborative monitoring system in mining area. We also constructed an AHP-TOPSIS (Analytic Hierarchy Process method- Technique for Order Preference by Similarity to an Ideal Solution) preference model of “space-sky-ground” collaborative monitoring of surface deformation in mining area, and carried out engineering application. Our study shows that the framework of the “space-sky-ground” collaborative monitoring system for surface subsidence in mining areas established in this paper, combined with the AHP-TOPSIS monitoring preference model, which can fully combine the advantages of each monitoring technique, overcome the limitations of a single monitoring technique, comprehensively obtain the surface subsidence data and work out the surface deformation subsidence pattern. This information provides a data and technical support for surface environment management.

A Novel Phase Unwrapping Method Used for Monitoring the Land Subsidence in Coal Mining Area Based on U-Net Convolutional Neural Network

Large-scale and high-intensity mining underground coal has resulted in serious land subsidence. It has caused a lot of ecological environment problems and has a serious impact on the sustainable development of economy. Land subsidence cannot be accurately monitored by InSAR (interferometric synthetic aperture radar) due to the low coherence in the mining area, excessive deformation gradient, and the atmospheric effect. In order to solve this problem, a novel phase unwrapping method based on U-Net convolutional neural network was constructed. Firstly, the U-Net convolutional neural network is used to extract edge to automatically obtain the boundary information of the interferometric fringes in the region of subsidence basin. Secondly, an edge-linking algorithm is constructed based on edge growth and predictive search. The interrupted interferometric fringes are connected automatically. The whole and continuous edges of interferometric fringes are obtained. Finally, the correct phase unwrapping results are obtained according to the principle of phase unwrapping and the wrap-count (integer jump of 2π) at each pixel by edge detection. The Huaibei Coalfield in China was taken as the study area. The real interferograms from D-InSAR (differential interferometric synthetic aperture radar) processing used Sentinel-1A data which were used to verify the performance of the new method. Subsidence basins with clear interferometric fringes, interrupted interferometric fringes, and confused interferometric fringes are selected for experiments. The results were compared with the other methods, such as MCF (minimum cost flow) method. The tests showed that the new method based on U-Net convolutional neural network can resolve the problem that is difficult to obtain the correct unwrapping phase due to interrupted or partially confused interferometric fringes caused by low coherence or other reasons in the coal mining area. Hence, the new method can help to accurately monitor the subsidence in mining areas under different conditions using InSAR technology.

D-InSAR Monitoring Method of Mining Subsidence Based on Boltzmann and Its Application in Building Mining Damage Assessment

Monitoring and predicting mining area subsidence caused by coal mining help effectively control geological disasters. Information regarding small surface deformations can be obtained using a differential interferometric synthetic aperture radar (D-InSAR), which exhibits high monitoring accuracy and can cover large areas; thus, D-InSAR are being applied for mining area settlement monitoring. However, mining areas are prone to large gradient deformations in short durations; obtaining information regarding such deformations is outside the scope of D-InSAR monitoring. To adapt to the characteristics of D-InSAR monitoring, this study selected a Boltzmann function model with a slow boundary convergence rate to address the problem of probabilistic integration methods exhibiting a high boundary convergence rate. The three-dimensional surface deformation of the mining area can be accurately obtained. According to the projection relationship between D-InSAR line of sight (LOS) directional deformation, subsidence, and horizontal movement, a D-InSAR monitoring equation for mining subsidence assisted by Boltzmann is derived. This equation is combined with the shuffled frog leaping algorithm (SFLA) to obtain the parameters to be estimated from the equation. The D-InSAR LOS deformation information of the 13121 working face in the Huainan mining area was obtained from July 14 to October 30, 2019. The proposed method was then used to obtain the predicted parameters of the working face under insufficient mining. The predicted parameters of mining subsidence were calculated to obtain the predicted parameters when it was fully mined. Then, the revised parameters were used to predict the subsidence and horizontal movement of the mining area and compared with the actual leveling observations. The results show that the mean square error of predicted subsidence is 97.1 mm, which is about 3.09% of the maximum subsidence value; the mean square error of predicted horizontal movement is 46.1 mm, which is about 4.1% of the maximum horizontal movement value. The predicted results of the aforementioned method were used to analyze the damage to the buildings above the working face and determine the damage level of the buildings to provide a reference for the demolition and maintenance of the Zhaimiao village.

Subsidence Monitoring Base on SBAS-InSAR and Slope Stability Analysis Method for Damage Analysis in Mountainous Mining Subsidence Regions

Surface subsidence caused by coal mining has a great impact on the geological and ecological environments and causes damage to houses, roads, and industrial buildings. In order to understand the subsidence pattern in the mountainous mining regions, three mining faces of the Zhangjiamao mining area in the north of Shaanxi province, northwestern China are taken as case study. Firstly, the small baseline subset (SBAS) technology is used to process 12 images obtained in the mining area to investigate the subsidence data from December 2019 to April 2020. The boundary of surface deformation of the mining area interpreted by the SBAS-InSAR technology is inconsistent with the theoretical boundary suggested by coal mine subsidence theories. Especially, there are some areas in which the real subsidence are larger than estimated area. This discrepancy must be corrected as steep slopes near the theoretical boundary may increase the likelihood of landslides. Our research indicates that: (1) The accumulated displacement and the maximum deformation rate reached −120.759 mm and −270.012 mm/yr in the study area, and the subsidence boundary of the three mining faces is revealed; (2) the combination of the predicted boundary and slope stability analysis can effectively identify the landslide region at the edge of subsidence boundary; (3) the field surveys have proved the effectiveness of this method. The mining area subsidence revealed by our research helps to further understand the impact of land subsidence caused by mining in the mountainous areas and provides a practical method to predict subsidence boundaries and the likelihood for landslides.

Research on 3D Laser Scanning Monitoring Method for Mining Subsidence Based on the Auxiliary for Probability Integral Method

When 3D laser scanning technology is used to monitor the surface deformation of mining subsidence in mining area, the surface of the working face is covered with a large number of vegetation, and the surface water accumulates above the working face, which makes the point cloud data obtained by the 3D laser scanning difficult to denoise, or even missing. At this time, the conventional 3D laser scanning technology can not obtain the surface deformation field of mining subsidence. Aiming at the above problems, the 3D laser scanning monitoring method for mining subsidence based on the auxiliary for PIM proposed (3DLS-PIM). Firstly, this paper introduces the PIM prediction model. Secondly, the mining subsidence observation equation based on 3DLS-PIM is constructed, and then the prediction parameters of PIM are solved based on quantum particle swarm optimization (QPSO). Finally, according to the PIM and its parameters, the mining subsidence surface deformation basin is predicted and obtain the surface deformation field of mining subsidence. Robust experiments show that QPSO has a certain ability to resist random errors and gross errors. The results of engineering application show that the mining area 3D deformation monitoring method proposed in this paper has certain engineering application value.

Mining large-gradient subsidence monitoring using D-InSAR optimized by GNSS

ABSTRACT 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.

Study on Mining Subsidence of Mineral Deposit and Overburden Stability Based on Probability Density Function

Mining subsidence, a major geological disaster caused by underground mining, will seriously damage the land. In this paper, the author predicts the surface subsidence in mining subsidence area of certain mineral deposits and strata condition by applying the Probability Density Function Method, and designs two evaluation schemes for calculation of the overlying rock formation stability. Study results provide a reliable basis for the disastrous governance of mining subsidence in mining areas.

Research on dynamic prediction model of surface subsidence in mining areas with thick unconsolidated layers

In order to improve the accuracy of the surface dynamic prediction model in mining areas with thick unconsolidated layers and improve Knothe time function, the influence coefficient was firstly changed into the coefficient in exponential form, and the influence coefficient of unconsolidated layer was added. Then, a subsidence basin prediction model for mining under thick unconsolidated layers was established. Next, the model was combined with the improved Knothe function, thus constructing a new mining subsidence prediction model. The new subsidence prediction model was applied in 1414 (1) working face in Huainan mining area. The results showed that the integrated model could better reflect the subsidence process, and the prediction values and the measured values agreed well.

DPIM-Based InSAR Phase Unwrapping Model and a 3D Mining-Induced Surface Deformation Extracting Method: A Case of Huainan Mining Area

The mining subsidence in mining area could cause large-gradient deformation in a short period of time. When the deformation gradient exceeds the threshold value of the Differential Interferometry Synthetic Aperture Radar (D-InSAR) technology monitoring gradient, D-InSAR technology is likely to cause the failure of InSAR phase unwrapping algorithm. At this time, the InSAR technology is unable to monitor the 3D surface deformation. Aiming at these problems, an dynamic probability integral method (DPIM)-based InSAR phase unwrapping model and a method of extracting 3D surface deformation were proposed. The phase unwrapping model firstly used the empirical parameters of the probability integral of the mining face to predict the line of sight (LOS) direction deformation phase of the mining subsidence surface. Secondly, the phase of differential interferogram was unwrapped with the assist of the predicted LOS deformation phase under the constraint of DPIM, and the true LOS deformation phase was obtained, then the true LOS deformation phase transformed into LOS deformation. Finally, according to the geometric projection relationship between the LOS deformation and 3D deformation of mining subsidence surface, the probability integral prior model was brought into the equation of the geometric projection relationship. On the basis of relevant boundary conditions, the 3D surface deformation was extracted from the LOS direction deformation field of mining subsidence. The feasibility of the method was verified by the simulation experiment results. The differential interferogram of the subsidence basin was obtained by the differential interference processing of image data of Sentinel-1A on Nov. 16, 2017 and Dec. 10, 2017 of 1613 working face of Guqiao South Mine. By using the DPIM-based phase unwrapping model, the phase of differential interferogram was unwrapped and the 3D surface deformation during this period as well as the deformation extraction method were developed. The results showed that the maximum fitting error value of subsidence was 79 mm, about 8.33% of the maximum value of subsidence, and the fitting error of mean square of subsidence was ±33.5 mm. The results showed that the DPIM-based phase unwrapping model and the method of extracting 3D surface deformation proposed in this paper have certain engineering application values.

Monitoring surface subsidence in the Binchang mining area using small baseline subset differential interferometric synthetic aperture radar with Sentinel-1A data

Surface subsidence caused by mining is difficult to monitor. Traditional field measurements are time-consuming and labor intensive. Small baseline subset (SBAS) technique is a long-term sequence method based on the principles of radar interferometry that can be used to effectively survey small ground surface deformations. This technique has been successfully applied to urban subsidence in previous studies, but it has rarely been used to study subsidence in mining areas. A total of 62 Sentinel-1A images taken from March 2017 to March 2019 were used to analyze the surface subsidence of the Binchang mining area in Shaanxi Province, China. The accuracy was evaluated using the measured field data from six ground control points for eight different months. The root-mean-square-error and correlation coefficient (r) for the data obtained using SBAS and field measurements were 2.27 cm and 0.944, respectively. We examined 12 main subsidence areas with a mean subsidence rate of −11.88 cm / year, where the maximum cumulative subsidence of a single deformation point reached 83.46 cm. The subsidence areas also had differences in spatial distribution and subsidence formation processes. The geological and groundwater conditions were the same for all areas of the study, and mining in the surrounding coal mines was the main cause of surface subsidence. We demonstrate that SBAS differential interferometric synthetic aperture radar technique can be used as an effective method for the long-term monitoring of mining deformation.

Inversion and Analysis of Mining Subsidence by Integrating DInSAR, Offset Tracking, and PIM Technology

High-intensity underground mining generates considerable surface subsidence in mining areas, including ground cracks and collapse pits on roads and farmland, threatening the safety of buildings. Large-amplitude subsidence (e.g., >2 m) is usually characterized by a large phase gradient in interferograms, leading to severe phase decorrelation and unwrapping errors. Therefore, the subsidence on the surface cannot be well derived simply using conventional differential interferometric synthetic aperture radar (DInSAR) or other geodetic measurements. We propose a new method that combines both DInSAR and subpixel offset-tracking technology to improve mine subsidence monitoring over large areas. We utilize their respective advantages to extract both the spatial boundaries and the amplitude of displacements. Using high-resolution RADARSAT-2 SAR images (5 m) acquired on February 13, 2012, and November 27, 2012, in the Shendong Coalfield located at the border between Shaanxi Province and Inner Mongolia Province, China, we obtain the subcentimetre-level subsidence of the mine boundary by DInSAR and resolve the metre-level mine subsidence centre based on subpixel offset tracking. The whole subsidence field is obtained by combining and analyzing the subcentimetre-level and the metre-level subsidence. We use the probability integral method (PIM) function model to fit the boundary and central mine subsidence to reconstruct the spatial distribution of the mine subsidence. Our results show that the maximum central subsidence reaches ~4.0 m (beyond the monitoring capabilities of DInSAR), which is generally in agreement with the maximum subsidence of ~4.0-5.0 m from field investigation. We also model the boundary and the central subsidence (the final fitting coefficient is 0.978). Our findings indicate that the offset-tracking method can compensate for the deficiency of DInSAR in large-amplitude subsidence extraction, and the inclusion of the PIM technique helps reconstruct the whole subsidence field in mining areas.

Prediction of Surface Subsidence Due to Mining

Ground subsidence caused by mining is increasing day by day, and some enormous disasters have been brought. Some scholars have carried out some results by a single factor analysis, but did not consider the multi-working conditions, resulting in a certain deviation from the actual project. Accurate and effective prediction of subsidence caused by coal mining is an urgent problem to be solved. In this paper, the main factors affecting surface subsidence in mining area, which are coal seam thickness and mining depth, are studied by means of composite analysis method. At the same time, three-dimensional finite element method is employed to simulate different engineering conditions. According to the simulation maximum value settlements, fitting through MATLAB software, the corresponding predicted models are established. Comparing with the actual engineering survey data, it is proved that the settlement prediction curve is in good agreement with each other. This research has reference value for actual coal mine engineering and construction engineering.

Study on subsidence monitoring technology using terrestrial 3D laser scanning without a target in a mining area: an example of Wangjiata coal mine, China

When using a Terrestrial 3D laser scanning (TLS) to monitor the mining subsidence in a mining area, the conventional methods are to set up the public target manually, or to set up the ground control network in advance. However, these have the disadvantages of high labor intensity and low efficiency. In order to overcome the shortcomings of the conventional methods, this paper presents the subsidence monitoring method using TLS without a target in the mining area. Feature points are used to splice the multi-station point clouds in the same phase, and a few station coordinates are used to uniformly transform the multiphase data into a geodetic coordinate system. Afterwards, the surface digital elevation model (DEM) is established by using the point clouds after coordinate transformation, and the dynamic subsidence value and subsidence basin during the period of observation are obtained by DEM subtraction. Compared to traditional methods, this method simplifies the field measurement process, reduces the labor intensity, and transfers the main work to the internal industry. It is suitable for surface subsidence monitoring with a harsh external environment and complicated topographic conditions. Surface deformation values are important basic data for mining area subsidence parameters acquisition, mining area environmental assessment, land reclamation, and ecological restoration. As a consequence, it is of great value to popularize and apply in mining area for subsidence monitoring.

Monitoring of large-scale deformation in mining areas using sub-band InSAR and the probability integral fusion method

ABSTRACTThe large-scale and rapid land subsidence that occurs in mining areas often leads to problems, such as densely spaced interference fringes and the temporal decorrelation of interferometric synthetic aperture radar (InSAR) interferograms. To solve these problems, sub-band InSAR is applied to monitor the large-scale deformation that occurs in mining areas. First of all, four different bandwidth images with three sub-band bandwidth parameters are used to extract simulated mining-induced subsidence with seven different deformation magnitudes. The results of the simulation experiment suggest the following conclusions. In monitoring subsidence with different deformation magnitudes using images with different bandwidths, an optimal monitoring value exists; wider image bandwidths lead to smaller optimal monitoring values and higher monitoring accuracies. Therefore, an appropriate sub-band bandwidth should be selected that depends upon the image bandwidth and the subsidence level to achieve optimal monitoring. The optimal sub-band bandwidth for monitoring subsidence of different magnitudes in mining areas is determined through simulation experiments, and these conclusions can provide a technical basis for selecting the appropriate sub-band bandwidth for the monitoring of subsidence in mining areas. Although sub-band InSAR can reduce the number of interference fringes and the difficulty of unwrapping, the simultaneous introduction of large amounts of noise leads to reduced monitoring precision, and the application of the probability integral method in the prediction of mine subsidence is more mature. Therefore, the combined use of sub-band InSAR and the probability integral fusion method to monitor mining-induced deformation is proposed in this paper. The probability integral method is used to perform noise peeling on the interferometric phases of the sub-bands to improve the monitoring accuracy of sub-band interferometry. Then, according to the results of the simulation experiment, the fusion method with the appropriate sub-band bandwidth parameters is applied to monitor the surface deformation associated with working face 52,304 from 2 December 2012 to 13 December 2012. Finally, the monitoring results are compared with the results of monitoring using conventional differential interferometric synthetic aperture radar (D-InSAR) and global positioning system (GPS) field survey data. The results show that the reliability and accuracy of the fusion method are much better than those of conventional D-InSAR in monitoring the large-scale deformation that occurs at the edges of subsidence basins.

Monitoring and Analysis of Surface Subsidence in Mining Area Based on SBAS-InSAR

DInSAR technique is easily influenced by decorrelation of time and space and atmosphere delay, SBAS(small baseline subset technique) was applied to process 13 scene of TerraSAR-X data. The residual DEM error, atmospheric delay error and orbit error are estimated and removed. The maximum subsidence rates of 2310 and 1301 working faces were 40 mm/a and 50 mm/a respectively by analyzing the subsidence rate of the coal mine area from 2012 to 2013. It found that the land subsidence of 2306, 2308, 2310 working faces is not obvious before November 15, 2012 by analyzing the timing cumulative subsidence map. Three candidate points of slowly decorrelation filter phase in the 2310 and the 1301 working faces were extracted respectively to be analyzed and we found that the relationship between sedimentation value and time was linear, moreover, the earlier the mining time was, the more the linear variation of the sedimentation characteristics was. Cumulative settlement values obtained by the SBAS and DInSAR were compared and analyzed and turned out that the difference between the two methods was less than 5 mm. The time sequence subsidences of several points in the trending and orientation of 2310 working face were selected and extracted, the surface subsidence of study area in different time period was quantitative analysis by analyzing the displacements of these points. Experiments show that SBAS-InSAR technology has a good application prospect in the monitoring and analysis of surface subsidence in mining area.

ANALYSIS OF LAND SUBSIDENCE MONITORING IN MINING AREA WITH TIME-SERIES INSAR TECHNOLOGY

Abstract. Time-series InSAR technology has become a popular land subsidence monitoring method in recent years, because of its advantages such as high accuracy, wide area, low expenditure, intensive monitoring points and free from accessibility restrictions. In this paper, we applied two kinds of satellite data, ALOS PALSAR and RADARSAT-2, to get the subsidence monitoring results of the study area in two time periods by time-series InSAR technology. By analyzing the deformation range, rate and amount, the time-series analysis of land subsidence in mining area was realized. The results show that InSAR technology could be used to monitor land subsidence in large area and meet the demand of subsidence monitoring in mining area.

Superposition model for analyzing the dynamic ground subsidence in mining area of thick loose layer

The dynamic ground subsidence due to underground mining is a complicated time-dependent and rate-dependent process. Based on the theory of rock rheology and probability integral method, this study developed the superposition model for the prediction and analysis of the ground dynamic subsidence in mining area of thick loose layer. The model consists of two parts (the prediction of overlying bedrock and the prediction of thick loose layer). The overlying bedrock is regarded as visco-elastic beam, of which the dynamic subsidence is predicted by the Kelvin visco-elastic rheological model. The thick loose layer is regarded as random medium and the ground dynamic subsidence is predicted by the probability integral model. At last, the two prediction models are vertically stacked in the same coordinate system, and the bedrock dynamic subsidence is regarded as a variable mining thickness input into the prediction model of ground dynamic subsidence. The prediction results obtained were compared with actual movement and deformation data from Zhao I and Zhao II mine, central China. The agreement of the prediction results with the field measurements show that the superposition model (SM) is more satisfactory and the formulae obtained are more effective than the classical single probability integral model (SPIM), and thus can be effectively used for predicting the ground dynamic subsidence in mining area of thick loose layer.