Large-gradient Deformation Research Articles

Detection and monitoring of mining-induced subsidence with Gaofen-3 and Sentinel-1A SAR datasets

ABSTRACT The mining-induced subsidence has caused severe casualties and poses a threat to the safe operation of coal extraction, especially under the roads, buildings and rivers. This alarming situation demands the large-area subsidence detection and the large-gradient deformation monitoring to ensure safety. Although the traditional Interferometric Synthetic Aperture Radar (InSAR) technology can monitor surface deformation in high accuracy, unfortunately, it often fails to recover the sufficient deformation when facing the effects of decorrelation and large gradient displacement. Therefore, this study firstly proposes a Stacking-InSAR and coherence gradient fusion method to detect the mining-induced subsidence over large area. Secondly, the robust offset tracking method is applied to retrieve the large-gradient deformation in Buertai mining site. Both Gaofen-3 and Sentinel-1A Synthetic Aperture Radar (SAR) datasets are acquired over the border of Shaanxi and Inner Mongolia, China, for experimental test. The results show that 179 mining-induced subsidence sites are detected with the proposed method, and the complete spatial temporal deformation fields are obtained by both Gaofen-3 and Sentinel-1A offset tracking method with maximum deformation of 3 m in range direction. This study provides a valuable tool for detecting the fine boundary of mining subsidence and recovering the accurate deformation time series.

TDFPI: A Three-Dimensional and Full Parameter Inversion Model and Its Application for Building Damage Assessment in Guotun Coal Mining Areas, Shandong, China

Subsidence prediction is essential for preventing and controlling geohazards in coal mining areas. However, the Interferometric Synthetic Aperture Radar (InSAR) technique is limited in deriving the goaf displacements with a large gradient and fast deformation rates, hindering its application for potential risk evaluation over the mining areas. In this study, we proposed a three-dimensional and full parameter inversion (TDFPI) model to derive the large-gradient subsidence and then investigate its application for building damage assessment over coal mining areas. By taking the Guotun coal mine as the case study, the TDFPI model was demonstrated to have effectively predicted the large-gradient deformation of the mining areas and successfully evaluated the house damage in Chelou village, which agrees well with our field investigations. Specifically, the predicted subsidence results were validated with high fitting accuracy against field measurements, with RMSE of 0.083 m and 0.102 m, respectively, on observation line A and line F. In addition, the classified damage levels are highly consistent with in situ field surveys for the house cracks in Chelou village, presenting its practicality and effectiveness for building damage evaluation, and thus can provide a useful tool for potential risk assessment and prevention over the mining areas.

Genetic algorithm-based parameter inversion and pipeline subsidence prediction

To invert mine subsidence parameters and predict the timing of associated risk to underground pipelines passing through mining areas with incoherent subsidence information, this study proposes a model based on combining the advantages of interferometric synthetic aperture radar (InSAR) technology, the probability integral method (PIM), segmented Knothe time function, and genetic algorithm. Firstly, InSAR technology is used to analyze a time series of surface deformation in a mining area, circle the range of large-gradient deformation, and distinguish the subsidence edge from the subsidence center. In the case of the subsidence edge (small-scale deformation), InSAR results are retained, whereas in the case of the subsidence center (large-scale deformation), subsidence parameters, such as the subsidence coefficient and influence radius, are obtained via the genetic algorithm. The subsidence of the working face in the mining area is determined by combining InSAR information and the PIM parameter inversion-based basin model. This allows us to obtain continuous surface subsidence information, study surface subsidence trends over time, and predict future pipeline subsidence. The researchers subsequently test the efficacy of this model in the Qinchi mining area in Shanxi Province, China. Sentinel-1A images were used as the data source, combine the PIM and segmentation time function, and use the genetic algorithm to obtain the subsidence mining parameters. The inversion results show that the subsidence coefficient is 0.5917 (with an error of 2%), and the main influence angle tangent value is 1.927 (with an error of 2.18%), indicating high inversion accuracy. Based on this model, the study predict that subsidence in the Qinchi mining area will threaten the structural integrity and operation of the intersecting pipeline by November 2025.

An Extraction Method for Large Gradient Three-Dimensional Displacements of Mining Areas Using Single-Track InSAR, Boltzmann Function, and Subsidence Characteristics

This paper presents an extraction method for large gradient three-dimensional (3-D) displacements of mining areas using single-track interferometric synthetic aperture radar (InSAR), Boltzmann function, and subsidence characteristics. This is mainly aimed at overcoming the limitations of surface deformation monitoring in mining areas by using single-track InSAR technology. One is that the rapid and large gradient deformation of the mine surface usually leads to image decoherence, which makes it difficult to obtain correct deformation information. Second, the surface deformation monitored by InSAR is only one-dimensional line of sight (LOS) displacement, and thus it is difficult to reflect the real 3-D displacements of the surface. Firstly, the Boltzmann function prediction model (BPM) is introduced to assist InSAR phase unwrapping; thus the missing large gradient deformation phase of InSAR is recovered. Then, the subsidence characteristics in mining horizontal (or near-horizontal) coal seams are used as prior knowledge for theoretical derivation, and a 3-D displacement extraction model of coal seam mining with single-track InSAR is constructed. The feasibility of the method is verified by simulating LOS displacements with random noise and underestimation phenomenon caused by the large gradient deformation as InSAR observations. The results show that the root mean square error (RMSE) of 3-D displacements on the observation line calculated by the proposed method is 21.5 mm, 19.0 mm, and 32.9 mm, respectively. Based on the single-track Sentinel-1 images, the method in this paper was applied to the extraction of surface 3-D displacements in the Huaibei coal mine, and the experimental results show that the extracted 3-D displacements are in good agreement with that of measurement by the surface observation station. The proposed method can adapt to limited InSAR acquisitions and complex monitoring environments.

Study on the Source of Debris Flow in the Northern Scenic Spot of Changbai Mountain Based on Multi-Source Data

The northern scenic area of Changbai Mountain is a high-incidence area of debris flow disasters, which seriously threaten the safety of tourist’s lives and property. Monitoring debris flow and providing early warning is critical for timely avoidance. Monitoring the change of debris flow source is an effective way to predict debris flow, and the change of source can be reflected in the settlement deformation of the study area. The offset tracking technique (OT) is insensitive to the coherence of SAR images and can resist the decoherence of D-InSAR and SBSA-InSAR to a certain extent. It is a technical means for monitoring large gradient deformation. It has been widely used in the field of seismic activity, glaciers and landslides in recent years, but few scholars have applied this technique in the field of debris flow. In this paper, we use OT techniques in combination with field surveys, Google imagery and Sentinel-1 data to monitor surface deformation in the northern scenic area of Changbai Mountain in 2017 and use D-InSAR techniques to compare and complement the OT monitoring results. The results of this study show that for monitoring surface deformation in the study area after a mudslide, it is better to use both methods to determine the surface deformation in the study area rather than one, and that both methods have their own advantages and disadvantages and yet can complement each other. Finally, we have predicted the development trend of mudflows in the study area by combining the calculated single mudflow solids washout, which will help to improve the long-term monitoring and warning capability of mudflows in the study area. The study also enriches the application of offset-tracking technology and D-InSAR in the field of geohazard monitoring and provides new ideas and methods for the study of mudflow material source changes.

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.

DS-InSAR Based Long-Term Deformation Pattern Analysis in the Mining Region With an Improved Phase Optimization Algorithm

The environment management and land utilization of the abandoned mining region is critically dependent on precise monitoring and comprehensive understanding of mining subsidence. In order to overcome the shortcomings of the traditional distributed target phase optimization method in the space continuity constraints of adjacent pixels, an improved phase optimization algorithm was proposed, which combines region growing and time series interferometric synthetic aperture radar with distributed scatterers (DS-InSAR). By using 17 L-band and 51 C-band SAR images, the characteristics of temporal and spatial distribution in Peibei mining area of Xuzhou, China, were obtained during the period from 2007 to 2011 and the period from 2017 to 2020. With the long-term monitoring, the evolution of deformation in the mining area was carried out. The modified phase optimization technique has proven its ability in the density of measurement points and the influence of noise in space, which is promising for the detection of large gradient deformation and the accurate analysis of surface deformation in mining areas. The study has been concentrated toward detecting continuous subsidence in the mining region. Coal mines in operation are usually accompanied by unstable ground, and the uplift or second subsidence has sometime occurred in the closed mine region. Conclusively, the presented methodology is practically feasible for long-term deformation pattern analysis in coal-exhausted mining areas.

A DPIM-InSAR method for monitoring mining subsidence based on deformation information of the working face after mining has ended

ABSTRACT The mining of underground coal resources causes long-period and large-gradient deformation of the overlying surface of the working face. Due to the characteristic of long period of deformation, D-InSAR (Differential Interferometry Synthetic Aperture Radar) technology can be easily affected by temporal incoherence. When the surface deformation gradient exceeds the monitoring gradient of D-InSAR technology, the conventional D-InSAR technology will easily cause unwrapping failure, and thus is difficult to realize 3D (Three Dimensional) monitoring of mining-induced surface deformation. In order to solve the problems, a prediction method of 3D surface deformation based on D-InSAR and constraints of DPIM (Dynamic Probability Integral Method) model was studied in this paper. Firstly, SAR image data of the working face after the end of mining were processed by differential interference to obtain the LOS surface deformation during a period of time after the mining. Secondly, according to the relationship between D-InSAR LOS (Line Of Sight) deformation and 3D surface deformation, the D-InSAR observation equation for mining subsidence based on DPIM constraints was constructed. Then, the solution method of the condition equation of DPIM-InSAR (Interferometry Synthetic Aperture Radar) based on GA (Genetic Algorithm) was constructed. Finally, based on obtained LOS deformation, the 3D surface deformation of surface subsidence was obtained using the 3D prediction method based on DPIM-InSAR. The results of simulation experiments effectively verified the feasibility of the method. Differential interference processing was performed for Sentinel-1A data after the mining. The LOS deformation of the 1613 working face of Guqiao South Mine between 20 January 2018 and 25 February 2018 was obtained. 3D surface deformation from the beginning of the mining to completion was obtained using the proposed prediction method of 3D surface deformation and subsidence based on DPIM-InSAR (Dynamic Probability Integral Method). The predicted subsidence values were compared with the measured values. The results showed that the fitting errors of the LOS deformation were mostly within 3 mm. The fitting mean square error was ± 2.37 mm (accounting for 6.7% of the maximum LOS deformation value), and the fitting accuracy was high. The fitting mean square error of subsidence was ± 120 mm (accounting for 7.3% of the maximum subsidence). The results in engineering application show that the proposed 3D predicting method of surface deformation and subsidence based on DPIM-InSAR has certain practical values.

DInSAR Monitoring of Surface Subsidence by Fusing Sentinel-1A and -1B Data to Improve Time Resolution in a Mining Area

Monitoring large gradient ground deformation due to temporal and spatial image decoherence has long been a challenge. We attempted an improvement using Sentinel-1A and Sentinel-1B C-band data fusion methods based on the variation law of subsidence velocity of ground leveling monitoring points. This approach improved the temporal resolution from 12 to 6 days. Using a mine in Datong, Shanxi Province, China as an example, 13 scenes of Sentinel-1A data and 12 scenes of Sentinel-1B data were fused and compared with ground-measured data. The results obtained were closer to the measured values than those obtained by single data set (Sentinel-1A or -1B only). Simultaneously, 61 scenes of Sentinel-1A data and 12 scenes of Sentinel-1B data were used to calculate the subsidence of the mining area over two years. The subsidence map was consistent with the actual leveling trend, which reflected the dynamic change of surface subsidence range in the mining area. This study provides an approach using DInSAR technology to monitor large gradient deformation in mining areas and provides an effective method to monitor of surface dynamic deformation.

Large-gradient deformation monitoring and parameter inversion in a mining area using a method combining a dynamic prediction model and InSAR

ABSTRACT It is a challenge to map large-gradient deformation over mining areas via current InSAR (interferometric synthetic aperture radar) techniques. To this end, we present a new method that is combined with the DPM (dynamic prediction model). Our method can be divided into two parts. The core of the first part is a DPM-based simulation and removal for the large-gradient phase. The phase continuity can be restored to a great extent as the simulation has considered the delayed effect of underground mining. In this step, we can determine the DPM parameters following the rule of thumb. In the second, with LOS (line-of-sight) displacement observed from the first part, a multi-objective GA (genetic algorithm) was used to refine the DPM parameters and the subsequent deformation was predicted. Finally, we show an example of the proposed method in the case of a mine in Huaibei, China.

Update two-dimensional SAR offset tracking deformation time series with complex sequential least squares estimation

ABSTRACT Unlike synthetic aperture radar (SAR) interferometry, SAR offset tracking method can measure two-dimensional (2-D) large-gradient deformation with precision as 1/10 to 1/30 pixels size. In terms of 2-D deformation time series estimation, the pixel-offset small baseline subset (PO-SBAS) method was proposed. However, little research has been conducted on the dynamic estimation of 2-D deformation time series for new SAR acquisitions, which has increasing demand with the availability of SAR data. In this letter, we introduced a novel method to update 2-D deformation time series dynamically under the frame of PO-SBAS. Firstly, complex least squares (CLS) method is derived to solve the complex linear function model, and the difference between the CLS estimation in complex value and the LS estimation in real value for the real and imaginary parts, respectively, are compared. Secondly, complex sequential least squares (CSLS) estimation is proposed to update 2-D deformation time series dynamically and efficiently. Finally, both simulated and real SAR data verified the performance of the proposed method. It can be taken as a promising SAR tool for dynamic 2-D deformation estimation for the scenarios of glacier movement, mining-induced displacement, and co-seismic deformation, etc.

Probability Integral Method Parameter Determination by SBAS‐InSAR Technology and GWO Algorithm

This paper proposed a method based on the SBAS‐InSAR and gray wolf optimization algorithm aiming at the time‐consuming and laborious defects of the traditional method used to obtain the expected parameters of the probability integral method and the shortcomings of the InSAR technology in the field of large gradient deformation detection in the mining area. The fitness function of the algorithm was established based on the geometric relationship between the radar side imaging and the three‐dimensional model of the probability integral method. The stable sinking point of the settlement boundary obtained by SBAS‐InSAR was used as the input value for the calculation of the predicted parameters of the probability integral method. Firstly, the simulation experiment was employed for the simulation of the direction of the InSAR line of sight combined with the geological mining conditions of the assumed working face, thereby obtaining the probability integral prediction parameters of the working face. Consequently, the maximum relative error of q, b, tanβ, and θ0 does not exceed 8%, and that of S1, S2, S3, and S4 does not exceed 35.5% (low parameter sensitivity). The error of the LOS‐direction deformation fitting is 0.076 m, which meets the tolerance requirements, and the result is trustworthy. At last, the parameter finding method is applied to the engineering example, that is, the 112201 working face of Xiaobaodang Coal Mine in the northern Shaanxi mining area. The settlement value of the stable boundary point is obtained based on the SBAS‐InSAR results, which is substituted into the fitness function. And the GWO optimization algorithm is used for optimization and parameter finding; the probability integral expected parameters of the working face are calculated as q = 0.63, b = 0.37, tanβ = 2.76, θ0 = 83.94, S1 = −36.34 m, S2 = 26.69 m, S3 = −45.64 m, and S4 = 39.62 m. Substitute the obtained parameters into the probability integral model for the prediction of the vertical and horizontal displacements of the working face, and verify its accuracy with the GPS measured data. The results showed that the maximum absolute error of vertical displacement reached 116 mm, the median error was 63 mm, and the maximum absolute error of north‐south horizontal movement reached 56 mm; meanwhile, the median error was 23 mm, the maximum absolute error of east‐west horizontal movement reached 61 mm, and the median error was 29 mm; all the above parameters are within the tolerance range, indicating that the method for the calculation of probability integral parameters proposed in this paper is applicable in actual engineering.

Retrieving 3D Large Gradient Deformation Induced to Mining Subsidence Based on Fusion of Boltzmann Prediction Model and Single-Track InSAR Earth Observation Technology

Surface deformation of mining area caused by mining subsidence can be divided into three stages: the initial stage, the active stage and the decline stage. At the active stage, D-InSAR technology could be easily affected by the incoherence of large-gradient deformation, thus leading to the failure of conventional D-InSAR technology in monitoring mining subsidence.In order to solve the problem, this paper developed a 3D surface deformation prediction method of mining subsidence by integrating dynamic Boltzmann prediction model with D-InSAR. It was firstly to monitor the surface movement and deformation by D-InSAR technology during the decline stage and obtain the surface LOS deformation field data. Then, on the basis of Boltzmann model, the geometric equation between LOS deformation by D-InSAR monitored and 3D deformation was established. Next, the geometric equation was solved based on the GA theory, and the parameters of Boltzmann prior model were obtained. On the basis, the 3D deformation of mining area were obtained, proving that D-InSAR technology was effective to get 3D deformation of mining subsidence. Through simulation experiments, the effectiveness of the proposed method is verified. The surface deformation of 1613 working face in Guqiaonan coal mine was monitored by D-InSAR during the decline stage, and the LOS deformation values were obtained. The 3D deformation was obtained from the initial stage to the decline stage by using the method of DB-InSAR. The monitoring results showed that the fitting errors in the LOS deformation were mostly within 3 mm, the RMSES was ±2.37 mm, shown that the fitting accuracy was high. The RMSE of subsidence were ±120 mm. The results showed that the method of DB-InSAR has engineering application values.

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.

Prediction of 3D deformation due to large gradient mining subsidence based on InSAR and constraints of IDPIM model

ABSTRACT After underground coal resources are mined out in mining areas, large-gradient deformation is prone to occur on the ground surface during a short period of time. When the deformation gradient exceeds the threshold value of the gradient monitored by the D-InSAR (Differential Interferometry Synthetic Aperture Radar) technology, the conventional D-InSAR technology is likely to cause the failure of InSAR (Interferometry Synthetic Aperture Radar) phase unwrapping algorithms. In this case, neither the conventional D-InSAR technology nor the method of prior model + InSAR can obtain the 3D (three-dimensional) deformation field of mining-induced subsidence. Aiming at these problems, a method of monitoring 3D deformation of mining subsidence with large gradient was developed in this paper, which combines the improved dynamic prediction model and a single pair of D-InSAR. The probability integral method was firstly improved, and its problem of fast convergence at the edge of the subsidence basin was solved. Secondly, according to the geometric projection relationship between the LOS (Light Of Sight) deformation of D-InSAR in the mining area and the 3D surface deformation, the observation condition equation of the mining subsidence monitored by D-InSAR based on the improved dynamic model of PIM (Probability Integral Method) was established. Then, the solving model of prediction parameters of the improved dynamic model of PIM was constructed according to the GA (Genetic Algorithm) theory. Finally, based on the LOS deformation obtained by D-InSAR technology, the 3D deformation of mining subsidence is obtained by the 3D deformation predicting method proposed in this paper. The feasibility of the method was verified by the simulation experiment results. The LOS deformation at the edge of the subsidence basin was obtained by the differential interference processing of data from two images of Sentinel-1A on 16 November 2017 and 10 December 2017 of 1613 working face of Guqiao South Mine. The 3D deformation of the surface in the mining area during this period were obtained by using the method proposed in this paper. The predicted subsidence value was compared with the measured surface subsidence value. The result is as follow: the maximum error value of subsidence was 60 mm, about 9.1% of the maximum value of subsidence, and the fitting error of mean square of subsidence was ± 26.21 mm. The results show that the predicting 3D deformation method of D-InSAR for large gradient of mining subsidence based on constraints of improved dynamic prediction model proposed in this paper has certain engineering application values.

Improving PSI Processing of Mining Induced Large Deformations with External Models

Ground subsidences, either caused by natural phenomena or human activities, can threaten the safety of nearby infrastructures and residents. Among the different causes, mining operations can trigger strong subsidence phenomena with a fast nonlinear temporal behaviour. Therefore, a reliable and precise deformation monitoring is of great significance for safe mining and protection of facilities located above or near the mined-out area. Persistent Scatterer Interferometry (PSI) is a technique that uses stacks Synthetic Aperture Radar (SAR) images to remotely monitor the ground deformation of large areas with a high degree of precision at a reasonable cost. Unfortunately, PSI presents limitations when monitoring large gradient deformations when there is phase ambiguity among adjacent Persistent Scatterer (PS) points. In this paper, an improvement of PSI processing, named as External Model-based Deformation Decomposition PSI (EMDD-PSI), is proposed to address this limitation by taking advantage of an external model. The proposed method first uses interferograms generated from SAR Single Look Complex (SLC) images to optimize the parameter adjustments of the external model. Then, the modelled spatial distribution of subsidence is utilized to reduce the fringes of the interferograms generated from the SAR images and to ease the PSI processing. Finally, the ground deformation is retrieved by jointly adding the external model and PSI results. In this paper, fourteen Radarsat-2 SAR images over Fengfeng mining area (China) are used to demonstrate the capabilities of the proposed method. The results are evaluated by comparing them with leveling data of the area covering the same temporal period. Results have shown that, after the optimization, the model is able to mimic the real deformation and the fringes of the interferograms can be effectively reduced. As a consequence, the large gradient deformation then can be better retrieved with the preservation of the nonlinear subsidence term. The ground truth shows that, comparing with the classical PSI and PSI with unadjusted parameters, the proposed scheme reduces the error by 35.2% and 20.4%, respectively.

Coal Mining Deformation Monitoring Using SBAS-InSAR and Offset Tracking: A Case Study of Yu County, China

Ground deformation caused by underground coal mining has a large subsidence gradient and high nonlinearity and may cause continuous destruction to the surface structure after mining. A synthetic aperture radar interferometry (InSAR) is a powerful method for measuring and reconstructing the ground displacement. In this article, a total of 19 descending orbit X-band TerraSAR-X and 16 ascending orbit C-band Sentinel-1 images were integrated to monitor the ground displacement of a coal mining area in Yu County (Hebei Province, China) using a small baseline subset (SBAS-InSAR) algorithm. We found that the SBAS-InSAR technology can obtain reliable results in regions without a high gradient deformation by combining the deformation rates derived from both the TerraSAR-X and Sentinel-1 datasets. The maximum subsidence rate was approximately 125 mm/y between 17 June 2015 and 07 January 2016. In the case of large-gradient deformation, it is indeed difficult to obtain the mining-induced surface subsidence information accurately using the conventional multitemporal (MT-InSAR) techniques owing to the limitations of the SAR wavelength. In response to this problem, a new decision-making fusion method based on SBAS-InSAR and offset tracking was developed to monitor the large-gradient settlement in mining areas. The results show that the method developed compensates for the shortcomings of the traditional MT-InSAR technologies in the field of large-gradient deformation and obtains reliable results.

An Improved Adaptive Template Size Pixel-Tracking Method for Monitoring Large-Gradient Mining Subsidence

The monitoring of large-gradient deformation caused by coal mining is of great significance to the prevention and management of disasters in mining areas. The interferometric synthetic aperture radar (InSAR) method captures the small-gradient ground deformation on the edge of the subsidence basin accurately but is unreliable for capturing large-gradient deformation. The intensity-based pixel-tracking method (e.g., the normalized cross-correlation (NCC) method) can overcome the limitations of InSAR’s maximum detectable displacement gradient and incoherence. However, the pixel-tracking method is sensitive to template size. It is difficult to estimate ground subsidence accurately by the conventional pixel-tracking method with fixed template size. In this paper, the signal-to-noise ratio (SNR) is redefined and an improved locally adaptive template size method is proposed by identifying optimal template adaptively based on maximization of the redefined SNR. The constraint radius is used to constrain the search area in this improved method. The frequency of misrepresentation is reduced by finding the peak of the correlation coefficient surface within the search area. Both simulation data and real ground subsidence data are used to test this algorithm. The results show that this method can improve monitoring accuracy compared with the traditional pixel-tracking method for fixed template size.

Combining SAR interferometric phase and intensity information for monitoring of large gradient deformation in coal mining area

Coal mining usually causes drastic large gradient deformation in the earth's surface in a short period, which leads to the formation of collapse basin or collapse pit. The drastic deformation renders the generation of dense fringes in interferogram, leading to difficulties in the phase unwrapping step. In this paper we propose using intensity tracking technique to monitor large gradient deformation in the subsidence center. To obtain the small deformation information around the basin and collapse pit, we use Small Baseline Subset (SBAS) interferometry technique to monitor the small deformation, after which we integrate the results under the two methods to obtain complete monitoring results. We take Daliu Tower mining of Shanxi Province in China as an example, using Terra SAR-X satellite data of 13 scenes spanning from 21/11/2012 to 02/04/2013 to conduct deformation monitoring experiment. The experiment result shows that the proposed method has the capability to detect and measure the large gradient deformation, meanwhile, small deformation information is preserved. Moreover, by comparing the experiment monitoring results to continuous GPS measurements, we estimate that the accuracy of deformation monitor is about 8 cm in the range direction.

Mining collapse monitoring with SAR imagery data: a case study of Datong mine, China

A mining-induced collapse is often characterized by large deformation gradient, spatial discontinuity, and temporal nonlinearity, resulting in the loss of interferometric SAR (InSAR) coherence and consequently subsidence information in areas of steep deformation gradients. In this study, we present different SAR deformation monitoring schemes to map the mining-induced subsidence and collapse. First, SAR data with different wavelengths, including C-band ERS-1, C-band Envisat ASAR, and X-band TerraSAR-X data, are used to highlight three mining subsidence stages and their temporal evolutions over Datong mine (China) in the past 20 years. Mining-induced subsidence over a region can be delimited from InSAR deformation maps, where InSAR coherence is lost over the area of peak subsidence. Second, in order to monitor the large-gradient surface deformation caused by underground mining activities, three SAR deformation monitoring schemes are proposed, including a full-resolution interferogram method, a “remove-restore” phase unwrapping method, and a fusion of phase and offset measurements. Then, taking the Datong coalfield as an example, we demonstrate the capabilities of these methods on mapping large-gradient deformation. Finally, we have found that over 80% of coalfields have deformed during the past 20 years. We conclude that the fusion of the InSAR phase and offset measurements can provide a reliable estimate of large-gradient mining-induced deformation.