Revealing Long-Term Displacement and Evolution of Open-Pit Coal Mines Using SBAS-InSAR and DS-InSAR

Abstract

Coal mines play an important role in the global energy supply. Monitoring the displacement of open-pit mines is crucial to preventing geological disasters, such as landslides and surface displacement, caused by high-intensity mining activities. In recent years, multi-temporal Synthetic Aperture Radar Interferometry (InSAR) technology has advanced and become widely used for monitoring the displacement of open-pit mines. However, the scattering characteristics of surfaces in open-pit mining areas are unstable, resulting in few coherence points with uneven distribution. Small BAseline Subset InSAR (SABS-InSAR) technology struggles to extract high-density points and fails to capture the overall displacement trend of the monitoring area. To address these challenges, this study focused on the Shengli West No. 2 open-pit coal mine in eastern Inner Mongolia, China, using 201 Sentinel-1 images collected from 20 May 2017 to 13 April 2024. We applied both SBAS-InSAR and distributed scatterer InSAR (DS-InSAR) methods to investigate the surface displacement and long-term behavior of the open-pit coal mine over the past seven years. The relationship between this displacement and mining activities was analyzed. The results indicate significant land subsidence was observed in reclaimed areas, with rates exceeding 281.2 mm/y. The compaction process of waste materials was the main contributor to land subsidence. Land uplift or horizontal displacement was observed over the areas near the active working parts of the mines. Compared to SBAS-InSAR, DS-InSAR was shown to more effectively capture the spatiotemporal distribution of surface displacement in open-pit coal mines, offering more intuitive, comprehensive, and high-precision monitoring of open-pit coal mines.

1. Introduction

Coal is the most widely distributed and abundant fossil fuel on Earth and is known for its low cost and reliable supply, accounting for over 30% of global energy consumption [1]. Open-pit mining is a primary method of coal extraction, known for its high efficiency and cost-effectiveness, and its share of total coal production continues to grow [2]. Continuous mining operations in open-pit areas have led to the formation of numerous waste materials and loose waste layers over time [3]. Due to large-scale, high-intensity mining, open-pit coal mining areas often experience surface displacement and ecological disasters, posing significant risks to mining safety [4]. As a result, monitoring and analyzing displacement caused by open-pit mining are crucial [5].

Currently, monitoring systems for mining areas primarily rely on traditional methods such as total station surveys and global navigation satellite systems (GNSSs). These traditional tools have been in use for many years, with mature technologies that can meet the basic requirements for monitoring mining displacement [6]. However, these technologies are limited by the sparsity of measurement points, as they can only measure specific target locations, making it difficult to cover the entire area of interest and resulting in high human and economic costs [7,8]. Consequently, there is a need for more effective and comprehensive monitoring technologies. In recent decades, InSAR has been used to collect multiple SAR images of the same area by emitting microwave electromagnetic waves and capturing the backscatter from surface objects [9]. It enables low-cost, high-resolution, and precise sub-wavelength distance change measurements [10]. It has been widely used to monitor various geological hazards, including land subsidence [11,12,13], landslides [14,15], and more.

The literature contains studies that have successfully examined land deformations caused by open-pit mining activities using InSAR time series. Eker et al. demonstrated the detection of mining-induced landslides at Himmetoğlu through the integration of persistent scatterer InSAR (PS-InSAR) and SBAS-InSAR techniques, revealing the potential of satellite SAR data in identifying deformation patterns and failure precursors [16]. Similarly, Wei et al. [5] and Lu et al. [17] employed SBAS-InSAR to analyze slope deformation in China′s Fushun West and Qidashan open-pit mines, respectively, providing critical insights into slope stability assessment in mining operations. However, the surface scattering characteristics in open-pit mining areas are unstable, with few coherent point targets and uneven distribution. Both PS-InSAR [18] and SABS-InSAR [19] technologies cannot capture the overall displacement trend of the mining area due to a lack of sufficient monitoring points. To address these limitations, the concept of distributed scatterers (DSs) was introduced. In 2007, Rocca et al. introduced distributed scatterers [20] and applied the phase optimization of DSs using Markov models. Ferretti et al. proposed SqueeSAR technology [21], which accounts for the statistical characteristics of DS pixel targets. The processing is divided into two steps, homogeneous pixel recognition and phase optimization, to reconstruct the deformation of DSs. Jiang et al. proposed the FaSHPS method for the rapid statistical identification of homogeneous pixels [22], which iteratively refines confidence intervals based on the central limit theorem. This method enhances computational efficiency, preserves resolution, suppresses noise, and mitigates bias. DS-InSAR technology has been widely used to monitor surface displacement in low-coherence areas, such as landslides in vegetated regions [23,24].

DS-InSAR technology has become a key focus of research aimed at improving monitoring point density and reducing phase errors caused by spatiotemporal decoherence [25]. Fadhillah et al. developed a DS-InSAR framework for low-coherence regions, achieving higher point density in open-pit mining activity areas [26]. Despite these advances, the applications of DS-InSAR in open-pit coal mines remain relatively limited, particularly in regions with dynamic mining activities and complex surface conditions. The displacement in open-pit mining areas is strongly correlated with mining activities. Understanding these connections is crucial for analyzing the displacement mechanisms of mining areas. In this study, we aimed to analyze the factors influencing surface displacement in open-pit mines by extracting displacement from high-density measurement points using advanced DS-InSAR technology. A total of 201 Sentinel-1 ascending orbit images were collected from the Shengli West No. 2 mining area in Xilinhot, Inner Mongolia, covering the period from 20 May 2017 to 13 April 2024. The effectiveness of DS-InSAR technology was evaluated and validated against SBAS-InSAR technology, with the accuracy of the displacement results further verified using GNSS data. Finally, the impact of open-pit mining activities on the spatiotemporal evolution of displacement in the mining area was analyzed comprehensively. This study contributes to the research on DS-InSAR technology for displacement monitoring in open-pit mines and provides valuable insights into the spatiotemporal evolution of displacement in mining areas.

2. Study Area and Datasets

2.1. Study Area

The Shengli Coalfield is situated in the northwest of Xilinhot City, Inner Mongolia. Its southeastern boundary lies 2 km from the city center (Figure 1). The study area is located in the southwestern part of the Shengli Coalfield, representing a transitional zone between eroded accumulation terrain and low, gentle hilly terrain. The terrain elevation ranges from 1012 to 1148 m, with a relative height difference of 136 m. The geological setting features stable slopes with no significant geotechnical instabilities observed, and no specific unstable areas have been identified within the mining domain. It is a typical open-pit mining area in the grasslands of Eastern China and is recognized as one of the top ten coal mines in Inner Mongolia, owing to its abundant reserves. These include the Shengli East No. 2 open-pit mine, No. 1 open-pit mine, West No. 3 open-pit mine, West No. 2 open-pit mine, and WuLanTuGa germanium open-pit mine, which are distributed in a northeast–southwest strip formation. The Shengli West No. 2 open-pit mine, designed for an annual output of 10.0 Mt, borders the Mengdong Energy WuLanTuGa germanium open-pit mine to the west. The surface width ranges from 1.9 to 3.3 km north–south and 3.4 to 4.0 km east–west, covering an area of approximately 12.3 square kilometers. Mining operations officially began in 2007. The mine surface is covered by Quaternary–Neogene unconsolidated deposits (topsoil, silty clay, sand, and gravel) with thicknesses ranging from 0.65 to 48.60 m (mean 18.31 m).

2.2. Datasets

This study used 201 Sentinel-1 ascending orbit images covering the entire area of the Shengli West No. 2 open-pit mine, spanning from 20 May 2017 to 13 April 2024, with an incident angle of 43.8°. The main parameters of the Sentinel-1 images used in this study are presented in

Table 1. Main parameters of the Sentinel-1 data.

Parameter	Value
Flight direction	Ascending
Beam mode	IW
Polarization	VV
Wave band	C
Wavelength/cm	5.6
Number of images	201
Monitored period	20 May 2017–13 April 2024

. The Copernicus DEM Global GLO-30, released by the European Space Agency, was used to remove terrain phase and perform geocoding. The GLO-30 product was generated from radar satellite data obtained during the TanDEM-X mission, which took place from 1 January 2011 to 1 July 2015 [27].

3. Methodology

The unstable surface scattering characteristics in the displacement areas of open-pit mines prevent the extraction of high-density points. The effectiveness of PS-InSAR and SBAS-InSAR methods for displacement monitoring is limited by sparse measurement points, making it difficult to capture the overall displacement trend. Additionally, SBAS-InSAR requires multi-look processing, which sacrifices spatial resolution and may overlook displacement in small areas [28], such as specific slopes and accumulation zones within the mining area. To address this limitation, this study used DS-InSAR, a suitable time series InSAR method, which enhances the density of monitoring points by extracting DS points that maintain moderate temporal coherence and spatial continuity.

In this study, deformation data for the study area were obtained using both SBAS-InSAR and DS-InSAR methods. SBAS-InSAR connects interferometric pairs by setting spatiotemporal baselines, filtering and unwrapping the differential interferograms, and using coherence to select high-coherence points in the unwrapped differential interferograms for displacement construction and inversion. DS-InSAR selects pixels with distributed scattering characteristics for homogeneous point recognition, which differs from the persistent scatterers identified by PS-InSAR. The main steps for measuring surface displacement in mining areas, based on PS and DS points, include PS and DS point selection, joint network construction, phase unwrapping, and atmospheric phase screen removal. The flow for SBAS-InSAR and DS-InSAR is illustrated in Figure 2.

DS-InSAR compensates for the inability to select reliable measurement points by identifying statistical homogeneous pixels (SHPs) in low coherence regions. The non-parametric Kolmogorov–Smirnov test was used to adaptively identify homogeneous neighborhoods. For KS hypothesis testing, the confidence level was set to 95% [29]. After determining the SHP family of each pixel, the coherence matrix of a reference pixel P was estimated by the following equation [30]:

$$\widehat{C}={\displaystyle \frac{1}{N_{SHP}}}{\displaystyle \sum _{x\in \mathsf{\Omega }}x{x}^H}$$

(1)

where $\widehat{C}$ is the covariance matrix of SHPs, $x$ is the column vector of a normalized complex value, $\mathsf{\Omega }$ is the SHP family, and $N_{SHP}$ is the number of SHP families.

The phases in correspondence with DSs were weighted optimally by the maximum likelihood estimator (MLE) under the assumption of Gaussianity [21].

$$\widehat{y}=\arg \underset{\mathsf{\Phi }}{\max }\left\{{\zeta }^H({\left|C\right|}^{-1}\circ C)\zeta \right\}$$

(2)

where ${\zeta }^H=\left[1,e^{j{\phi }_1},\dots ,e^{j{\phi }_{N-1}}\right]$, and the mathematical operation $\circ$ indicates the Hadamard product between two matrices.

4. Results

4.1. Time Series Displacement Maps

SBAS-InSAR and DS-InSAR methods were used to process 201 Sentinel-1 images covering the Shengli West No. 2 open-pit mine, extracting temporal surface displacement. Figure 3 illustrates the average displacement rates derived from SBAS-InSAR and DS-InSAR inversions, respectively. The positive Line-of-Sight (LOS) values indicate target movement toward the satellite, while negative values correspond to movement away from the satellite. Mining operations over the open-pit mine are progressing towards the south–north direction, with the overburdens, i.e., loess, gravel, and sand excavated from the working part, moved to the southern sector for reclamation purposes. The compaction process of the waste materials is the primary contributor to the significant land subsidence (maximum rate > 272 mm/y) in the southern area of the open-pit mine. Positive LOS velocity, indicating uplift, was observed in the northern part of the West No. 2 open-pit mine. This uplift is attributed to stress redistribution caused by mining activities, which triggered upward sliding of the lower rock mass along deeper weak layers.

The displacement areas identified by both methods show good consistency in their distribution. However, SBAS-InSAR suffers from two key limitations: (1) sparse measurement points due to its reliance on high coherence, resulting in insufficient coverage in low-coherence regions such as waste dumps; (2) resolution degradation caused by multi-looking processing, which averages pixels to suppress noise but sacrifices spatial resolution, thereby failing to detect small-scale displacements. Consequently, SBAS-InSAR results only roughly delineate displacement boundaries, with a displacement rate ranging from −250.9 to −100.7 mm/y and a maximum cumulative displacement of 1647.0 mm. In contrast, DS-InSAR overcomes these limitations by exploiting distributed scatterers, which are abundant in low-coherence areas like waste dumps. By adaptively identifying statistically homogeneous pixels and optimizing phase estimation without multi-looking, DS-InSAR achieves higher point density and preserves spatial resolution. This enables the precise characterization of displacement gradients and boundaries, as evidenced by its broader displacement rate range (−272.0 to −94.4 mm/y) and higher cumulative displacement (1783.8 mm).

To comprehensively analyze the spatial variation in surface displacement in the Shengli West No. 2 open-pit mine, cumulative displacement was recorded every year, starting from 20 May 2017. Figure 4 and Figure 5 illustrate the time series displacement maps calculated using SBAS-InSAR and DS-InSAR, respectively. The overall displacement trends in both results are consistent, although Figure 4 contains only a few points in the mining area and waste dump. Therefore, we focused on analyzing the DS-InSAR results, which have a higher point density. Figure 5 shows that during the monitoring period, subsidence was primarily concentrated in the southern A area and eastern B area of the waste dump, while significant uplift occurred in the northern C area of the mining area. The displacement area is unevenly distributed, and as the waste is compacted, the displacement of the waste dump spreads significantly outward from the center, forming a distinct displacement funnel. Over time, multiple displacement areas of varying extents and severity have developed within the waste dump area. The A and B areas continue to subside, forming subsidence funnels of varying scales. The annual average subsidence rate at the A area is 281.2 mm/y, with a cumulative maximum subsidence of approximately 1817.5 mm. The annual average subsidence rate at the B area is approximately 219.0 mm/y, with a cumulative maximum subsidence of around 1314.1 mm. Additionally, as mining operations progress northward, the C area of the mining site continues to uplift or horizontally move southward along the slope, with an average annual uplift rate of 98.4 mm/y and a cumulative maximum uplift of approximately 550.3 mm.

To comprehensively analyze the temporal variation in surface displacement in the Shengli West No. 2 open-pit mine, we selected feature points from three displacement areas for detailed analysis. In the subsidence area of waste dump A, typical subsidence center points, P1 and P2, were selected for analysis. As shown in Figure 6, subsidence was relatively slow from May 2017 to May 2018, after which it began to intensify, approaching a linear rate. By July 2022, subsidence began to slow down, and a slight uplift appeared in February 2024. In the subsidence area of waste dump B, point P3, located in the subsidence center, was selected for analysis. The subsidence was approximately linear from May 2017 to April 2022, after which it slowed down significantly. In the C uplift area of the mining area, we selected uplift center point P4 for analysis. The area remained stable from May 2017 to May 2019, after which the uplift began to intensify, rising approximately linearly until May 2023, when it returned to stability.

4.2. Accuracy Assessment

This study compared GNSSs with SBAS-InSAR and DS-InSAR to evaluate the accuracy of the InSAR results. The spatial distribution of the three GNSS stations (G84, G87, and G93) is shown in Figure 1. The GNSS data were taken from 25 May 2023 to 12 July 2023. The nearest InSAR monitoring point within a 20 m radius of each GNSS location was selected as the corresponding point for comparison. Since a GNSS measures three-dimensional displacement, while InSAR projects displacement onto the LOS direction, it is necessary to project the three-dimensional displacement onto the LOS direction for comparative analysis. For quantitative accuracy evaluation, the cumulative displacement of each GNSS during the monitoring period was compared, with the results shown in

Table 2. Comparison of InSAR and GNSS results.

GNSS Station	$\mathit{L}\mathit{O}{\mathit{S}}_{\mathit{G}\mathit{N}\mathit{S}\mathit{S}}$ (mm)	$\mathit{L}\mathit{O}{\mathit{S}}_{\mathit{S}\mathit{B}\mathit{A}\mathit{S}\text{-}\mathit{I}\mathit{n}\mathit{S}\mathit{A}\mathit{R}}$ (mm)	$\mathit{L}\mathit{O}{\mathit{S}}_{\mathit{D}\mathit{S}\text{-}\mathit{I}\mathit{n}\mathit{S}\mathit{A}\mathit{R}}$ (mm)	${\mathbf{D}}_{\mathbf{S}\mathbf{B}\mathbf{A}\mathbf{S}\text{-}\mathbf{I}\mathbf{n}\mathbf{S}\mathbf{A}\mathbf{R}}$	${\mathbf{D}}_{\mathbf{D}\mathbf{S}\text{-}\mathbf{I}\mathbf{n}\mathbf{S}\mathbf{A}\mathbf{R}}$
G84	1.1	−0.9	−0.8	−2.0	−1.9
G87	−8.8	−4.5	−6.3	4.3	2.5
G93	−3.8	0.5	−0.1	4.3	3.7

. The ${\mathrm{D}}_{SBAS\text{-}InSAR}$ and ${\mathrm{D}}_{DS\text{-}InSAR}$ represent the differences between the SBAS-InSAR and GNSS and the DS-InSAR and GNSS, respectively. The standard deviation for SBAS-InSAR was 3.0 mm, while for DS-InSAR, it was 2.4 mm. While the GNSS observation period is shorter than the SAR dataset, the consistency of displacement trends across both datasets suggests that the limited GNSS timespan does not undermine the validity of our findings. This indicates that the displacement results derived from DS-InSAR not only have higher point density but also greater accuracy.

5. Discussion

5.1. Analysis of the Impact of Open-Pit Mining Activities on Displacement

A SAR image was acquired annually from 20 May 2017 to 13 April 2024. By comparing images from the same period each year, a comprehensive analysis was conducted on the mining progress and spatial dynamics of the open-pit mine over the seven-year period. As shown in Figure 7, comparing and analyzing the changes in SAR images over the past seven years revealed that the mining area continues to expand northward, with the mine pit gradually increasing in size. Area D has advanced northward by approximately 0.9 km. In the SAR image from 20 May 2017, the mining boundary was at latitude 43.97492°N. By 13 April 2024, the mining boundary had advanced to latitude 43.98225°N, with an average annual advancement of about 0.13 km. During this process, the shape of the mining face also changed. Initially, the mining face was relatively flat, but as the mining depth increased, it gradually adopted a stepped shape to meet mining technology and safety requirements. Area E has advanced about 1.8 km northward, at a much faster rate than Area D. On 20 May 2017, the mining boundary was at latitude 43.96667°N. By 13 April 2024, the eastern mining boundary had reached latitude 43.98364°N, with an average annual advance of about 0.26 km. The mining face also has a stepped shape, but due to the rapid advancement, the hierarchical distribution of its steps differs from that of Area D. Detailed analysis of SAR images over the past seven years has clearly revealed the dynamic changes in open-pit mining activities over time and space, providing valuable data for resource management and mining planning.

Open-pit mining activities significantly impact the spatiotemporal evolution of displacement in the mining area. During the monitoring period, mining activities were primarily concentrated on the northern area of the open-pit mine. The mining activities in Area E were the most significant, corresponding to the displacement zone in Figure 5 (Area C), which exhibits a positive value in the LOS direction. The continuous removal of rock, soil, and coal seams during mining operations disrupts the previously balanced stress distribution of the rock mass. Under specific geological conditions, during stress readjustment, the rock and soil mass undergo displacement along the slope of the stepped mining face. The LOS direction in the ascending orbit SAR image shows positive displacement, corresponding to uplift or horizontal movement, meaning the rock and soil mass moves toward the radar. This phenomenon has also been observed in previous studies [4]. Additionally, the displacement time series for point P4 in Figure 6 shows a trend toward stabilization after May 2023. The SAR images from 1 May 2023 and 13 April 2024 clearly show that mining activities took place in the northeast corner of Area E during this period, which explains why the area around point P4 remained relatively stable.

5.2. Analysis of Displacement Mode of Open-Pit Mine Slope

Landslides in open-pit mines are the most common type of failure. When assessing the stability of open-pit coal mine slopes, the potential failure modes must first be identified and evaluated. From a rock mass structural perspective, the failure mode of slopes mainly depends on the development of joints and fissures. As shown in Figure 8, the landslide mode in the waste dump is a combination of cut layer and bedding landslide modes, typically occurring in multi-layered slopes. At this point, the displacement in the upper part of the slope is mainly downward, while the displacement in the lower part follows the weak layer′s direction [31,32]. Geometric analysis of SAR imagery shows that displacement in the direction away from the radar (i.e., the LOS direction) is negative, consistent with the InSAR results. To further explore the relationship between landslide modes and InSAR displacement data, this study conducted an assessment by integrating displacement rates with geological parameters. In Area A of the waste dump (Figure 5), the annual average subsidence rate reaches 272.0 mm/y. This corresponds to a combined cut layer–bedding landslide mode, where shallow weak layers lead to displacement concentrated in the compaction process of surface loose soil materials. It should be noted that the use of single-orbit SAR data limits the direct determination of true 3D deformation, as InSAR measures only the projection of displacement along the LOS direction. However, by integrating LOS displacements with geological surveys, slope geometry, and failure mechanisms, we inferred the deformation directions.

As shown in Figure 9, the slope in the mining area experiences a combination of cut layer, bedding and bulging landslide modes, primarily controlled by the weak layer above the coal. When the weak layer is deeply buried, cut layer, bedding and bulging landslide mode may occur. In this case, the upper part of the slope deforms mainly downward, while the lower part deforms upward [31,32]. Geometric analysis of SAR imagery shows positive displacement in the direction toward the radar (i.e., LOS direction), consistent with the InSAR results. In Area C of the mining zone (Figure 5), the positive LOS displacement (annual uplift rate of 94.4 mm/y) is closely associated with a combined cut layer–bedding and bulging landslide mode. In this area, deeper weak layers trigger the upward sliding of lower rock masses along weak layers due to stress redistribution, resulting in surface uplift.

6. Conclusions

Ground stability is a critical safety concern in open-pit mines. This study employed spaceborne InSAR time series to analyze surface displacement around the Shengli West No. 2 open-pit mine in eastern Inner Mongolia, China. The surface′s continuous changes led to unstable scattering characteristics, with few coherence targets and uneven distribution. To address this challenge, we used the DS-InSAR to estimate the spatiotemporal displacement of the active open-pit mine over the past 7 years. The results show that DS-InSAR extracted high-density measurement points in the open-pit mining and waste dump areas, providing detailed displacement that improves the identification of the displacement area. Significant land subsidence was observed in the waste dump (reclaimed area), primarily due to the compaction of loose soil materials. Significant land uplift or horizontal movement was detected in the mining area, likely related to excavation activities. Compared to conventional geodetic methods, InSAR provides large-scale, high-resolution displacement measurements, enabling the comprehensive monitoring of mining-induced displacement.