Monitoring and Law Analysis of Secondary Deformation on the Surface of Multi-Coal Seam Mining in Closed Mines

Abstract

A large number of mines have been closed due to resource depletion, failure to meet safety production requirements, and other reasons. To effectively ensure the safety of the ecological environment above these closed mines along with the safety of engineering construction, it is necessary to monitor the secondary deformation of closed mines. Based on TerraSAR-X, Sentinel-1A data, and InSAR technology, this study obtained high-density secondary surface deformation data on the Jiahe Coal Mine and Pangzhuang Coal Mine in the western Xuzhou area. Combining mining geological data, we analyzed the spatiotemporal variation patterns and mechanisms of secondary deformation in multi-seam mining of closed mines. It was found that when mining multiple seams involves large interlayer spacing, the secondary deformation pattern shows a “W” shape. In this situation, the deformation can be divided into five stages: subsidence, uplift, re-subsidence, re-uplift, and relative stability. This study provides technical support for the evaluation and prevention of secondary deformation hazards in closed mines.

1. Introduction

Due to the high-intensity development of coal resources in China over recent decades, a number of mines have seen a sharp decline in reserves, leading to resource exhaustion; meanwhile, others have failed to meet safe mining conditions and face closure or abandonment [1]. It is estimated that 15,000 mines will be closed in China by 2030 [2]. The safety hazards and secondary disasters present in the surface and underground of closed mines cannot be ignored [3]. Currently, there is a lack of long-term safety and environmental evaluation after mine closures, and a continuous research system for monitoring has not yet been established [4,5,6]. Potential subsidence zones and other secondary disaster-prone areas have not yet been delineated. Therefore, monitoring the secondary deformation of closed mine surfaces, analyzing and studying the related deformation mechanism and laws, and taking reasonable technical measures have become key to ensuring the safety of construction projects above closed mines, with important theoretical and practical value.

Surface secondary subsidence of closed mines mainly refers to the secondary movement and deformation of overburden and surface under the influence of groundwater and weathering after the closure of underground mines [7]. Compared with single-seam mining, multi-seam mining has differences in various aspects, for example the overburden structure, the time and intensity of water effects on broken rock, and the superposition of deformation in the caving zone. These differences lead to different secondary deformation patterns on the surface. Research on the secondary deformation patterns of multi-seam mining in closed mines is crucial in order to understand the mechanisms and patterns of deformation as well as to mitigate and prevent secondary deformation disasters.

Traditional monitoring methods for mine surface deformation, such as leveling and GPS, are point-based and cannot comprehensively reflect deformation characteristics within the entire subsidence range of the mining area [8]. Interferometric Synthetic Aperture Radar (InSAR) technology, with its large monitoring range, high precision, and all-weather capability, has been widely used in the monitoring of large areas and slow deformation [9,10,11]. Many scholars have used time series InSAR technology to obtain secondary surface deformation information of closed mines; for example, Samsonov [12], Cuenca [13], and Vervoort [14] used time series InSAR to obtain the evolution of vertical deformation components caused by groundwater level changes after mine closures in Luxembourg, the Netherlands, and Belgium, respectively. Deng Kazhong [15], Zou Hao [16], and Zheng Meinan [17] used time series InSAR to obtain the surface subsidence deformation of closed mines in eastern Xuzhou, Fengfeng, and Huainan, revealing a three-stage pattern of subsidence, relative stability, and uplift, with surface uplift caused by rising groundwater levels.

Despite these studies, there is a lack of detailed analyses combined with the specific mining conditions of the coal mining area, and there are few analyses of subsidence patterns and mechanisms under multi-seam coal mining conditions.

Therefore, this study used 51 TerraSAR-X ascending-orbit SLC images from 17 January 2014, to 8 January 2018 and 156 Sentinel-1A ascending-orbit SLC images from October 2016 to June 2022 as data sources to monitor surface deformation in the western Xuzhou mining area. Using SBAS-InSAR and DS-InSAR methods, we obtained time series surface deformation in the closed mines of Jiahe and Pangzhuang in western Xuzhou. The reliability of DS-InSAR was verified through comparison with leveling data, and SBAS-InSAR subsidence data were used as supplementary research materials. Finally, based on the case of two-seam mining and combined with data from the mining face, we analyzed the effects of dip angles, interlayer spacing, and the positions of the upper and lower mining faces on secondary surface deformation patterns, thereby clarifying the spatiotemporal distribution of secondary deformation in closed multi-seam mines.

2. Materials

2.1. Study Area

The study area is located in the northwest of Xuzhou City, Jiangsu Province, China in the Huang-Huai-Hai Plain, a typical high water table plain coal mining area. The strata in the study area belong to the North China type, with coal-bearing strata from the Carboniferous and Permian periods. The area is covered by Quaternary alluvial deposits, with no bedrock exposure within the mining area, which includes Cambrian, Ordovician, Carboniferous, Permian, and Quaternary formations (ordered from old to new). The Jiahe mine field covers about 25 km² and was closed in December 2015, while the Pangzhuang mine field covers about 18.3 km² and was closed in December 2012. Historically, there were no old or small mines within the mine fields, and the goaf areas were formed by years of mining within clear mine boundaries, mostly using multi-seam mining with caving or strip mining for roof management. Additionally, the Jiahe mine has moderate aquifer recharge conditions, with less water accumulation in the goaf and clear locations, averaging 115 m³/h of water inflow. The Pangzhuang mine has large surface subsidence areas with water accumulation up to 5–6 m deep, averaging 55 m³/h of water inflow. Both mines left mutually unaffected boundary coal pillars without goaf interconnections. The study area and the basic information of each mine are shown in Figure 1.

2.2. Images

We chose 51 scenes of TerraSAR-X ascending-orbit SLC data from January 2014 to January 2018 and 156 scenes of Sentinel-1A ascending-orbit SLC data from October 2016 to June 2022 as data sources for monitoring surface deformation in the mining area. The TerraSAR-X imagery feature a spatial resolution of 0.9 m by 1.9 m with an incidence angle of 28.8°. In contrast, the Sentinel-1A imagery exhibit a spatial resolution of 2.3 m by 13.9 m accompanied by an incidence angle of 39.2°. In data processing, the DEM data from the SRTM (Shuttle Radar Topography Mission) provided by NASA with a resolution of 30 m were used for geocoding.

One way to mitigate the decoherence is to apply only those interferograms with short time baseline. TerraSAR-X data were processed with temporal and spatial thresholds of 90 days and 500 m, forming 152 interferometric pairs. Due to the discontinuity of the small baseline set, two supplementary interferometric pairs were added, for a total of 154 pairs. The minimum time baseline was 22 days and the maximum was 176 days, while the minimum spatial baseline distance was 23.6 m and the maximum was 720.7 m. For Sentinel-1A, because the imaging plan is quite stable and the orbit control possesses high accuracy, the temporal and spatial thresholds were set as 12 days and 200 m, respectively, forming 150 interferometric pairs. Due to the discontinuity of the small baseline set, five supplementary interferometric pairs were added, for a total of 155 pairs. The minimum time baseline was 12 days and the maximum was 24 days, while the minimum spatial baseline distance was 5.2 m and the maximum was 135.8 m. The specific time–space baselines of the interferometric pairs of both TerraSAR-X and Sentinel-1A are shown in Figure 2.

3. Methods

Surface deformation following closure of underground mines is characterized by its long-term, concealed, and sudden nature; moreover, due to the abundance of farmland and ponds on the surface of underground mining areas and the complex backscattering coefficient of ground objects, using traditional time series InSAR technology for long-term deformation monitoring results in a small number and low density of coherence points. This makes it difficult to obtain a complete picture of secondary deformation on the mine surface or effectively monitor surface secondary deformation.

To increase the number and density of coherent points in non-urban areas, Ferretti [18] and others proposed the SqueeSAR technique. This technique combines Permanent Scatterers [19] (PS) and Distributed Scatterers (DS) for time series InSAR analysis. Based on the principle of SqueeSAR, the Distributed Scatterers InSAR (DS-InSAR) technique focuses on selecting homogeneous points compared to other time series InSAR technologies. By optimizing the phase of distributed targets according to the statistical characteristics of homogeneous points, the impact of noise on distributed targets can be reduced. The combination of PS and DS points is used to calculate the deformation, ultimately achieving the goal of increasing the density of selected points in non-urban areas.

The main steps for selecting DS points are identification of homogeneous pixels and phase optimization [20]. The most commonly applied phase optimization methods include eigenvalue decomposition based on the covariance matrix, least squares phase estimation, maximum likelihood estimation method weights, and coherent weight phase triangulation, among others. This article applies the eigenvalue decomposition method based on the covariance matrix for use in identification of homogeneous points and phase optimization algorithms.

3.1. Identification of Homogeneous Particles

Fast Statistically Homogeneous Pixel Selection (FaSHPs) is a commonly used method for identifying homogeneous points. The core idea of its algorithm is to transform the hypothesis testing problem into a confidence interval estimation problem. It determines whether a pixel is a homogeneous pixel point utilizing the similarity between image pixel data and the reference point time series. This paper uses the FaSHPs method to calculate the number of homogeneous points around each pixel individually, setting a threshold based on multiple homogeneous points and then selecting reference pixels with a number of homogeneous points exceeding this threshold as DS preselection points. According to the central limit theorem, the greater the number of SAR images in the time series, the closer the mean amplitude $A\left(p\right)$ will be to a Gaussian distribution. At this point, the interval estimation of $A\left(p\right)$ can be expressed as

$$\mathrm{P}\left\{\mu \left(p\right)-{\mathrm{z}}_{1-\alpha /2}·\sqrt{{\displaystyle \frac{var\left(A\right(p\left)\right)}{N}}}<\overline{A}\left(p\right)<\mu \left(p\right)+{\mathrm{z}}_{1-\alpha /2}·\sqrt{{\displaystyle \frac{var\left(A\right(p\left)\right)}{N}}}\right\}=1-\alpha .$$

(1)

In the above equation, $P\{·\}$ represents the probability, ${\mathrm{z}}_{1-\alpha /2}$ represents the $1-\alpha /2$ quantile of the standard normal distribution, $\mu \left(p\right)$ represents the expected value of pixel p, var$\left(A\right(p\left)\right)$ represents the temporal amplitude variance of the pixel, and N represents the number of SAR images.

Assuming that the amplitude of the images follows a Rayleigh distribution, the coefficient of variation (CV) can be calculated by the following formula:

$$\mathrm{CV}={\displaystyle \frac{\sqrt{var(·)}}{E(·)}}\approx 0.52/\sqrt{L}.$$

(2)

In the above equation, $E(·)$ represents the expected value, while L is the number of looks in the SAR intensity image. Substituting the above formula transforms Equation (1) into

$$\mathrm{P}\left\{\mu \left(p\right)-{\mathrm{z}}_{1-\alpha /2}·{\displaystyle \frac{0.52·\mu \left(p\right)}{\sqrt{N}}}<\overline{A}\left(p\right)<\mu \left(p\right)+{\mathrm{z}}_{1-\alpha /2}·{\displaystyle \frac{0.52·\mu \left(p\right)}{\sqrt{N}}}\right\}=1-\alpha .$$

(3)

Let $\overline{A}\left(p\right)$ be the true value of the mean amplitude of pixel p in the time dimension; then, the confidence interval for the mean amplitude can be calculated. When computing the mean amplitude of adjacent pixels in the time series, if the result falls within the confidence interval of the reference pixel’s mean amplitude, then the two pixels are considered homogeneous points.

3.2. Phase Optimization Method

After identifying homogeneous points, phase optimization processing based on these homogeneous pixels is crucial for subsequent deformation information extraction using DS points. Phase optimization processing suppresses the impact of decorrelation noise on DS points, improving their coherence so they can be combined with PS points for deformation inversion. The following is a detailed introduction to the eigenvalue decomposition method of the covariance matrix.

Given that ground objects often exhibit spatial correlation, a series of N SAR images in a time series are often considered to follow a complex circular Gaussian distribution with zero mean. The probability density function of the observation vector $d\left(p\right)={[d_1,d_2,...,d_N]}^T$ for any pixel p in the time-series SAR images can be expressed as follows:

$$\mathrm{f}(\mathrm{d}\left(\mathrm{p}\right)\mid \mathrm{C}\left(\mathrm{p}\right))={\displaystyle \frac{1}{{\pi }^{N}det\left(C\left(p\right)\right)}}exp\left(-\mathrm{d}{\left(\mathrm{p}\right)}^H\mathrm{C}{\left(\mathrm{p}\right)}^{-1}\mathrm{d}\left(\mathrm{p}\right)\right).$$

(4)

In this equation, C(p) is the covariance matrix of the observation vector d(p) for any pixel p in the SAR images, det(C(p)) represents the determinant of the matrix, and H denotes the Hermitian (conjugate) transpose. The complex covariance matrix C(p) obtained by maximum likelihood estimation of the sample is defined as follows:

$$\mathrm{C}\left(\mathrm{p}\right)=\mathrm{E}\left[\mathrm{d}\left(\mathrm{m}\right)\mathrm{d}{\left(\mathrm{m}\right)}^H\right]={\displaystyle \frac{1}{\mathrm{M}}}\sum _{m\in \Omega }d\left(m\right)d{\left(m\right)}^H.$$

(5)

In the above equation, $\Omega$ represents the set of homogeneous pixels within a window of a certain size centered on any pixel p, while M denotes the number of homogeneous pixels. The complex covariance matrix C(p) obtained by conjugate multiplication of the observation vectors of the selected homogeneous pixel set and averaging contains the interferometric phase information of all interferometric pairs.

$$\mathrm{C}\left(\mathrm{p}\right)=\left[\begin{array}{cccc}{I}_1& {\tilde{y}}_{12\sqrt{I_1{I}_2}}& \cdots & {\tilde{y}}_{1N\sqrt{I_1{I}_N}}\\ {\tilde{y}}_{21\sqrt{I_1{I}_2}}& I_2& \cdots & y_{2N\sqrt{I_2{I}_N}}\\ ⋮& ⋮& \ddots & ⋮\\ {\tilde{y}}_{N1\sqrt{I_1{I}_N}}& y_{N2,\sqrt{I_2{I}_N}}& \cdots & I_N\end{array}\right]$$

(6)

In the above equation, $I_i$ represents the intensity of any pixel p in the N images. Consequently, each pixel in the image generates an N × N covariance matrix. By performing eigenvalue decomposition on the covariance matrix of each pixel, the signal information of different scattering characteristics can be separated. Different eigenvalues correspond to scattering signals of varying intensities, as shown below

$$\mathrm{C}\left(\mathrm{p}\right)=\sum _{i=1}^N{\lambda }_i{\mu }_i{\mu }_i^H$$

(7)

$$\widehat{C}\left(p\right)={\widehat{C}}_{\mathrm{signal}.}+{\widehat{C}}_{\mathrm{noise}.}={\lambda }_1{\mu }_1{\mu }_1^H+\sum _{i=2}^N{\lambda }_i{\mu }_i{\mu }_i^H$$

(8)

In these equations, ${\lambda }_i$ represents non-negative eigenvalues and $({\mu }_1,{\mu }_2,...,{\mu }_N)$ represents the corresponding eigenvectors. Let ${\lambda }_1>{\lambda }_2>...>{\lambda }_N$; different ${\lambda }_i$ correspond to different scattering phases, with larger ${\lambda }_i$ values indicating more dominant scattering phases. Typically, n (n < N) of the largest eigenvalues and eigenvectors are selected as the dominant scatterers, while the remaining N–n eigenvalues and eigenvectors are treated as decorrelation noise. However, in practical applications, the number of dominant scattering mechanisms n is usually unknown. Therefore, after performing eigenvalue decomposition on the covariance matrix, the largest eigenvalue ${\lambda }_i$ and its corresponding eigenvector ${\mu }_i$ are generally taken as the dominant scatterer, while the remaining phases are filtered out as noise phases, achieving the goal of phase optimization.

3.3. DS-InSAR Technical Process

The main processing steps of the DS-InSAR technique (Figure 3) used in this article are as follows:

(1)

Selection of homogeneous pixels based on confidence interval hypothesis testing.

(2)

Optimization of candidate point phases based on eigenvalue decomposition of the coherence matrix.

(3)

Setting a time coherence threshold of 0.6 and identifying DS candidate points with coherence greater than 0.6 as the final DS points.

(4)

Setting an amplitude dispersion index threshold of 0.37 and selecting PS points.

(5)

Performing deformation calculation by combining the DS points and PS points.

4. Results

For the western region of Xuzhou, 51 scenes of TerraSAR-X images were selected. Due to the good image quality and coherence of the selected points, the Permanent Scatterers (PS) technique provided a sufficient point density to meet monitoring requirements and subsequent pattern analysis. Based on the SBAS-InSAR method, deformation results from January 2014 to January 2018 in the western mining areas of Xuzhou were obtained to supplement the data for subsequent pattern analysis. This provides a more complete dataset for the deformation cycle after mine closure, which is advantageous for analyzing deformation patterns post-closure. As the western mining area of Xuzhou mainly consists of farmland and ponds, and lacks strong scatterers, both the SBAS-InSAR and DS-InSAR methods were employed to process the Sentinel-1A data, resulting in secondary deformation results of the closed mine surface in the western region of Xuzhou obtained using both methods. The reliability of the monitoring results was verified through comparative analysis with the leveling results.

4.1. Monitoring Results and Analysis Based on TerraSAR-X and SBAS-InSAR

Using SBAS-InSAR technology, the secondary deformation rates of closed mines in the western Xuzhou mining area from 17 January 2014 to 8 January 2018, were obtained, as shown in Figure 4.

Three significant subsidence areas were observed in the study area, with two located in the Jiahe mine and one in the Pangzhuang mine. The average surface deformation rate in the study area ranged from −32 to 12 mm/a, with a local maximum subsidence rate of −114 mm/a. Two significant subsidence areas with more severe subsidence than the other two areas were observed in the northern part of the Jiahe mine. To further analyze the subsidence characteristics of each subsidence center, profile lines were selected in the subsidence center areas of Jiahe Mine and Pangzhuang Mine, denoted as AA’ and BB’, respectively. The positions of the profile lines and profile diagrams are shown in Figure 4. The maximum subsidence rates detected were 90 mm/a for Jiahe Mine and 46 mm/a for Pangzhuang Mine. Considering the land usage, the area above Pangzhuang Mine mainly consists of rice fields and ponds, with few artificial structures, resulting in sparse point selection. Similarly, the area above Jiahe Mine is predominantly farmland and bare land, with few artificial structures, again resulting in sparse point selection. Considering the specific coal mining faces, subsidence in both Jiahe Mine and Pangzhuang Mine is mainly influenced by faces 2, 7, and 9.

Figure 5 presents partial long-term cumulative surface subsidence maps of the western mining area of Xuzhou from 2014 to 2018. From the figure, it is evident that the overall trend in the study area consists of continuous subsidence. Regarding subsidence values, the maximum subsidence value for Jiahe Mine is 450 mm, while for Pangzhuang Mine it is 333 mm. Looking at the development process of surface subsidence in the two mines, both the cumulative subsidence values and the subsidence area continue to expand with the continuous accumulation of monitoring time. However, there is no significant change in the spatial location of the subsidence center.

4.2. Monitoring Results and Analysis Based on Sentinel-1A Data and DS-InSAR

Using Sentinel-1A data from 4 October 2016 to 11 June 2022 along with DS-InSAR technology, the cumulative surface deformation values after mine closure in the line-of-sight direction were obtained for the western mining area of Xuzhou. DS-InSAR utilizes a combination of DS and PS for time series InSAR analysis, effectively increasing the number and density of high-coherence points. DS-InSAR obtained a total of 443,206 high-coherence points with an average point density of 1441.9 points/km².

To further analyze the temporal variation of surface deformation in each mining area and obtain the time series of cumulative secondary deformation variables for each mine, a time interval of 6 months was set. The results are shown in Figure 6.

From the long-term cumulative surface subsidence in the western mining area of Xuzhou from 2016 to 2022, it can be observed that the development of surface subsidence accumulates over time. Both Jiahe Mine and Pangzhuang Mine exhibit a deformation trend of subsidence followed by uplift. Additionally, there is no significant change in the spatial location of the subsidence centers for the two mines.

4.3. Reliability Analysis of Monitoring Results

A comparative analysis of the monitoring results obtained by SBAS-InSAR and DS-InSAR methods demonstrates that the surface secondary deformation results obtained by the DS-InSAR monitoring method are superior to those of the SBAS-InSAR method in terms of spatial integrity, continuity, and other aspects. Statistical analysis of the standard deviation of vertical deformation rates obtained by both methods as well as quantitative evaluation of the accuracy of monitoring results using external leveling data further corroborate the superiority of the DS-InSAR method.

4.3.1. Comparative Analysis of Sentinel-1A DS-InSAR Results and Leveling Data

In order to quantitatively evaluate the accuracy of the DS-InSAR monitoring results, leveling points located in the Quanshan Industrial Park in Xuzhou, including two leveling benchmarks, were selected for validation. As the coherent points selected by InSAR technology may not completely overlap with the selected leveling points and there may be some errors in geographic encoding results when comparing DS-InSAR monitoring results with leveling data, the average deformation of a high-coherence point set within a 50 m range from the leveling point and the leveling data from twenty valid leveling points were compared. Of this total, seventeen valid points were selected for the StaMSBAS monitoring results and twenty valid points were selected for the DS-InSAR monitoring results, both of which were selected to compare the 30 October 2018 monitoring values with the level data.

From Figure 7b, it can be observed that the deformation trends monitored by both methods generally align with the deformation trends measured by leveling, and the agreement in deformation magnitude is good. To quantitatively assess the monitoring accuracy of both methods, three metrics, namely, Maximum Deviation (MaxD), Minimum Deviation (MinD), and Standard Deviation (SD), were selected to evaluate the accuracy of the InSAR monitoring results. The accuracy comparison results are presented in

Table 1. Precision evaluation of monitoring results of SBAS-InSAR and DS-InSAR.

Method	MaxD (mm)	MinD (mm)	SD (mm)
SBAS-InSAR	14.0	0.8	4.9
DS-InSAR	10.2	0.5	3.5

.

From Table 1, it can be seen that both the SBAS-InSAR and DS-InSAR monitoring results exhibit high overall accuracy, reaching sub-centimeter level accuracy; however, the accuracy of the SBAS-InSAR monitoring results is lower than those obtained using the DS-InSAR method. The higher accuracy of the DS-InSAR monitoring results can be attributed to several factors. First, when performing calculation using DS-InSAR technology by combining DS and PS points, selection and phase optimization of DS points not only increases the number and density of high-coherence points, it also improves the density of the network when constructing the network using high-coherence points, resulting in an increased number of effective edges. This makes the unwrapped phase obtained using the three-dimensional phase-unwrapping algorithm more stable. Additionally, by optimizing the phase of the DS points, the influence of the atmospheric delay phase and other noise phases is suppressed to some extent, improving the accuracy of the DS-InSAR monitoring results.

Comparative analysis between the monitoring results obtained using both methods and the leveling data indicates that as compared to SBAS-InSAR technology, DS-InSAR technology not only ensures the spatial integrity and continuity of obtaining surface deformation after mine closure, it also improves the accuracy of the obtained surface deformation information. This demonstrates that DS-InSAR technology can meet the requirements for monitoring surface deformation after mine closure.

4.3.2. Comparison of Overlapping Results between TerraSAR-X and Sentinel-1A at the Same Point

Considering the lack of leveling data for the study area during that time period, a comparison analysis was conducted to verify the reliability of the TerraSAR-X data monitoring results by examining the subsidence trends and magnitude of the TerraSAR-X data and Sentinel-1A data during overlapping time periods. These common points were selected from strong scatterers such as buildings and roads. Because the monitored subsidence values may be significantly affected by noise, two common points (labeled as E and F) were selected from each subsidence center for comparative analysis in order to ensure the accuracy of the validation. The specific locations of these selected points are illustrated in Figure 7a.

From Figure 8, it can be observed that the difference in subsidence values between common points in the overlapping time periods of TerraSAR-X data and Sentinel-1A data is at the sub-centimeter level. Additionally, the secondary deformation detected by both types of imagery data remains consistent in magnitude and subsidence trend. Therefore, the monitoring results based on the SBAS-InSAR method using TerraSAR-X data are reliable and can be used as a supplementary source for obtaining the secondary deformation after mine closure using the DS-InSAR method based on Sentinel-1A data.

5. Discussion

Unlike the “bowl-shaped” surface secondary deformation observed in single-coal seam closed mines [21], which is characterized by stages of subsidence, relative stability, uplift, and stability, multiple-seam coal mining operations feature multiple vertical collapse zones. These collapse zones are subject to varying durations of groundwater influence, and may exhibit phenomena such as subsidence above collapse zones of upper coal seams and uplift below collapse zones of lower coal seams. This study primarily focuses on the analysis of the impact of different dip angles, interlayer distances, and the spatial relationship between upper and lower working faces on the surface secondary deformation patterns under conditions of dual-coal seam mining. It aims to elucidate the spatiotemporal distribution pattern of surface secondary deformation in closed mines with multiple coal seam mining operations, which is characterized by a “W-shaped” pattern (comprising stages of subsidence, uplift, subsequent subsidence, subsequent uplift, and stability).

5.1. Time Series Variation of Surface Secondary Deformation in Multi-Seam Mining in Closed Mine

5.1.1. Deformation Rule of Jiahe Coal Mine

To analyze the surface deformation patterns after the closure of Jiahe Mine, four points (P1–P4) with the locations shown in Figure 1b were selected based on their surface deformation characteristics and hydrogeological mining data. The corresponding mining information for the selected points is provided in

Table 2. Mining information of selected multi-seam working face in Jiahe Coal Mine.

Mine Name	Point	Working Panel	Stop Time	Mining Thickness/m	Inclination Angle/°	Depth/m	Coal Seam Spacing/m
Jiahe Coal Mine	P1	7425	2001.6	2.5	7	520	30
		9423	1995.12	2.1	10	550
	P2	2616	2001.6	2.3	12	393	54
		7622	2002.9	2.0	11	447
	P3	2049	1971.12	1.85	5	300	125
		7415	1988.9	2.8	7	425
	P4	2401	1978.10	1.7	5	106	123
		7405	1979.12	2.9	8	229

.

In the time series deformation curve for Point P1 (Figure 9), with the seventh coal seam 7425 working face above and the ninth coal seam 9423 working face below, the analysis of its temporal deformation reveals a minor initial subsidence, indicating a period of relative stability after mine closure. This can be attributed to the prolonged cessation of operations in both working faces, resulting in minimal secondary deformation in the absence of groundwater influence. As the groundwater level rises, the deeper 9423 working face is affected, leading to temporary subsidence as the fractured rock mass in the goaf continues to compact under groundwater softening. Subsequently, as the groundwater level reaches a certain height, the increased pore water pressure within the fractured rock mass of the 9423 working face causes uplift, followed by the infiltration of groundwater into the overlying 7425 working face. Through the softening action of groundwater, the fractured rock mass within the 7425 working face goaf undergoes recompaction, resulting in a phase characterized by concurrent subsidence of the lower coal seam and uplift of the upper coal seam. However, the overall compression of the fractured rock mass in the upper working face exceeds the uplift induced by increased pore water pressure in the lower coal seam, leading to a subsequent period of subsidence. As the groundwater level rises further, reaching a balance between the uplift induced by increased pore water pressure in the 9423 working face and the subsidence in the 7425 working face, a phase of uplift ensues. Initially, this uplift is driven by the greater uplift in the 9423 working face compared to the subsidence in the 7425 working face, followed by a period of accelerated uplift as pore water pressure induces uplift in both working faces, before the uplift rate gradually diminishes. Thus, this uplift phase can be subdivided into two stages: an initial slow uplift followed by a rapid uplift.

In the time series deformation curve for Point P2 (Figure 9), with the second coal seam 2616 working face above and the seventh coal seam 7622 working face below, the geological and mining conditions at Point P2 are similar to those at Point P1, with the exception of differences in mining depth, allowing for feasible comparative analysis. Analysis of the temporal deformation at Point P2 reveals minor subsidence transitioning to a period of relative stability after mine closure. This can be attributed to the extended cessation of operations in both working faces, resulting in minimal compression of the fractured rock mass under the influence of groundwater softening. As the groundwater level rises to a certain height, leading to increased pore water pressure and entering a phase of relative stability, the surface undergoes uplift. However, due to the smaller distance between the two working faces and the significant inflow of water at Jiahe Mine, the groundwater rises rapidly. As groundwater infiltrates the goaf of the upper working face, the fractured rock mass undergoes recompaction, causing subsidence to occur at the surface. This results in a sharp change in the temporal curve at the inflection point. As the fractured rock mass in the goaf of the 7622 working face gradually compacts and the groundwater level rises, the subsidence gradually slows down and gradually transitions to uplift under the influence of increased pore water pressure. This mechanism of uplift is consistent with that observed at Point P1. Unlike Point P1, however, Point P2 exhibits a period of relative stability towards the end of the observation period.

In the time series deformation curve for Point P3 (Figure 9), with the second coal seam 2049 working face above and the seventh coal seam 7415 working face below, the geological and mining conditions at Point P3 are similar to those at Point P2, with the exception of the spacing between the two working faces, allowing for comparative analysis to summarize the secondary deformation patterns. Analysis of the temporal deformation reveals that during the period from mine closure to December 2017, the subsidence curve at Point P3 is consistent with the subsidence trend and deformation magnitude observed at Point P2. Moreover, the lower 7415 working face at Point P3 and the lower 7622 working face at Point P2 have roughly the same mining depth, indicating that the secondary deformation patterns of working faces with similar mining depths are consistent. After the rise in groundwater level, the increased pore water pressure within the goaf of the 7415 working face leads to surface uplift. From April 2018 to October 2018 a period of relative stability occurs, attributed to the similarity between the uplift induced by increased pore water pressure in the lower working face and the subsidence in the fractured rock mass of the upper working face. As the compaction of the fractured rock mass in the upper working face increases, surpassing the uplift induced by the fractured rock mass in the lower working face, a phase of accelerated subsidence occurs. With the rise in groundwater level in the upper working face, the fractured rock mass is uplifted by groundwater action, causing the surface to uplift again, consistent with the uplift mechanism observed at Point P1.

In the time series deformation curve for Point P4 (Figure 9), with the second coal seam 2401 working face above and the seventh coal seam 7405 working face below, although the overall mining depth is shallow, the spacing between coal seams is larger. The geological and mining conditions at Point P4 are similar to those at Point P3, with the exception of differences in mining depth, allowing for comparative analysis to summarize the secondary deformation patterns. According to the temporal deformation curves, the early subsidence trend at Point P4 is consistent with that at Point P3, and both Point P4 and Point P3 exhibit a period of stability after mine closure, with Point P4 showing a longer duration of stability. This is attributed to the earlier cessation of operations at Point P4 and the shallower mining depth compared to Point P3, resulting in smaller residual subsidence after mine closure and minimal influence from groundwater. From October 2017 to April 2018, accelerated subsidence occurs due to groundwater softening, leading to further compaction of the fractured rock mass in the goaf. However, the accelerated subsidence phase at Point P4 occurs approximately 14 months later than at Point P3, indicating a delayed response to groundwater compared to Point P3. Subsequent deformation mechanisms at Point P4 are consistent with those at Point P3, where the rise in groundwater level leads to surface uplift as the increased pore water pressure causes a reduction in effective stress between fractured rock masses.

5.1.2. Deformation Rule of Pangzhuang Coal Mine

To analyze the surface deformation patterns after the closure of Pangzhuang Mine, this section focuses on a detailed analysis of points located on the second coal seam and ninth coal seam at Pangzhuang Mine. Specifically, one point is selected near the subsidence center and another within the central part of the mining area. The selected points (P5, P6), with the locations shown in Figure 1c, correspond to the mining information of the respective working faces, as outlined in

Table 3. Mining information of selected multi-seam working face in Pangzhuang Coal Mine.

Mine Name	Point	Working Panel	Stop Time	Mining Thickness/m	Inclination Angle/°	Depth/m	Coal Seam Spacing/m
	P5	212	1993.2	2.2	5	460	90
Pangzhuang		9542	2003.7	2.4 + 0.75 rock layer	15	550
Coal Mine	P6	238	1986.1	2.4 + 0.6 rock layer	5	405	145
		928	1998.2	3.0	8	540

.

The time series deformation curve at point P5 (Figure 10), with the 212 working face above and the 9542 working face below, indicates that the surface was in a state of subsidence from the closure of the mine until December 2017. However, in December 2016, despite the low winter rainfall, a brief uplift occurred followed by another subsidence. During this period, there was no significant rise in the groundwater level, excluding short-term elevation due to groundwater. Analysis suggests that the prolonged cessation of operations at the 9542 working face resulted in the compaction of the fractured rock mass in the goaf. Upon groundwater ingress, the friction between rock masses increased, leading to overall uplift. Subsequently, the softening effect of groundwater reduced the friction coefficient between rock masses, causing gradual subsidence and recompaction. Consequently, from February to December 2017, the surface subsided again. As the groundwater level rose to a certain height, the increase in pore water pressure within the goaf of the 9542 working face caused the uplift of the lower goaf to exceed the downward compaction of the upper goaf, resulting in surface uplift. Due to the greater depth from the upper working face, this uplift persisted until December 2018. After the groundwater level rose to the 212 working face, the renewed compaction of fractured rock masses due to the softening effect of groundwater caused more subsidence than the uplift at the 9542 working face, leading to another surface subsidence. As the rise in groundwater level and the compaction of fractured rock masses in the upper goaf gradually ceased, the trend of surface subsidence slowed down, and oscillatory uplift occurred again in December 2019.

In the time series deformation curve at point P6 (Figure 10), with the 238 working face above and the 928 working face below, the overall deformation pattern is consistent with that of point P5. From the closure of the mine until April 2018, the surface was in a state of subsidence, with a longer duration compared to point P5. Analysis reveals that although the cessation period, mining depth, and thickness at the lower working face were roughly the same as at point P5, the larger spacing between upper and lower coal seams at P6 resulted in a longer combined period of compaction-induced subsidence in the upper coal seam and uplift in the lower coal seam due to groundwater influence, causing a delayed uplift compared to P5. When the groundwater level rose to a certain height, the increase in pore water pressure within the goaf of the 928 working face caused the uplift of the lower goaf to exceed the downward compaction of the upper goaf, resulting in surface uplift. This uplift continued until December 2018. After the groundwater level rose to the 238 working face, the renewed compaction of fractured rock masses due to the softening effect of groundwater caused more subsidence than the uplift at the 928 working face, leading to another surface subsidence. As the rise in groundwater level and the compaction of fractured rock masses in the upper goaf gradually ceased, the trend of surface subsidence slowed down, and oscillatory uplift occurred again in December 2019.

5.2. Secondary Deformation Regularity of W Type

Analysis of the time series deformation at points P1 to P6 of the multi-seam selections from Jiahe and Pangzhuang mines reveals that the secondary surface deformation pattern resulting from the closure of multi-seam coal mines differs from that in single-seam mines. The mechanism is more complex, involving a combination of subsidence and uplift caused by the upward movement of lower coal seams and the downward movement of upper coal seams. The pattern can be summarized as follows: when the goaf of the lower coal seam does not intrude into the upper coal seam, and when there is a certain interval between them, the secondary deformation pattern of multi-coal seam mining closure follows a "W" shape, which can be divided into five stages:

(1)

Subsidence: This stage can be further divided into two parts based on driving forces. Initially, before groundwater affects the lower working face, subsidence is mainly due to the compaction of fractured rock masses within the upper and lower goafs. After the rise in groundwater level, the compaction of fractured rock masses in the lower goaf due to the softening effect of groundwater leads to increased subsidence. Certain working faces may experience accelerated subsidence during this stage due to groundwater influence causing instability in coal pillars or overlying rock structures.

(2)

Uplift: When the groundwater level rises to a certain height but has not yet affected the upper goaf, the increased pore water pressure within the lower goaf leads to uplift. The magnitude of uplift in this stage depends on the interval between coal seams.

(3)

Subsequent Subsidence: As the groundwater level rises to the upper goaf, the subsidence caused by the softening effect of groundwater in the upper goaf gradually exceeds the uplift in the lower goaf. Surface uplift gradually slows down, and eventually turns into subsidence.

(4)

Subsequent Uplift: After the groundwater level rises to a certain height, the compaction of the upper goaf enters a declining phase and a short-term equilibrium is reached between the uplift caused by the increased pore water pressure in the lower goaf and the subsidence in the upper goaf. Subsequently, uplift initially occurs due to the greater uplift in the lower goaf compared to subsidence in the upper goaf, followed by both goafs contributing to the uplift due to increased pore water pressure, resulting in a relatively rapid uplift phase. The uplift rate then slows again, dividing this stage into early slow uplift, mid-term accelerated uplift, and late slow uplift.

(5)

Relative Stability: Referring to the secondary deformation pattern of single coal seam mining, when the groundwater returns to a regionally stable level, the surface uplift reaches its maximum value. Under the combined effects of mining-induced fractured rock masses and groundwater, a relative equilibrium state is reached and the surface enters a phase of relative stability. However, this stage was not observed during the observation period in the study area.

6. Conclusions

This paper is based on the use of DS-InSAR technology to acquire high-density secondary surface deformation data of closed coal mines Jiahe Coal Mine and Pangzhuang Coal Mine in western Xuzhou. Combined with hydrogeological and mining data of the mining area, the temporal and spatial variation patterns and mechanisms of secondary deformation caused by multi-coal seam mining in the study area were analyzed. It was found that under the conditions of multi-coal seam mining, the secondary surface deformation patterns of closed coal mines show partial consistency with those under single coal seam mining conditions; however, the mechanism is more complex, involving the superposition of subsidence and uplift due to multi-coal seam mining. When mining two coal seams with large interlayer distances and horizontal or multiple inclined coal seams, the overall deformation trend presents a “W” shape, which can be divided into five stages: subsidence, uplift, subsequent subsidence, subsequent uplift, and relative stability.

In combination with precise hydrogeological data in the mining area, subsequent research should further investigate the impact of seasonal changes in groundwater on the secondary deformation of closed multi-seam coal mines in order to accurately define the precise time boundaries of each stage of subsidence, uplift, subsequent subsidence, subsequent uplift, and stability.