Integration of DInSAR-PS-Stacking and SBAS-PS-InSAR Methods to Monitor Mining-Related Surface Subsidence
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.3. Methods
- (1)
- Data preprocessing. Select the required 52 Sentinel-1A image data, DEM data, and orbit data. Data clipping and baseline estimations are performed according to the scope of the study area.
- (2)
- Ground control points (GCPs) screening. The obtained 52 Sentinel-1A SAR image data are processed using the PS-InSAR three-threshold method (the coherence coefficient threshold, amplitude dispersion index threshold, and deformation velocity interval) to obtain stable and qualified PS points as ground control points.
- (3)
- DInSAR-PS-Stacking processing. The GCPs selected using the PS-InSAR three-threshold method are used for the refinement and re-flattening step of DInSAR-PS-Stacking processing, and then the cumulative time-series deformation phase information is obtained by weighted stacking; finally, the time-series cumulative deformation results are obtained.
- (4)
- SBAS-PS-InSAR processing. The GCPs selected using the PS-InSAR three-threshold method are used for the orbit refinement and re-leveling steps of SBAS-InSAR processing, and then the time-series deformation information is obtained by deformation inversion.
- (5)
- Comparative verification and analysis. The deformation information monitored by the DInSAR-PS-Stacking and SBAS-PS-InSAR methods are compared and analyzed according to comparative validation and the deformation results.
- (6)
- DInSAR-PS-Stacking and SBAS-PS-InSAR fusion. The time-series deformation information of these two methods is fused using the two-threshold method (OTSU method sets the average coherence threshold and deformation threshold), and the time-series deformation information after fusion is obtained for complementary advantages.
- (7)
- Deformation analysis after fusion. The settlement analysis of the fused deformation results obtained by the DInSAR-PS-Stacking and SBAS-PS-InSAR methods provides a scientific reference for coal mining subsidence control and disaster warning.The technical flow of the data processing is shown in Figure 2.
2.3.1. Ground Control Point Screening
- (1)
- Coherence coefficient method
- (2)
- Amplitude dispersion index method
2.3.2. DInSAR-PS-Stacking Processing
2.3.3. SBAS-PS-InSAR Processing
2.3.4. DInSAR-PS-Stacking and SBAS-PS-InSAR Fusion
3. Results and Analysis
3.1. Analysis of Refinement and Re-Flattening Results
3.2. Monitoring and Analysis of DInSAR-PS-Stacking and SBAS-PS-InSAR
- (1)
- The DInSAR-PS-Stacking and SBAS-PS-InSAR methods can accurately locate and detect the change trend of mining subsidence, which is in good agreement with the mining process of the coal mining face. The surface subsidence was found to gradually increase with the mining of the working face. The location, range, distribution, and space–time subsidence laws of the surface subsidence of the coal mine monitored by the two InSAR methods had good consistency.
- (2)
- The point subsidence results obtained by these two InSAR techniques are well correlated with GPS monitoring results. The settlement trend of each point is basically the same, and the monitoring results are effective and reliable. The time series settlement errors monitored by these two InSAR methods show that the settlement edge is small and the large settlement area is large.
- (3)
- Underground coal mining leads to surface subsidence, which makes radar images partially incoherent. Especially in the area with large settlement, the settlement gradient was large and the decoherence was substantial. For the large subsidence area of the goaf, DInSAR-PS-Stacking was found to be more effective than SBAS-PS-InSAR according to the monitoring results. The SBAS-PS-InSAR method is more effective for monitoring slow and small deformations.
3.3. Deformation Fusion Monitoring and Analysis of DInSAR-PS-Stacking and SBAS-PS-InSAR
4. Discussion
5. Conclusions
- (1)
- Both the DInSAR-PS-Stacking and SBAS-PS-InSAR methods can monitor the surface deformation of the mining area in real time and effectively, and can accurately monitor the location, range, and spatial and temporal distribution of coal mine subsidence.
- (2)
- There is a subsidence basin gradually expanding from north to south in the coal mining face, and the edge is steep, which can easily produce surface cracks. The change trend of the subsidence basin is obvious and consistent with the mining situation of the working face. Large-scale mining of underground coal mines is the main factor causing surface subsidence.
- (3)
- The DInSAR-PS-Stacking and SBAS-PS-InSAR methods are compared to better monitor the surface deformation of the mining area, effectively obtain the subsidence distribution of the mining area, and analyze the deformation of the coal mine goaf. The two InSAR methods have small deformation errors at the edge of the subsidence basin and large deformation errors in the large subsidence area. In contrast, the SBAS-PS-InSAR method performs better in monitoring slow and small deformation in the edge area of the subsidence basin. The DInSAR-PS-Stacking method is more effective in monitoring large deformation in large subsidence areas.
- (4)
- The surface deformation results monitored by the DInSAR-PS-Stacking and SBAS-PS-InSAR methods are fused, and the fused deformation monitoring results are better. The fused method improves the inaccuracy of the traditional single InSAR method used to monitor the mining area, reduces the error, and improves the accuracy and integrity of the mining area deformation monitoring. Furthermore, the fused method provides more comprehensive deformation information for the comprehensive management of mining subsidence and realizes the effective monitoring of surface deformation in mining areas.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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No | Image Data | Orbit | No | Image Data | Orbit | No | Image Data | Orbit |
---|---|---|---|---|---|---|---|---|
1 | 6 January 2018 | 020033 | 19 | 22 August 2018 | 023358 | 37 | 26 March 2019 | 026508 |
2 | 30 January 2018 | 020383 | 20 | 3 September 2018 | 023533 | 38 | 7 April 2019 | 026683 |
3 | 11 February 2018 | 020558 | 21 | 15 September 2018 | 023708 | 39 | 19 April 2019 | 026858 |
4 | 23 February 2018 | 020733 | 22 | 27 September 2018 | 023883 | 40 | 1 May 2019 | 027033 |
5 | 7 March 2018 | 020908 | 23 | 9 October 2018 | 024058 | 41 | 13 May 2019 | 027208 |
6 | 19 March 2018 | 021083 | 24 | 21 October 2018 | 024233 | 42 | 6 June 2019 | 027558 |
7 | 31 March 2018 | 021258 | 25 | 2 November 2018 | 024408 | 43 | 18 June 2019 | 027733 |
8 | 12 April 2018 | 021433 | 26 | 14 November 2018 | 024583 | 44 | 30 June 2019 | 027908 |
9 | 24 April 2018 | 021608 | 27 | 26 November 2018 | 024758 | 45 | 12 July 2019 | 028083 |
10 | 6 May 2018 | 021783 | 28 | 8 December 2018 | 024933 | 46 | 24 July 2019 | 028258 |
11 | 18 May 2018 | 021958 | 29 | 20 December 2018 | 025108 | 47 | 5 August 2019 | 028433 |
12 | 30 May 2018 | 022133 | 30 | 1 January 2019 | 025283 | 48 | 17 August 2019 | 028608 |
13 | 11 June 2018 | 022308 | 31 | 13 January 2019 | 025458 | 49 | 29 August 2019 | 028783 |
14 | 23 June 2018 | 022483 | 32 | 25 January 2019 | 025633 | 50 | 10 September 2019 | 028958 |
15 | 5 July 2018 | 022658 | 33 | 6 February 2019 | 025808 | 51 | 22 September 2019 | 029133 |
16 | 17 July 2018 | 022833 | 34 | 18 February 2019 | 025983 | 52 | 4 October 2019 | 029308 |
17 | 29 July 2018 | 023008 | 35 | 2 March 2019 | 026158 | |||
18 | 10 August 2018 | 023183 | 36 | 14 March 2019 | 026333 |
Parameter | Value |
---|---|
Pass direction | Ascending |
Beam mode | IW |
Polarization | VV |
Wave band | C |
Wavelength/cm | 5.6 |
Number of images | 52 |
Monitored period | 6 January 2018–4 October 2019 |
Monitoring Points | Correlation Coefficient (Pearson) | Mean Absolute Error (MAE)/mm | Root Mean Square Error (RMSE)/mm |
---|---|---|---|
1 | 0.9735 | 12.2 | 13.9 |
6 | 0.9637 | 47.2 | 52.2 |
12 | 0.9568 | 41.9 | 51.0 |
22 | 0.9858 | 32.1 | 42.3 |
Monitoring Points | Correlation Coefficient (Pearson) | Mean Absolute Error (MAE)/mm | Root Mean Square Error (RMSE)/mm |
---|---|---|---|
1 | 0.9698 | 3.2 | 3.8 |
6 | 0.8595 | 74.5 | 82.6 |
12 | 0.9120 | 59.9 | 72.4 |
22 | 0.9135 | 62.0 | 83.9 |
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Chen, Y.; Dong, X.; Qi, Y.; Huang, P.; Sun, W.; Xu, W.; Tan, W.; Li, X.; Liu, X. Integration of DInSAR-PS-Stacking and SBAS-PS-InSAR Methods to Monitor Mining-Related Surface Subsidence. Remote Sens. 2023, 15, 2691. https://doi.org/10.3390/rs15102691
Chen Y, Dong X, Qi Y, Huang P, Sun W, Xu W, Tan W, Li X, Liu X. Integration of DInSAR-PS-Stacking and SBAS-PS-InSAR Methods to Monitor Mining-Related Surface Subsidence. Remote Sensing. 2023; 15(10):2691. https://doi.org/10.3390/rs15102691
Chicago/Turabian StyleChen, Yuejuan, Xu Dong, Yaolong Qi, Pingping Huang, Wenqing Sun, Wei Xu, Weixian Tan, Xiujuan Li, and Xiaolong Liu. 2023. "Integration of DInSAR-PS-Stacking and SBAS-PS-InSAR Methods to Monitor Mining-Related Surface Subsidence" Remote Sensing 15, no. 10: 2691. https://doi.org/10.3390/rs15102691
APA StyleChen, Y., Dong, X., Qi, Y., Huang, P., Sun, W., Xu, W., Tan, W., Li, X., & Liu, X. (2023). Integration of DInSAR-PS-Stacking and SBAS-PS-InSAR Methods to Monitor Mining-Related Surface Subsidence. Remote Sensing, 15(10), 2691. https://doi.org/10.3390/rs15102691