Abstract
Due to the shortcomings of traditional interferometric synthetic aperture radar (InSAR) technologies in mining subsidence monitoring, such as a low density of monitoring points and difficulty obtaining fine surface deformation information, this paper proposes a mining area surface deformation monitoring method based on distributed scatterers InSAR while improving the phase information optimization strategy. This method obtains homogeneous points using the hypothesis test of confidence interval algorithm and constructs an adaptive phase optimization method based on the Goldstein principal phase filtering and Fisher information matrix weighting. It effectively preserves the information of deformation fringes, particularly in regions with dense interferometric fringes, and obtains detailed deformation information from the study area through time processing. In the experiments, 63 Sentinel-1 images were used to extract surface subsidence information for the Peibei mining area from September 24, 2018, to November 12, 2020. Compared with the Permanent Scatterers InSAR (PS-InSAR) results, the point density increased by a factor of 4.2. The correlation coefficient between the homonymous points obtained with the two methods and the deformation rate is 0.94, indicating that they have a good consistency. The monitoring results show that the six mining areas in Peibei have different degrees of subsidence during the monitoring period with a clear nonlinear trend, and the maximum cumulative subsidence over time exceeds 350 mm. The analysis shows that the improved DS-InSAR monitoring results are in line with the general law of mining subsidence and have practical application value.
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Abbreviations
- InSAR:
-
Interferometric synthetic aperture radar
- DS-InSAR:
-
Distributed scatterers InSAR
- PS-InSAR:
-
Permanent scatterers InSAR
- HTCI:
-
Hypothesis test of confidence interval
- FIM:
-
Fisher information matrix
- GNSS:
-
Global Navigation Satellite System
- SBAS-InSAR:
-
Small baseline subset InSAR
- TCP-InSAR:
-
Temporarily coherent point InSAR
- FaSHPS:
-
Fast statistically homogeneous pixel selection
- SHP:
-
Statistically homogeneous point
- PSD:
-
Phase standard deviation
- SPD:
-
Sum of phase differences
- RPN:
-
Residue point number
- g :
-
Gamma distribution
- α :
-
Significance level
- \(\hat{\mu }_{{{\text{ref}}}}\) :
-
The estimated value of the reference pixel
- \(\hat{\mu }\) :
-
The mean intensity of the other pixels in the window in the time domain
- H :
-
Filtered interferogram
- u v :
-
Spatial frequency
- \(S\left\{ \cdot \right\}\) :
-
Smoothing factor
- β :
-
Filtering index
- \(\overline{\gamma }\) :
-
The average coherence of the effective filtering window
- W :
-
The value of the phase optimization weight
- L :
-
Multi-look number
- \(\tilde{\gamma }\) :
-
Coherence
- \(\circ\) :
-
The Hadamard product between two matrices
- T :
-
Complex coherence matrix
- I :
-
Unit matrix
- \(\Omega\) :
-
Coherence estimation neighborhood
- \(s\left( \cdot \right)\) :
-
The complex SAR image used for interference
- D :
-
The number of SHPs in \(\Omega\)
- R :
-
The number of Bootstrap samples
- \(\gamma^{*}\) :
-
The coherence value for R samples
- \(\tilde{T}\) :
-
Reconstructed coherence matrix
- N :
-
The number of SAR images
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Acknowledgements
The authors would like to thank the European Space Agency for providing Sentinel-1A data.
Funding
The research work was funded by the Natural Science Foundation of China (No. 42274054, 51774270); Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People's Republic of China (No. KLSMNR-G202218); Intergovernmental International Scientific and Technological Innovation Cooperation Project (No. 2017YFE0107100).
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Tian, Z., Zhao, L., Fan, H. et al. Mining subsidence monitoring using distributed scatterers InSAR based on Goldstein filter and Fisher information matrix-weighted optimization. Nat Hazards 120, 4205–4231 (2024). https://doi.org/10.1007/s11069-023-06359-2
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DOI: https://doi.org/10.1007/s11069-023-06359-2