What question did this study set out to answer?

The aim is to enhance the accuracy and reliability of surface subsidence measurements in mining regions.

Monitoring and analysis of surface subsidence in mining areas of integrated SBAS-InSAR and probability integral method

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Combines SBAS-InSAR and probability integral method to accurately assess surface subsidence in mining areas, suggesting a robust monitoring approach.

The aim is to enhance the accuracy and reliability of surface subsidence measurements in mining regions.
Utilized SBAS-InSAR technology to capture cumulative surface subsidence data.
Inverted parameters of the probability integral method using data from edge subsidence monitoring and profile leveling.
Calculated average coherence coefficient and maximum deformation as thresholds for data fusion.
Validated approach with 228 Sentinel-1 images and profile leveling data from April 2015 to December 2023.
Achieved a root mean square error (RMSE) of 28.27 mm, outperforming individual methods.
Demonstrated strong alignment between the subsidence trend and leveling data.
Successfully matched combined data with leveling measurements except for a few features.

Gao et al. (2026) studied this question.

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Combines SBAS-InSAR and probability integral method to accurately assess surface subsidence in mining areas, suggesting a robust monitoring approach.