# Investigating Mining-Induced Surface Subsidence in Mountainous Areas Using Integrated InSAR and GNSS Monitoring

## TL;DR

This study combines InSAR and GNSS to monitor mining-induced surface subsidence in mountainous areas, achieving more accurate results than using either method alone.

## Contribution

A novel integrated method for mining subsidence monitoring that combines InSAR and GNSS data to improve accuracy and reliability.

## Key findings

- The maximum surface subsidence rate was −186.68 mm/year with a maximum subsidence amount of 248 mm.
- The root mean square error of the data collaborative monitoring was reduced by 96.8% compared to InSAR alone.
- The integrated method outperforms standalone InSAR or GNSS in monitoring mining-induced subsidence.

## Abstract

Leveraging the complementary advantages of InSAR and GNSS, this study proposes a refined method for monitoring mining-induced surface subsidence by integrating both technologies. The method begins with calculating the time-series cumulative subsidence basin from InSAR. Subsequently, a constraint condition is established to identify large-gradient deformations, thereby distinguishing the subsidence edge from the subsidence center. For the subsidence edge with minor deformation, the InSAR results are retained. For the large-gradient subsidence center, the subsidence basin around the mining panel is reconstructed by integrating InSAR and GNSS models. Continuous surface deformation information in a geographic coordinate system is then obtained through spatial interpolation, ultimately yielding comprehensive surface subsidence results across the mining area. Taking a mining area in Shanxi Province as the study region, the feasibility and accuracy of the proposed method were validated using 35 SAR images acquired between April 2016 and September 2017, along with leveling measurement data from the mining panel. The maximum surface subsidence rate of the settlement basin obtained from the solution is −186.68 mm/year, and the maximum surface subsidence amount is 248 mm. Compared with the InSAR monitoring results, the root mean square error of the data collaborative monitoring is reduced by 96.8%, and it is reduced by 64.4% compared with the GNSS probability integral method. The results demonstrate that the proposed method can achieve subsidence results consistent with the actual situation. Its monitoring capability is significantly superior to that of using either InSAR or GNSS alone, effectively compensating for the limitations inherent in each individual technology when applied to mining subsidence monitoring. Consequently, this integrated approach provides more accurate and reliable information on surface subsidence in mining areas.