Accurately predicting large-scale surface displacements is a vital task in the prevention and control of geological hazards in mining areas. Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) faces challenges in capturing significant deformations, and implementing extensive global navigation satellite system (GNSS) monitoring can only obtain a single point of deformation. To combine the strengths of both techniques, we proposed a novel approach named the LL-SIG (Integrating single SBAS-InSAR pair, GNSS and Log-logistic model), which leverages the understanding of mining subsidence patterns and integrates single-orbit InSAR data with limited GNSS data to achieve overall and high-gradient three-dimensional (3-D) deformation monitoring and prediction in mining areas. Firstly, we employed empirical Bayesian kriging interpolation to integrate GNSS and SBAS-InSAR data at a consistent time interval. This allowed us to obtain an overall large-gradient deformation pattern of the mining area based on its subsidence characteristics. Subsequently, we combined the Log-logistic model with the SIP (single InSAR pair) model to calculate the 3-D deformation resulting from mining activities at any moment. The proposed method was tested in the Fenxi Ruitai mine. The root mean square error (RMSE) of the fitted deformation in the line-of-sight (LOS) direction was within 10% of the maximum deformation. Compared to the Logistic model and Gompertz model, the Log-logistic model show superior accuracy and efficiency in mine deformation prediction under the same testing conditions. The RMSE of deformation for this method was 3.20 cm, 5.85 cm, and 3.73 cm in the vertical, northward, and eastward directions, respectively.

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Assessing the development potential of gravity energy storage (GES) in mining goafs is vital for the integrating new energy power generation, achieving carbon neutrality goals, and transforming resource-based mining cities. This study proposes an integrated evaluation system using time-series interferometric synthetic aperture radar (InSAR) and a geographic information system (GIS) to assess the safe development potential of GES in goafs. First, a goaf subsidence risk model is constructed based on time-series InSAR. Subsequently, a GES development suitability model is established by integrating environmental parameters including topography and geomorphology. Finally, the GES development potential of goafs is quantitatively evaluated based on energy storage capacity and the Levelized Cost of Gravity Storage (LCOGS). A case study in Yangquan demonstrates that, based on SBAS-InSAR monitoring results from 2021 to 2022, seven out of eight closed mining areas covering a total area of 53.91 km² are suitable for the development of GES facilities. Under defined scenarios, the Nanzhuang Mining Area demonstrates particularly strong potential, with a projected energy storage scale of 410 MWh and an LCOGS of 0.5894 CNY/kWh, indicating good GES development potential. The proposed safety-constrained workflow provides a robust tool to guide GES development in goafs and is transferable to other resource-based and post-mining cities worldwide.