Ground surface uplift prediction of abandoned coal mines from InSAR observations using Weibull function and Kalman filter

Ground uplift caused by groundwater rebound in closed coal mining areas threats infrastructure safety. We proposes a novel method for predicting surface uplift that does not rely on in-situ measurements of groundwater levels or geological/mining data. The method is based on the characteristic 'S'-type growth pattern of surface uplift revealed by the principle of effective stress. The Weibull function and the Kalman filter are integrated for prediction model development. The Weibull function captures the long-term main trend, while the Kalman filter dynamically assimilates time-series InSAR monitoring data to account for actual fluctuations. An empirical study conducted at the closed Guanshan and Taiji mines in Beipiao City, Liaoning Province, is sued to validate the proposed model. The prediction shows an average deviation of only −1.2 mm and an average standard deviation of 2.9 mm when compared to leveling survey data, achieving an overall prediction accuracy of approximately 3 mm. By combining historical InSAR deformation data with the proposed model, this approach overcomes the dependency of traditional methods on field monitoring data, enabling accurate prediction of the surface uplift process in closed mining areas. This research not only provides a new methodological perspective for analyzing the mechanisms of mining-induced surface deformation but also offers efficient and reliable technical support directly applicable to land reclamation planning, infrastructure risk prevention and control in mining regions.