Deformation prediction for the Yuka mining area based on LICSBAS using a velocity-neighborhood spatial algorithm combined with a temporal graph convolutional network (T-GCN) - Jia, Hongguo; Yang, Yang; Wang, Zhiwei; Zeng, Xin; He, Yujun; Bai, Zhenghang; Liu, Yuchen (IEEE journal of selected topics in applied earth observations and remote sensing, 2025)
It is becoming increasingly vital to assess the risks of geological disasters associated with mining activities. However, most existing methods solely evaluate the deformation of individual points in mining areas without accounting for the overall deformation of an entire working face; additionally, if decorrelation occurs, it can degrade the interferometric time-series InSAR measurements of existing models. To address these disadvantages, we introduce a novel velocity-neighborhood spatial algorithm to accurately predict mining deformation trends and build upon the precise extraction of surface deformations from mining areas. Initially, surface deformation data from the Yuka mining area in Qinghai Province were obtained using the LiCSBAS method and transformed into a graphical structure. Subsequently, the T-GCN model was used to predict deformation trends, and the results show that it improved upon the accuracy of traditional recurrent neural networks and machine learning algorithms by 4% with a 15% reduction in root mean square error, thus proving its substantive advantages in spatiotemporal deformation trend predictions. The proposed method provides a more accurate and comprehensive scientific basis for monitoring and controlling geological disasters in mining areas while showcasing the vast application prospects of the T-GCN model.