Abstract: Research on monitoring and prediction of surface deformation in mining areas is of great significance for safe production and disaster prevention and warning in mining areas. Existing studies tend to monitor and predict the vertical subsidence of the ground surface, and there are fewer studies on the prediction of 3D directional deformation. To address the above problems, this paper is based on the small baselines set synthetic aperture radar interferometry (SBAS-InSAR) technology to monitor the surface deformation of the west second mining area of Jinchuan mining area with multi-track data. A combined SMA-CNN-GRU-Attention network model with slime mould Algorithm (SMA) is proposed to predict the surface deformation in this area. The results show that adding SMA for optimal parameter solving reduces the MAE and RMSE of the vertical prediction results by 30% and 46% compared to the CNN-GRU network model; the MAE and RMSE of the north-south prediction results are 37% and 39% lower, respectively; and the accuracy of the east-west prediction results is lower, with the MAE and RMSE lower than those of the CNN-GRU network model by 6% and 10%, respectively. The SMA algorithm can accelerate the efficiency of the optimal parameter selection of the model, and it can also improve the prediction performance of the CNN-GRU-Attention model to a larger extent.The SMA-CNN-GRU-Attention multi-feature input prediction model has the superiority compared with other prediction models, and it provides an effective method for the research of 3D deformation prediction of the ground surface.

Key words: SBAS-InSAR, deformation monitoring, 3D deformation prediction, SMA optimization algorithm, combined models