Abstract: To address the issues of local optima and inaccurate edge subsidence predictions in the parameter inversion process of the mining subsidence probability integral method, the authors propose applying the dung beetle optimizer algorithm to invert probability integral method parameters and integrate SBAS-InSAR subsidence monitoring values to obtain comprehensive subsidence information for the mining area. The method first utilizes gradient information from SBAS-InSAR technology to obtain reliable subsidence values for areas with small deformation gradients in the mining area. It then applies to the dung beetle optimizer algorithm, known for its strong optimization capability and high accuracy, to invert the parameters of the probability integral method and calculate subsidence values for areas with large deformation gradients. Finally, the subsidence values from the probability integral method and SBAS-InSAR monitoring are fused using a quadratic distance weighting method approach to derive the mining subsidence deformation information for the mining area. Using the 10604 working face of the Malan Mine in Gujiao City, Shanxi Province, as the study area, experimental analysis was conducted using data from 62 field leveling monitoring points and 25 Sentinel-1A images. The results indicate that the parameter inversion using the dung beetle optimizer algorithm is excellent, and accurate subsidence information can be obtained after data fusion. This approach improves accuracy by 59 % compared to using SBAS-InSAR alone and by 32 % compared to using the probability integral method alone.