Abstract: The loess mining area in northern Shaanxi often has the characteristics of shallow coal seam and thin bedrock.
High-intensity mining can easily lead to a large amount of surface deformation. For large-scale deformation,in the process of
applying InSAR technology,the center of the subsidence basin will have the problem of decoherence and the corresponding
phase unwrapping error. The probability integration method can effectively compensate for the subsidence information in the
center of the subsidence basin. In order to improve the accuracy and reliability of mining subsidence parameter inversion,GNSS
and SBAS-InSAR monitoring data are integrated,and sparrow search algorithm (SSA) is introduced to carry out high-precision
parameter inversion,aiming to establish a more efficient and accurate subsidence monitoring and evaluation method. Taking a
working face in northern Shaanxi mining area as the test area,the results show that SBAS-InSAR technology can obtain largescale
surface deformation compared with traditional monitoring methods. The sparrow algorithm is used to obtain the predicted
parameters of the probability integral method of the subsidence basin,and the accuracy of the results is high. Compared with the
measured results,the fitting results of InSAR and GNSS are better,which can provide theoretical reference for the study of surface
subsidence law and coal mine safety in mining area.

Key words: mining subsidence,SBAS-InSAR,parameter inversion,probability integral,sparrow optimization algorithm