Analysis of SBAS-Insar Mining Mine Subsidence Monitoring Capability and Optimization Based On Log-Logistic Models

The monitoring of surface deformation in mining areas is of significant importance for preventing and controlling geological hazards. However, existing research often focuses on the spatial and temporal development of surface subsidence, while neglecting the influence of processing parameters in SBAS-InSAR technology on its monitoring capability in mining areas and the subsequent processing of deformation data. This paper analyzes how the unwrapping coherence threshold setting in SBAS-InSAR affects the monitoring range and accuracy of surface deformation in mining areas. Furthermore, we employed a four-parameter Log-logistic model and a genetic algorithm (GA) to more accurately represent dynamic subsidence, subsidence velocity, and acceleration characteristics of ground surface points at both initial
$(t=0)$
and infinite
$(t=\infty)$
time in coal mining areas. These techniques are used for fitting and optimizing deformation data of ground surface points in mining areas, aiming to enhance the accuracy of deformation results obtained through SBAS-InSAR.