Inversion Study of Goaf and Water‐Rich Zones in the Renjiazhuang Coal Mine Using the ESPAC Method

ABSTRACT We aim to explore the distribution characteristics of coal mine goaf and water‐rich areas and improve the accuracy of underground hidden structure identification. We take the Renjiazhuang Coal Mine in Ningxia as a case study and deploy a linear station array to acquire microtremor data. Surface–wave dispersion curves are extracted using the spatial autocorrelation (SPAC) and extended spatial autocorrelation (ESPAC) methods. We then invert the S‐wave velocity structure using a genetic algorithm (GA) and a simulated annealing (SA) algorithm. The results show that ESPAC provides better noise resistance in the low‐frequency band and yields more continuous and higher resolution dispersion curves than SPAC. In addition, the GA inversion outperforms the SA inversion in terms of convergence accuracy and stability. The low‐velocity anomalies identified from the GA inversion are highly consistent with the actual goaf and water‐rich zones, demonstrating the accuracy and applicability of the combined ESPAC–GA approach under complex geological conditions. We provide a technical reference for the application of microtremor technology in complex geological environments and lay a foundation for the optimisation of subsequent multimodal fusion and inversion algorithms.