Dense-array ambient noise tomography is a powerful tool for achieving high-resolution subsurface imaging, significantly impacting geohazard prevention and control. Conventional dense-array studies, however, require simultaneous observations of numerous stations for extensive coverage. To conduct a comprehensive karst feature investigation with limited stations, we designed a new synchronous–asynchronous observation system that facilitates dense array observations. We conducted two rounds of asynchronous observations, each lasting approximately 24 h, in combination with synchronous backbone stations. We achieved wide-ranging coverage of the study area utilizing 197 nodal receivers, with an average station spacing of 7 m. The beamforming results revealed distinct variations in the noise source distributions between day and night. We estimated the source strength in the stationary phase zone and used a weighting scheme for stacking the cross-correlation functions ($C^1$ functions) to suppress the influence of nonuniform noise source distributions. The weights were derived from the similarity coefficients between multicomponent $C^1$ functions related to Rayleigh waves. We employed the cross-correlation of $C^1$ functions ($C^2$ methods) to obtain the empirical Green’s functions between asynchronous stations. To eliminate artifacts in $C^2$ functions from higher-mode surface waves in $C^1$ functions, we filtered the $C^1$ functions on the basis of different particle motions linked to multimode Rayleigh waves. The dispersion measurements of Rayleigh waves obtained from both the $C^1$ and $C^2$ functions were utilized in surface wave tomography. The inverted three-dimensional (3D) shear-wave (S-wave) velocity model reveals two significant low-velocity zones at depths ranging from 40 to 60 m, which align well with the karst caves found in the drilling data. The method of short-term synchronous–asynchronous ambient noise tomography shows promise as a cost-effective and efficient approach for urban geohazard investigations.