Distributed Acoustic Sensing (DAS) has revolutionized ambient noise interferometry, yet the reliability of retrieved empirical Green’s functions (EGFs) remains highly sensitive to non-diffuse noise fields and anthropogenic transients. In complex or noisy environments, global metrics frequently misclassify signal convergence due to out-of-band environmental noise, scattered coda, and non-stationary directional transients. Using both 30 min and 4 h passive recordings, this study presents a physics-guided quality control framework for evaluating interferometric diagnostics. Specifically, this study employs the signal-to-trailing noise ratio (STRN), signal-to-precursory noise ratio (SPNR), and spectral signal-to-noise ratio (SSNR) within the surface-wave arrival window $t=x/v$. Global phase metrics remain heavily suppressed $(\overline{x}\approx 0.05)$ regardless of stacking duration, whereas the surface-windowed SPNR exhibits an extraordinary statistical shift (p < 0.001), reaching 0.870 ± 0.106 at 30 min and 0.967 ± 0.034 at 4 h. We implement one-to-one correspondence between surface-windowed indicator values and fundamental-mode Rayleigh wave dispersion sharpness. In severely noise-contaminated segments, unwindowed global metrics yield distorted dispersion ridges with severe energy leakage, but the surface-wave window results in an increase in the SPNR above 0.70, fully reconstructing continuous dispersion trajectories (250–500 m/s). Grounded in these results, we formalize a standardized four-step quality control workflow (from velocity windowing to metric calculation, automation, and data output) and outline tailored adaptation guidelines for urban, mountainous, and industrial DAS deployments. This framework provides an automated, physically sound protocol that eliminates manual selection, optimizes computational efficiency, and ensures reliable dispersion extraction for passive DAS imaging.