Fiber-Optic Seismology Reveals Structural–Hydrothermal Coupling in Permafrost Degradation on the Qinghai–Tibet Plateau

Permafrost degradation along the Qinghai–Tibet Engineering Corridor (QTEC) poses severe risks to infrastructure stability. While current monitoring techniques often lack sufficient spatial continuity or depth resolution, Distributed Acoustic Sensing (DAS) offers a scalable alternative for subsurface characterization. This study introduces a high-resolution imaging framework that leverages existing fiber-optic infrastructure and a Convolutional Neural Network (CNN) to isolate transient traffic-induced vibrations from low Signal-to-Noise Ratio (SNR) DAS records. By selectively stacking these detected signals, we expand the recoverable frequency range of ambient noise interferometry to 45 Hz (vs. 35 Hz for standard stacking), enabling the reconstruction of a 2D shear-wave velocity (Vs) profile near the Kunlun Mountain Pass. The imaging results clearly delineate the permafrost table and base, revealing a permafrost thickness of up to 90 m. Furthermore, we identify pronounced lateral heterogeneities and a structural discontinuity interpreted as a fault-controlled talik. This subsurface anomaly spatially coincides with localized subsidence observed via InSAR, highlighting a coupled structural–hydrothermal mechanism for degradation. Our workflow demonstrates the efficacy of AI-enhanced "dark fiber" sensing for identifying concealed cryospheric hazards in remote alpine regions.