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Abstract Subsurface processes significantly influence surface dynamics in permafrost regions, necessitating utilizing diverse geophysical methods to reliably constrain permafrost characteristics. This research uses multiple geophysical techniques to explore the spatial variability of permafrost in undisturbed tundra and its degradation in disturbed tundra in Utqiaġvik, Alaska. Here, we integrate multiple quantitative techniques, including multichannel analysis of surface waves (MASW), electrical resistivity tomography (ERT), and ground temperature sensing, to study heterogeneity in permafrost’s geophysical characteristics. MASW results reveal active layer shear wave velocities (V_s) between 240 and 370 m/s, and permafrostV_sbetween 450 and 1,700 m/s, typically showing a low‐high‐low velocity pattern. Additionally, we find an inverse relationship between in situV_sand ground temperature measurements. TheV_sprofiles along with electrical resistivity profiles reveal cryostructures such as cryopeg and ice‐rich zones in the permafrost layer. The integrated results of MASW and ERT provide valuable information for characterizing permafrost heterogeneity and cryostructure. Corroboration of these geophysical observations with permafrost core samples’ stratigraphies and salinity measurements further validates these findings. This combination of geophysical and temperature sensing methods along with permafrost core sampling confirms a robust approach for assessing permafrost’s spatial variability in coastal environments. Our results also indicate that civil infrastructure systems such as gravel roads and pile foundations affect permafrost by thickening the active layer, lowering theV_s, and reducing heterogeneity. We show how the resultingV_sprofiles can be used to estimate key parameters for designing buildings in permafrost regions and maintaining existing infrastructure in polar regions. 

Seismic imaging and monitoring of the near-surface structure are crucial for the sustainable development of urban areas. However, standard seismic surveys based on cabled or autonomous geophone arrays are expensive and hard to adapt to noisy metropolitan environments. Distributed acoustic sensing (DAS) with pre-existing telecom fiber optic cables, together with seismic ambient noise interferometry, have the potential to fulfill this gap. However, a detailed noise wavefield characterization is needed before retrievingcoherent waves from chaotic noise sources. We analyze local seismic ambient noise by tracking five-month changes in signal-to-noise ratio (SNR) of Rayleigh surface wave estimated from traffic noise recorded by DAS along the straight university campus busy road. We apply the seismic interferometry method to the 800 m long part of the Penn State Fiber-Optic For Environment Sensing (FORESEE) array. We evaluate the 160 virtual shot gathers (VSGs) by determining the SNR using the slant-stack technique. We observe strong SNR variations in time and space. We notice higher SNR for virtual source points close to road obstacles. The spatial noise distribution confirms that noise energy focuses mainly on bumps and utility holes. We also see the destructive impact of precipitation, pedestrian traffic, and traffic along main intersections on VSGs. A similar processing workflow can be applied to various straight roadside fiber optic arrays in metropolitan areas.