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This dataset provides distributed acoustic sensing (DAS)-derived axial strains of a ~2 km (kilometer) fiber-optic cable in permafrost terrain near Utqiaġvik, Alaska, during October 2021 to September 2022. The dataset establishes a baseline for assessing cryoseismic activity and dynamic near-surface vibration in Arctic permafrost and supports broader investigations of geophysical and geomechanical processes in Arctic permafrost environments using distributed fiber-optic sensing. 

Abstract Urban karst geology poses significant geohazard risks, most notably sinkholes and surface depression stemming from soluble and fractured bedrock that is prone to dissolution and collapse. However, mapping and characterizing these hazards using traditional geophysical surveys in cities is challenging due to dense infrastructure and high levels of human activity. In this work, we demonstrate how distributed acoustic sensing (DAS), deployed via preexisting telecommunication fiber‐optic cables, can be leveraged to detect fractured weak zones in a populated setting. By recording traffic noises, we are able to conduct large‐scale, cost‐effective, and minimally intrusive subsurface investigations. Our workflow integrates ambient noise interferometry with advanced signal enhancement techniques, specifically frequency‐wavenumber (F‐K) filtering and bin‐offset stacking. F‐K filtering isolates wavefields traveling in opposite directions to suppress localized noise, while bin‐offset stacking further enhances signal coherency by superposing channels with common offsets. The resulting Noise Cross‐correlation Functions exhibit unique inverse‐dispersion patterns that signify the presence of leaky surface waves generated by a low‐velocity half‐space. We invert the corresponding dispersion curves to derive a 2D S‐wave velocity model, highlighting a prominent low‐velocity anomaly indicative of a fractured zone. To confirm the karstic nature of this anomaly, rock physics modeling is employed to estimate spatial variations in fracture density, revealing marked heterogeneity in the fractured zone. Our findings underscore the power of DAS‐based ambient noise interferometry for delineating karst features and diagnosing potential sinkhole risks in urban environments. By exploiting widely available fiber‐optic networks, this approach significantly broadens the practicality of near‐surface geohazard mapping at the city scale. 

Abstract Distributed acoustic sensing (DAS) with preexisting telecommunication optical fibers (dark fibers) has shown its ability to record rain‐induced seismic noise with unprecedented high spatiotemporal resolution. This rain‐induced noise exhibits strong correlations with rainfall intensity and rainwater discharge in pipeline sewers, highlighting its potential to infer rainwater flow characteristics. While raindrop impact models exist, a physical model linking stormwater discharge processes to DAS‐recorded signals is still lacking. In this study, we introduce a data‐driven method, deep embedded clustering (DEC), to automatically detect and classify rain‐induced noise from massive DAS data, predicting the presence of moderate to heavy rain and the duration of stormwater discharge. We analyze continuous DAS recordings from 2019 to 2021 from a 4.2 km‐long underground fiber‐optic array in State College, PA. During training, the DEC model employs an autoencoder to learn the latent features from preprocessed spectrograms and then clusters these latent features into four clusters. Distinct features from spectrograms within each cluster reveal that four clusters correspond to background noise, rain‐induced noise of varying rain intensities and stormwater discharge in sewers. Tests on unseen data sets in 2019 and 2021 demonstrate DEC's ability to not only predict rainfall rate levels but also indicate post‐rain discharge durations. Furthermore, the model‐derived post‐rain discharge durations align with synthetic hydrograph estimates, yielding a drainage system time of concentration as 21 min in this region. Finally, we apply this workflow to two more locations to show the potential of spatial monitoring. Our results show that the combination of machine learning and fiber‐optic sensing offers a scalable solution for improving stormwater management in urban environments. 

### Access Files be accessed and downloaded from the directory via: [http://arcticdata.io/data/10.18739/A2736M43B](http://arcticdata.io/data/10.18739/A2736M43B). ### Overview This dataset is part of the outcomes of a collaborative project funded by the National Science Foundation (NSF) Signals in the Soil (SitS) program. This project focused on dynamic soil processes and the geophysical and geomechanical characterization of permafrost. This dataset focused long-term permafrost monitoring using distributed temperature sensing (DTS) systems. The dataset provides ground temperature profiles from the active layer to the near-surface permafrost in Utqiaġvik, Alaska from 2021 to 2024, establishing a baseline for evaluating future changes in permafrost thermal conditions under natural environmental settings. Temperature measurements were collected using DTS technology along a 2-kilometer-long fiber-optic cable, co-located with a distributed acoustic sensing (DAS) array. These DTS-derived temperature profiles offer essential ground-truth data to support the interpretation of geophysical and geomechanical properties derived from DAS measurements. The outcomes of this project support realistic evaluations of infrastructure performance in Arctic Alaska and inform the design of more resilient and adaptive infrastructure systems in permafrost regions. 

Abstract The mechanical state of Arctic landfast sea ice remains poorly constrained due to limited observations. This study investigates interactions between drifting sea ice and the coastal landfast ice near Utqiaġvik, Alaska by integrating data from broadband seismometer, Distributed Acoustic Sensing, and marine radar. We find that decreases in sea ice velocity, marking transitions from drift to compressive contact, coincide with increased seismic energy. Tremor characteristics vary seasonally with ice conditions. In January, dense ice packs produced sustained harmonic tremors with gliding and U‐shaped spectral features, consistent with repetitive stick‐slip motion at the ice–ice or ice–ground interface under velocity‐weakening friction. In April, smaller fragmented floes generated short‐lived, chaotic tremors linked to brittle failure and spatially dispersed impacts. These findings demonstrate that seismic tremors encode the mechanical properties of interacting ice, offering a new tool to distinguish ice regimes and monitor evolving Arctic coastal dynamics under climate change. 

Abstract Arctic permafrost is rapidly degrading in response to global warming. Its thermodynamic evolution governs carbon emissions, hydrological shifts, and terrain stability, with critical consequences for both natural systems and built infrastructure. Accurate prediction of the thermodynamic behavior of permafrost remains elusive, hindered by limited observations and underdeveloped methodologies. Here, we introduce a digital twin framework that integrates differentiable modeling (DM) with high spatial resolution distributed temperature sensing (DTS) data to predict and infer key permafrost characteristics—ground temperature, unfrozen water content, thermal conductivity, and heat capacity. By leveraging a neural‐network‐based parameterization, our framework fuses observational data with physical heat transfer equations, enabling real‐time calibration and updating of the spatiotemporally varying soil thermodynamic characteristics. Applied to permafrost beneath a road embankment in Utqiaġvik, Alaska, the digital twin accurately reconstructs the spatiotemporal evolution of soil temperature fields and captures spatial variability in permafrost thermodynamic properties. The prediction results were further validated against shear‐wave velocity distributions inferred from distributed acoustic sensing (DAS), temperature data obtained from borehole thermistors, and thermodynamic properties measured by laboratory testing, demonstrating the framework's robustness. This work advances the predictive understanding of permafrost dynamics under climate change and establishes a generalizable pathway for digital twin applications in Arctic science. 

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.