Beyond supporting ultra-high-capacity data transmission, metropolitan and access networks are expected to enable real-time infrastructure monitoring, driving the emergence of integrated sensing and communication (ISAC). Distributed acoustic sensing (DAS) has proven to be well-suited to urban sensing application requirements, yet its seamless integration into ISAC remains challenging—conventional high-peak-power sensing pulses in DAS induce nonlinear crosstalk in communication channels. DAS inherently suffers from interference fading due to single-frequency laser sources, which limits sensitivity. Here, we propose an ISAC architecture based on an electro-optic (EO) comb and a 7-core fiber, achieving nonlinearity-suppressed self-homodyne transmission and fading-suppressed DAS. Unmodulated comb lines and sensing pulses are polarization-multiplexed into orthogonal polarization states within the central core to minimize nonlinear crosstalk while delivering local oscillators (LOs) for wavelength division multiplexing (WDM) coherent transmission within six outer cores—achieving 10.56 Tbit/s capacity. In addition to supporting WDM transmission, the EO comb’s wavelength diversity is also exploited to enhance DAS performance. Specifically, a dual-pulse probe loaded onto four comb lines yields a 6 dB signal-to-noise ratio gain and a 64% reduction in fading occurrences, achieving a sensitivity of 1.72 pε/Hz with 8 m spatial resolution. Moreover, our system supports simultaneous multi-wavelength backscatter detection in sensing and simplified digital signal processing in self-homodyne communication, reducing receiver complexity and cost. Our work presents a scalable, energy-efficient ISAC framework that unifies high-capacity communication with high-sensitivity sensing, providing a blueprint for future intelligent optical networks.

Abstract Extreme weather significantly challenges the effective and timely monitoring of landslide disasters. Distributed acoustic sensing (DAS) offers unique capabilities for monitoring slope failures during extreme weather events such as typhoons by transforming pre-deployed optical fiber cables into high-resolution vibration-acoustic sensor arrays. This study documents sudden shifts in landslide disturbance signals during a super typhoon’s passage using DAS with 1 Hz downsampled modulated signals. By leveraging multi-domain analysis (time-frequency-space), we identify landslide disturbance micro-deformation signatures, revealing interconnected spatial responses and dynamic patterns. We introduce a spatiotemporal indicator evaluation framework to monitor landslide occurrence and evolution under extreme weather conditions. The monitoring of landslide occurrence correlates well with post-disaster incident records and meteorological data. These results demonstrate that DAS systems can enhance early detection and high-resolution monitoring of landslide disasters under extreme weather conditions, highlighting the potential for comprehensive natural disaster management.

Summary In recent years, distributed acoustic sensing (DAS) has enabled the observation of strain over tens of kilometres at metre-level intervals by using optical fibre as a sensor. This study presents an analytical solution for the cross-spectrum of ambient noise with DAS data acquired from arbitrarily shaped and/or multiple fibre-optic cables, with the aim of estimating subsurface S-wave velocity structures using the spatial autocorrelation (SPAC) method. Our formulation accounts for both isotropic and anisotropic wave incidence. The analytical cross-spectrum depends on the angles between the horizontal direction connecting the two measurement points and the axial strain directions at the two points. This study demonstrates that both Rayleigh and Love waves contribute to the cross-spectrum, and that their contributions vary in a complex manner depending on the cable geometry, seismic velocity structure, interstation distance between observation points, and source amplitudes. By using this analytical solution, an integrated analysis combining the SPAC method and the ambient noise tomography method is applicable to DAS data acquired from arbitrarily shaped and/or multiple cables. In addition, the analytical expression considering anisotropic wave incidence will be useful for correcting travel-time anomalies caused by source heterogeneity. The application of our formulation to DAS data from winding or multiple cables will facilitate high-resolution and precise imaging of the three-dimensional structure.

Abstract Employing the multi‐station Thunderstorm Energetic Radiation Observation System, we detected X‐ray bursts during two rocket‐triggered lightning events in 2024. By innovatively integrating optical imaging with three‐dimensional lightning channel reconstruction based on Distributed Acoustic Sensing (DAS), we analyzed the X‐ray emission characteristics from these events. During the Tl_20240812 event, lateral deflection of a descending negative leader resulted in X‐rays being detected exclusively by a distal sensor. This clear spatial correlation provides direct and conclusive geometric evidence that the radiation is emitted in a beam‐like pattern along the leader propagation path. Furthermore, based on the Tl_20240801 event, this study achieved the first quantitative estimation of the X‐ray photon beam half‐angle width, determined to be between 40° and 46°. This angular range aligns with the predicted structure of the leader tip electric field, thereby providing robust support for the hypothesis that X‐rays originate from the leader tip high‐field runaway electron mechanism.

Summary From April until the end of June 2025, we deployed a dense seismic network of 271 three-component stations within an 8 km radius around Lavey-les-Bains, Switzerland, to investigate the structure of the country’s hottest known natural geothermal system. The site hosts a 3 km-deep exploration well (Lavey-1), drilled in 2022, that revealed unexpectedly low flow rates despite temperatures exceeding 120°C, prompting the suspension of the project. The site lies within the narrow Rhône Valley, characterized by steep topography, strong lateral structural heterogeneity, and elevated anthropogenic noise, complicating seismic imaging. The dense nodal array was complemented by a distributed acoustic sensing (DAS) system along a buried telecommunication cable, providing a hybrid dataset suited for passive seismic imaging. We describe the network geometry, instrumentation and deployment logistics; assess data completeness and noise characteristics and present first examples of ambient noise and earthquake recordings. Preliminary analyses demonstrate a high data quality and spatial coverage. This experiment establishes a benchmark dataset for developing advanced passive imaging techniques in complex Alpine environments.

Summary Given the scarcity of seismometers in marine environments, traditional seismology has limited effectiveness in oceanic regions. Submarine Distributed Acoustic Sensing (DAS) systems offer a promising alternative for seismic monitoring in these areas. However, the existing machine learning model trained on land-based DAS data does not perform well with submarine DAS due to differences in noise characteristics, deployment conditions, and environmental factors. This study presents a machine learning approach tailored specifically to submarine DAS data to enable automated seismic event detection and P and S wave identification. Leveraging DeepLab v3, a neural network architecture optimized for semantic segmentation, we developed a specialized model to handle the unique challenges of submarine DAS data. Our model was trained and validated on a dataset comprising nearly 57 million manually and semi-automatically labeled seismic records from multiple globally distributed submarine sites, providing a robust basis for accurate seismic detection. The model adapts to a variety of deployment scenarios and can process DAS data from cables with different lengths, configurations, and channel spacings, making it versatile for various ocean environments. We thus provide an adaptable and efficient tool for automated earthquake analysis of DAS data, which has the potential to enhance real-time earthquake monitoring and tsunami early warning in submarine environments.

Distributed Acoustic Sensing (DAS) has emerged as an innovative technology in seismology, sensing seismic waves along fiber optic cables and, thus, increasing ten‑folds the spatial density of seismic measurements. However DAS potential in seismic monitoring is still under investigation. In this study, we assess the differences in seismic event detection and localization when using DAS, conventional seismometers, and combination of both, by analyzing a dataset acquired during a field experiment in Megolo di Mezzo (Northern Italy). A ~1 km buried fiber optic cable was deployed with an almost L‑shape configuration. Seismic data were continuously recorded from November 2023 to February 2024 and compared with simultaneous observations from the local seismic network (DIVEnet). Several local earthquakes were detected, including microseismic events not listed in the official catalog. P‑wave arrival times were extracted from DAS recordings using different picking algorithms and compared to manual picks from seismometers. Event localization was performed through a Bayesian Monte Carlo approach applied separately to DAS and seismometer data, and jointly. Results demonstrate that DAS shows considerable potential in earthquake detection, particularly for low‑magnitude events and those occurring close to the fiber optic cable, as potentially expected during anthropic activities underground. The joint inversion of DAS and seismometer datasets reduced localization uncertainties and produced solutions consistent with the official INGV catalog. However, differences of up to ~2 km between DAS – and seismometer – based epicenters highlight the limitations of simplified velocity models and the impact of network geometry. These findings confirm the complementarity of DAS and traditional networks and underline the potential of hybrid monitoring strategies for advancing earthquake detection and characterization in complex geological environments.

Distributed Acoustic Sensing (DAS) has emerged as a promising observational tool for a variety of geophysical monitoring applications. Its cost-effectiveness and high spatial sensor density offer a compelling alternative to traditional seismic sensors, particularly in regions where conventional deployment is challenging. DAS inherently measures strain (or strain rate), whereas conventional seismic sensors record displacement (or velocity). However, most seismological algorithms are optimized for translational ground motion data, motivating robust methods for converting DAS data into equivalent ground motion. In this work, we present a novel deep learning model that accurately converts DAS strain into geophone particle velocity trained on co-located nodal seismometers for the PoroTomo data obtained at Brady geothermal field in 2016. The model combines Fourier Neural Operator (FNO) and Bidirectional Long Short-Term Memory (BiLSTM) with an attention mechanism (FNO-BiLSTM-Attention). The model is trained and evaluated using earthquake waveform data recorded simultaneously by co-located DAS channels and geophones. To validate the conversion process, we compared it with both geophone data and a physics-based conversion method. Then, a seismic beamforming analysis was performed using the deep learning-based converted DAS data, with results compared to those from the geophones. The results show an excellent match between both estimations and they are notably better than using DAS strain directly. The further improvement over using nodal data comes from improved signal coherency and density of spatial data.

Thanks to recent advancements in distributed acoustic sensing (DAS) techniques, we can measure the time series of axial strains along an optical fiber at extremely dense spatial intervals. However, only a single component of a strain tensor is measured, and the partitioning of seismic energy into this component is unknown. In this study, we address this problem by formulating energy partitioning into different strain components for diffuse waves in a three-dimensional homogeneous isotropic half-space, building upon previous studies on energy partitioning into displacement components. We investigate how the contributions of both body and surface waves to the six independent components of a strain tensor change with depth. The results show that the horizontal normal strains, which surface DAS observation can measure, are primarily composed of shear horizontal-waves and Rayleigh waves at the free surface. The vertical normal strain, which borehole DAS observation can measure, is dominated by Rayleigh waves at the free surface. However, that contribution quickly decays within the depth of one shear wave-wavelength, and the shear vertical-wave contribution remains. This study serves as a reference for further extension to more realistic media, such as horizontally layered media, and opens a way to interpret the late coda of DAS strain seismograms quantitatively.

Technological advances in global communications depend significantly on robust and efficient long-distance infrastructures. One notable example is the submarine cable network. Installed on the ocean floor, these cables use fiber optic technology to transmit large volumes of data at high speed and low latency between continents. Beyond their primary communication function, recent innovations allow these cables to serve as Distributed Acoustic Sensing (DAS) systems, effectively converting tens of kilometers of passive fiber into massive, coherent arrays of vibration sensors. The primary objective of this project is to enhance maritime surveillance capabilities by integrating DAS technology with advanced kinematic modeling. This paper establishes a rigorous physical and mathematical framework for interpreting the acoustic signatures of surface vessels detected by bottom-mounted fibers. We derive the complete opto- acoustic transfer function, formulate the hyperbolic moveout equations based on a moving point-source solution to the wave equation, and implement a stochastic inversion scheme using Differential Evolution. By optimizing a correlation-based loss function, we demonstrate the ability to recover vessel trajectory, speed, and depth from complex interferometric patterns with speed estimation errors consistently below 1%. This approach allows for the extraction of quantitative physical parameters from raw optical data, bridging the gap between photonics and hydroacoustics.

This article presents an advanced processing workflow for a Seismic While Drilling (SWD) dataset acquired using Distributed Acoustic Sensing (DAS) in a cross-well setting at the Otway International Test Centre (OITC) in Victoria, Australia, where no pilot signals were recorded. Recording the drill bit signature enables and simplifies the decoding of passive seismic signals emitted by the drill bit while drilling. In conventional SWD, a measured drill bit signature is used to correlate passive seismic recordings and to determine source trigger times, analogous to vibroseis processing. Without this reference, both source timing and signature must be inferred from the recorded wavefield. This can typically be achieved by backpropagating the recorded seismic data over short time windows, estimating the source location and trigger time based on the peak RMS energy in space and time. However, to simplify the processing of SWD data, a data processing workflow is presented, guided by travel time and seismic modelling, which transforms passive SWD data into active equivalents. The transformed data can then be used to characterize the subsurface by implementing travel time tomography and cross-well imaging. The results demonstrate reliable velocity and structural information can be recovered from DAS-based SWD data without pilot measurements, enabling simplified and scalable deployment of passive seismic while-drilling workflows.

The integration of high-speed optical communication and distributed sensing could bring intelligent functionalities to ubiquitous optical fiber networks, such as seismic detection, while in the face of a major challenge of constructing a long-haul thousand-kilometer fiber link. This work proposes the first, to our knowledge, interconnected counter-propagating recirculating loop (ICP-RL) with high-loss loop back (HLLB) path as a compact in-lab platform for ultra-long-haul integrated sensing and communication (ISAC), and experimentally demonstrates a simultaneous 32-GBaud PMD-QPSK signal transmission and distributed acoustic sensing (DAS) system application with a spatial resolution of 3.3 km over 3651-km SSMF. The recirculating loop is controlled by acoustic-optical modulators, which also provide an essential frequency shift for the Rayleigh backward scattering sensing signal distinction. The proposed ICP-RL makes possible a low-cost in-lab validation platform for long-haul ISAC systems during the research and development (R&D) phase.

Abstract This study presents the implementation and evaluation of a fiber-optic Distributed Acoustic Sensing (DAS) system for monitoring overhead high-voltage transmission lines in the Turkish Transmission Grid. The DAS system, deployed on a 154-kV transmission line spanning 40 km, utilizes existing optical ground wires (OPGW) for real-time acoustic and vibrational sensing. The study outlines the fundamental principles of DAS technology, detailing how optical fiber functions as a distributed sensor to detect mechanical disturbances along the transmission line. Signal processing algorithms and feature extraction techniques were developed to analyze recorded acoustic signatures associated with different types of activities, including structural impacts, mechanical tampering, and environmental stressors such as wind-induced vibrations and icing effects. Key signal features, including root mean square (RMS) amplitude, crest factor, zero-crossing rate, spectral centroid, power spectral density (PSD), and wavelet entropy, were extracted and analyzed for their suitability in activity classification. Extensive field tests were conducted, including controlled experiments such as hammering, mechanical and manual unscrewing, and metal cutting at various distances from the interrogation unit. A signal processing pipeline was implemented to enhance detection accuracy, utilizing noise reduction, spectral analysis, and feature-based classification. The developed detection algorithm processes real-time acoustic data and assigns an activity score based on extracted features, enabling efficient identification of anomalies and security threats along the transmission line. The system demonstrated high sensitivity to impulsive events, reliably detecting 22 out of 23 activities with a near-zero false alarm rate. The results indicate that DAS technology is capable of accurately monitoring and detecting unauthorized activities around high-voltage transmission towers and it can serve as an effective non-intrusive monitoring solution for power transmission infrastructure. The DAS technology presented herein has the potential to become an essential tool in ensuring the safety and reliability of Türkiye’s energy infrastructure, enabling rapid response to both human-made and natural threats.

The integrity of diaphragm walls is of paramount importance to the safety of foundation pits; leakage can readily induce issues such as instability and heave, necessitating efficient and precise detection. This study conductsl aboratory model tests based on Distributed Acoustic Sensing (DAS) technology to investigate the applicability of active and passive methods in leakage identification, as well as the effects of leakage magnitude, sensing optical cable layout positions, and boundary conditions. The results indicate that DAS can effectively identify leakage through acoustic vibration characteristics: the passive method exhibits distinct leakage characteristic signals at 10.25 Hz and 20.25 Hz, with leakage velocity showing a linear positive correlation with peak amplitude; the active method identifies leakage via an external vibration source, and when the spacing between the sensing optical cable and the leakage channel exceeds 50 cm or the channel depth is less than 5 cm, signal attenuation occurs and distinguishability decreases. This study validates the feasibility of DAS technology, providing a scientific basis for real-time distributed leakage detection in foundation pit engineering.

This study aims to quantitatively assess blockage conditions in highway tunnel drainage pipelines using acoustic wave signals. A full-scale physical model of a drainage pipeline was constructed to simulate six blockage ratio conditions ranging from 12.5% to 75%. Distributed Acoustic Sensing (DAS) technology was employed to collect acoustic signals along the pipeline. Time-domain analysis and Fast Fourier Transform (FFT)-based frequency-domain analysis were conducted to compare the waveform amplitude and dominant frequency components between blocked and unobstructed pipeline sections. The results demonstrate a significant increase in time-domain amplitude at the blockage location, with a maximum enhancement of up to 50% compared to unobstructed sections. In the frequency domain, this phenomenon is particularly pronounced within specific dominant frequency bands (core frequency bands). For instance, the 395–405 Hz band was identified as the core band under the 50% blockage ratio condition. Furthermore, the time-domain amplitude at the blockage shows a positive correlation with the blockage ratio (12.5–75%). The comprehensive analysis indicates that the time-domain characteristics of DAS-based acoustic signals can effectively identify both the location and severity of blockages in highway tunnel drainage pipelines. This research provides fundamental data for evaluating the blockage state of tunnel drainage systems based on acoustic signatures.

Distributed acoustic sensing (DAS) is a new, powerful modality of active and passive acoustic sensing of the ocean and atmosphere. The measurand in DAS is a time-resolved variation of phase of the Rayleigh-scattered coherent light propagating in an optical fiber. The optical phase is coupled to mechanical waves in the surrounding fluid through the strains and stresses in the fiber. Despite the exponential proliferation of DAS applications, physics-based understanding of the transfer function between the acoustic field and the DAS measurand is lacking. We partially fill this gap by considering scattering of acoustic waves by unclad and clad fiber suspended in fluid. The fiber is modeled as an infinite, solid circular cylinder, the properties of which may vary with distance from the cylinder axis. The theory is simplified by the fiber diameter being small compared to acoustic wavelength. DAS proves sensitive to acoustic pressure in the incident wave rather than radial or axial particle displacement. DAS sensitivity is found to differ drastically from the one previously predicted for fiber-optic hydrophones assuming uniform pressure field. The angular and frequency dependence of the DAS transfer function are strongly affected by resonance scattering of sound that is associated with an axially symmetric mode of free vibrations of infinite cylinder. Appropriate cladding can shift the resonance scattering from propagating to evanescent acoustic waves and increase the signal-to-noise ratio of DAS measurements. The DAS transfer function derived for acoustic pressure applies also to pressure variations due to gravity waves.

Abstract This study presents a novel approach to perform high-resolution S-wave crosswell tomography by combining distributed acoustic sensing (DAS) with a borehole-coupled SV-wave source. Linear DAS fibers, which exhibit optimal sensitivity to vertically polarized shear waves, were used to acquire dense seismic data sets with significantly improved spatial resolution compared to conventional geophones. The SV-wave source (BIS-SV) provides efficient signal generation and coupling, allowing accurate traveltime picking and stable tomographic inversion results. Field experiments conducted at the ECCSEL Svelvik CO₂ Field Lab demonstrated that DAS-based S-wave tomography can be performed with acquisition times of 3–4 min per shot depth and a twofold increase in spatial resolution compared to conventional methods. In contrast to P-wave tomography, S-wave results revealed distinct subsurface structures and allowed direct derivation of dynamic shear stiffness, a key parameter in geotechnical engineering. A low correlation (R² = 0.196) between P- and S-wave traveltimes confirmed that both methods provide complementary insights into subsurface properties. In addition, the DAS system eliminated the need for water-filled boreholes and mechanical geophone coupling, providing significant cost and logistical advantages. DAS seismic tomography adds significant value by providing continuous, high-resolution, cost-effective subsurface monitoring with minimal field effort, making it a transformative tool for modern geotechnical and environmental applications.

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.

Abstract Recent advances in Enhanced Geothermal System (EGS) development have opened new frontiers for geothermal energy production. However, both static geophysical imaging and time-lapse monitoring of these systems can be challenging, with recent pilots located in areas with attenuating near-surface sediments and generally low-quality surface seismic data. These same problems are relevant to achieving accurate subsurface characterization, which is essential for optimizing drilling and reservoir development, and enhancing the economic viability of EGS by ensuring sustainable energy extraction. We demonstrate the applicability of source-independent converted wave imaging utilizing microseismic energy to passively image key geologic structures at an active EGS pilot site. To test this imaging concept, we acquired a dense linear nodal (3C) dataset at the Frontier Observatory for Research in Geothermal Energy (FORGE) facility located in Milford, UT. This acquisition campaign, FORGE Observation Array Linear (FOAL) 1, was conducted during the April 2022 stimulation of a deep EGS injection well (FORGE Well 16A). Despite a short observation period (~1 month) and a limited number of seismic events, we successfully imaged the granite/alluvium interface - the major velocity contrast at the FORGE site. The results suggest the potential of our approach for both site characterization and real-time monitoring of subsurface changes. Moreover, this illuminates the prospects of using related large-N technologies, including Distributed Acoustic Sensing (DAS) for source-independent imaging.

Oil and gas pipeline security is critical to national infrastructure, yet existing monitoring systems often lack the sensitivity and real-time responsiveness required to detect subtle intrusion events. This study presents a novel multimodal sensing and interaction frame-work that integrates phase-sensitive optical time-domain reflectometry (φ-OTDR)–based distributed acoustic sensing (DAS) with an optimized one-dimensional convolutional neural network (1-D CNN) architecture. The approach leverages both raw fiber optic vi-bration signals and carefully selected handcrafted features, enabling robust automatic in-trusion classification across multiple event types including manual tapping, mechanical excavation, and human footsteps. By incorporating transfer learning from publicly avail-able human activity datasets, the model achieves enhanced feature generalization, result-ing in a classification accuracy exceeding 95%. This work demonstrates the potential of combining advanced multimodal sensing technologies with deep learning-based interac-tive analytics for real-time pipeline security monitoring, paving the way for intelligent in-frastructure protection systems. Future efforts will focus on expanding dataset diversity, integrating multi-sensor fusion, and enhancing adaptive interaction capabilities for field deployment.