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Train noise is a kind of green, non‐destructive and strong‐energy artificial seismic sources, which is widely used in railway safety monitoring, near‐surface imaging and urban underground space exploration. Distributed acoustic sensing is a new seismic acquisition technology, which has the advantages of dense sampling, simple deployment and strong anti‐electromagnetic interference ability. In recent years, distributed acoustic sensing has been gradually applied in the fields of urban traffic microseism monitoring, crack detection and underground space imaging. However, previous studies mainly focused on microseism interferometry using train event coda noise, and there is limited research on the workflow of interferometry imaging using distributed acoustic sensing–based heavy train events noise (with short coda windows), which produces an abundant of near‐source interference. Aiming at proving the effectiveness of this idea, we investigated a process workflow to get underground shear‐velocity structure based on distributed acoustic sensing recorded heavy traffic noise near Qinhuangdao train station. A weighted sliding absolute average method is used to weaken the strong amplitude to the coda wave level and reduce the near‐source influence. We demonstrated that the cross‐coherence interferometry method, after spectral whitening, has the best effect on sidelobe suppression in the virtual source surface wave shot gathers, through a comparative analysis of cross‐correlation and cross‐coherence results. For obtaining concentrated energy and strong continuity in phase velocity spectra, we selected the time windows with high spatial coherence and signal‐to‐noise ratio not less than 1.2 for stacking from 720 time windows in F–K domain. When dividing subarrays to extract pseudo‐two‐dimensional profile, we set the overlap rate between adjacent time windows to 80% to increase stacking times, enhancing the precision of phase velocity spectra and reducing the errors of picking dispersion curve. Our results show that heavy traffic train events noise (non‐pure coda) can be used to detect underground velocity structure with clear dispersion and high inversion reliability. This research provides a new processing flow for distributed acoustic sensing train noise imaging and can be applied in future urban underground space exploration. [ABSTRACT FROM AUTHOR]

Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi‐supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million m3 ${\mathrm{m}}^{3}$ on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground‐truth labeling. The proposed algorithm is capable of distinguishing between rock‐slope failures and background noise, including road and train traffic, with a detection precision of over 95% $95\%$. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal‐to‐noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards. Plain Language Summary: Steep mountains and hills produce dangerous rockfalls and similar phenomena such as landslides and debris flows. A major collapse is typically preceded by a series of rockfalls over days or months. It is therefore crucial to reliably detect these events and recognize the warning signs of an impending major collapse. When rocks bounce on the ground they release seismic waves, which generate vibrations that propagate long distances. Such vibrations stretch and compress fiber optic cables within the ground enough so they can be measured with a novel technique called Distributed Acoustic Sensing (DAS). Here we show how to identify such DAS signals using machine learning algorithms to detect precursory rockfall activity and a major collapse on a slope in Switzerland. We compare our detections with radar measurements, which are highly reliable but also come at a greater cost for installation. Since we can apply DAS to unused fiber within the ground, our approach may pave the way for the next generation of natural hazard warning. Key Points: A semi‐supervised neural network is developed for rock‐slope failure monitoring with Distributed Acoustic Sensing at Brienz, SwitzerlandOur model achieves over 95% precision for rock slope failures detected by a Doppler radar system over 45 daysThe sustained detection of slope failures before the major collapse highlights the potential of our approach for early warning [ABSTRACT FROM AUTHOR]

On continental margins, sediments cause significant and spatially variable delays in seismic phase arrival times. The strong impedance contrast of the sediment‐bedrock interface causes P‐wave splitting that is clearly seen on distributed acoustic sensing recordings of earthquakes, resulting in additional phase arrivals that must be picked separately. We introduce sediment corrections to correctly account for those additional phases in the hypocenter localization procedure. Conceptually, the sediment correction method differs from the commonly‐used station corrections; instead of introducing a mathematically optimal constant time delay for each station and each phase, the corrections are derived from a physical, first‐order modeling of the wave propagation in the sediments. To calibrate the sediment corrections, a two‐step procedure is adopted: (a) the delay between the P‐phase and the converted Ps‐phase is taken as a proxy of the sediment thickness; (b) the P‐ and S‐wave speeds are determined through inversion. We show that sediment corrections are able to account for most of the observed bias while considerably reducing the number of free parameters compared to classical station correction. Moreover, the observed local delays are almost fully explained by the presence of the sedimentary layer, rather than by the 3D velocity variations of the bedrock. We retrieve vP ${v}_{\mathrm{P}}$ and vS ${v}_{\mathrm{S}}$ values that are compatible with values commonly found for sediments. Given the simplicity and physical foundation of the proposed method, we recommend the use of sediment corrections over station corrections whenever significant P‐wave splitting can be observed. Plain Language Summary: Loose sediments on the ocean floor strongly affect how seismic waves travel from the earthquake hypocenter to the surface. The most important effect is that sediments slow down the seismic waves, causing them to arrive later at the seismic instruments. These delays can make it difficult to accurately locate the source of earthquakes. This study proposes a new method called sediment corrections. We used the distributed acoustic sensing technology to track delays caused by sediment along a telecommunication cable offshore central Chile, located in a very active subduction zone. Using a simple physical model of how seismic waves travel through sediments, we recovered the main structural features of the sediments beneath the cable. Using the properties of the sediments, we then improved the localization of the earthquake sources. Importantly, this method has the potential to be applied in different sedimentary environments, reducing the challenges associated with interpreting data collected in such geological areas. Key Points: We propose a novel inversion method to account for the presence of shallow sedimentary layers in hypocenter localizationThe inferred sediment properties explain the travel time discrepancies compared to the regional velocity modelThe sediment correction method improves hypocenter localization efforts with a small number of free parameters [ABSTRACT FROM AUTHOR]

The straddle-type monorail is an electric-powered public vehicle widely known for its versatility and ease of maintenance. The finger-band is a critical connecting structure for the straddle-type monorail, but issues such as loose bolts are inevitable over time. Manual inspection is the primary method for detecting bolt looseness in the finger-band, but this approach could be more efficient and resistant to missed detections. In this study, we conducted a straddle-type monorail finger-band-anomaly-monitoring experiment using Distributed Acoustic Sensing (DAS), a distributed multi-point-monitoring system widely used in railway monitoring. We analyzed track vibration signals' time-domain and frequency-domain characteristics under different monorail operating conditions. Our findings revealed the following: 1. DAS can effectively identify the monorail's operating status, including travel direction, starting and braking, and real-time train speed measurement. 2. Time-domain signals can accurately pinpoint special track structures such as turnouts and finger-bands. Passing trains over finger-bands also results in notable energy reflections in the frequency domain. 3. After the finger-band bolts loosen, there is a significant increase in vibration energy at the finger-band position, with the degree of energy increase corresponding to the extent of loosening. [ABSTRACT FROM AUTHOR]

Distributed Acoustic Sensing (DAS), widely adopted in hydraulic fracturing monitoring, continuously measures sound from perforation holes due to fluid flow through the perforation holes during fracturing treatment. DAS has the potential to monitor perforation Tulsa, OK 74136erosion, a phenomenon of increasing perforation size due to sand (referred to as proppant) injection during treatment. Because the sound generated by fluid flow at a perforation hole is negatively related to the perforation diameter, by detecting the decay of the DAS signal, the perforation erosion level can be estimated, which is critical information for fracture design. We used a Computation Fluid Dynamics (CFD) acoustic simulator to calculate the acoustic pressure induced by turbulence inside a wellbore and investigated the relationship between the acoustic response from fluid flow through a perforation and the perforation size by running the simulator for various perforation diameters and flow rates. The results show that if the perforation size is constant, the plot between the calculated sound pressure level and the logarithm of flow rate follows a straight line relationship. However, with different perforation sizes, the intercept of the linear relationship changes, reducing the sound pressure level. Lighthill's power law indicates that the change in intercept corresponds to the logarithm of the ratio of the increased diameter to the original diameter. The reduction in sound pressure level observed in the CFD simulation correlates with the reduction in the DAS signal in field data. The findings of this study help to evaluate perforation diameter growth using DAS and interpret fluid distribution in fracture stimulation. [ABSTRACT FROM AUTHOR]

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1–110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system's performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets. [ABSTRACT FROM AUTHOR]

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In the nearshore area, ocean current display intricate complexities due to interactions among tide, river, and coastline, which makes accurate current modeling challenging. Continuous in situ observation with high spatial and temporal resolution helps to better understand the dynamics of these currents. In this study, we used a 10‐km long submarine fiber‐optic cable with distributed acoustic sensing technology to record seismic signals associated with ocean waves. The current velocity and water depth were obtained from the velocity dispersion using frequency‐wave number analysis matched against theoretical ocean wave propagation equations. The results show remarkable agreement with observation of a nearby current meter, confirming the dominance of tidal currents as well as a small‐scale residual current. The temporal variation of water depth is consistent with observation by a nearby tidal gauge. This study demonstrates the potential of using submarine fiber‐optic cable for long‐term, high‐resolution, near real‐time nearshore current monitoring. Plain Language Summary: The ocean currents in the nearshore area are complicated due to coastlines, seabed topography, and other factors. The complexity makes it crucial to monitor high‐resolution currents. However, it is challenging to observe them in situ with high temporal and spatial resolution over long periods using conventional methods. The novel distributed acoustic sensing technology can turn submarine fiber‐optic cables into high‐density vibration sensors. This allows us to sense nearshore ocean wave pressure loading and obtain the propagation speed of ocean waves. Ocean waves propagate faster in the direction of the current than in the opposite direction. Therefore, the current velocity can be obtained by measuring the difference of propagation speed of ocean waves in different directions along the fiber‐optic cable. In this study, we used a 10‐km long submarine fiber‐optic cable in the Yellow River Delta to conduct submarine seismic observations. We obtained the current velocity with a temporal resolution of 5 min during a 25‐day period. The reliability and strengths of our method were verified by comparison with other observations. This study demonstrates that integrating our approach with submarine fiber‐optic cables can offer an innovative approach for high‐resolution ocean current monitoring in the nearshore area. Key Points: We obtained the velocity of landward and oceanward ocean waves from in situ data recorded by distributed acoustic sensing on a submarine fiber‐optic cableWe propose a near real‐time method to monitor current and water depth for the asymmetry of ocean wave propagationThe obtained tidal current agrees with a nearby observation and the residual current pattern reveals a small‐scale eddy [ABSTRACT FROM AUTHOR]

Over the past decade, distributed acoustic sensing has been utilized for structural health monitoring in various applications, owing to its continuous measurement capability in both time and space and its ability to deliver extensive data on the conditions of large structures using just a single optical cable. This work aims to evaluate the performance of distributed acoustic sensing for monitoring a multilayer structure on a laboratory scale. The proposed structure comprises four layers: a medium-density fiberboard and three rigid polyurethane foam slabs. Three different damages were emulated in the structure: two in the first layer of rigid polyurethane foam and another in the medium-density fiberboard layer. The results include the detection of the mechanical wave, comparing the response with point sensors used for reference, and evaluating how the measured signal behaves in time and frequency in the face of different damages in the multilayer structure. The tests demonstrate that evaluating signals in both time and frequency domains presents different characteristics for each condition analyzed. The supervised support vector machine classifier was used to automate the classification of these damages, achieving an accuracy of 93%. The combination of distributed acoustic sensing with this learning algorithm creates the condition for developing a smart tool for monitoring multilayer structures. [ABSTRACT FROM AUTHOR]

Near-surface seismic structure, particularly the shear wave velocity (Vs), can strongly affect local site response, and should be accurately estimated for ground motion prediction during seismic hazard assessment. The Imperial Valley (California), occupying the southern end of the Salton Trough, is a seismically active basin with thick surficial lacustrine sedimentary deposits. In this study, we utilize ambient noise records and local earthquake events for high-resolution near-surface characterization and site effect estimation with an unlit fiber-optic telecommunication infrastructure (dark fiber) in Imperial Valley by using the distributed acoustic sensing (DAS) technique. We apply ambient noise interferometry to retrieve coherent surface waves from DAS records, and evaluate performances of three different surface wave methods on DAS ambient noise dispersion imaging. We develop a quality control workflow to improve the dispersion measurement of noisy portions of the DAS data set by using a data selection strategy. Using the joint inversion of both the fundamental mode and higher overtones of Rayleigh waves, a high resolution two-dimensional (2D) Vs structure down to 70 m depth is obtained. We successfully achieve an improved Vs30 (the time-averaged shear-wave velocity in the top 30 m) model with higher spatial-resolution and reliability compared to the existing community model for the area. We also explore the potential for utilizing DAS earthquake events for site amplification estimation. The preliminary results reveal a clear anti-correlation between the approximated site response and the Vs30 profile. Our results indicate the potential utility of DAS deployed on dark fiber for near-surface characterization in appropriate contexts.

Distributed acoustic sensing (DAS) technology enables the detection of waves generated by seismic events, generally as uniaxial strain/strain rate time-series observed for dense, subsequent, portions of a Fibre Optic Cable (FOC). Despite the advantages in measurement density, data quality is often affected by uniaxial signal polarization, site effects and cable coupling, beyond the physical energy decay with distance. To better understand the relative importance of these factors for data inversion, we attempt a first modelling of noise patterns affecting DAS arrival times for a set of seismic events. The focus is on assessing the impact of noise statistics, together with the geometry of the problem, on epicentral location uncertainties. For this goal, we consider 15 'real-world' cases of DAS arrays with different geometry, each associated with a seismic event of known location. We compute synthetic P -wave arrival times and contaminate them with four statistical distributions of the noise. We also estimate P -wave arrival times on real waveforms using a standard seismological picker. Eventually, these five data sets are inverted using a Markov chain Monte Carlo method, which offers the evaluation of the relative event location differences in terms of posterior probability density (PPD). Results highlight how cable geometry influences the shape, extent and directionality of the PPDs. However, synthetic tests demonstrate how noise assumptions on arrival times often have important effects on location uncertainties. Moreover, for half of the analysed case studies, the observed and synthetic locations are more similar when considering noise sources that are independent of the geometrical characteristics of the arrays. Thus, the results indicate that axial polarization, site conditions and cable coupling, beyond other intrinsic features (e.g. optical noise), are likely responsible for the complex distribution of DAS arrival times. Overall, the noise sensitivity of DAS suggests caution when applying geometry-only-based approaches for the a priori evaluation of novel monitoring systems. [ABSTRACT FROM AUTHOR]

This paper delivers an in-depth bibliometric analysis of distributed acoustic sensing (DAS) research within the realm of geophysics, covering the period from 2012 to 2023 and drawing on data from the Web of Science. By employing bibliographic and structured network analysis methods, including the use of Bibliometrix and VOSviewer®, the study highlights the most influential scholars, leading institutions, and pivotal research contributions that have significantly shaped the field of DAS in geophysics. The research delves into key collaborative dynamics, unraveling them through co-authorship network analysis, and delves into thematic developments and trajectories via comprehensive co-citation and keyword co-occurrence network analyses. These analyses elucidate the most robust and prominent areas within DAS research. A critical insight gained from this study is the rise of 'photonic seismology' as an emerging interdisciplinary domain, exemplifying the fusion of photonic sensing techniques with seismic science. This paper also discusses certain limitations inherent in the study and concludes with implications for future research. [ABSTRACT FROM AUTHOR]

As an emerging seismic acquisition technology, distributed acoustic sensing (DAS) has drawn significant attention in earth science for long-term and cost-effective monitoring of underground activities. Field seismic experiments with optical fibers in a vertical seismic profile (VSP) configuration were conducted at the Newell County Facility of Carbon Management Canada in Alberta, Canada, for CO 2 injection and storage monitoring. Seismic full-waveform inversion (FWI) represents one promising approach for high-resolution imaging of subsurface model properties. In this study, anisotropic FWI with variable density is applied to the DAS-recorded walk-away VSP data for characterizing the subsurface velocity, anisotropy, and density structures, serving as baseline models for future time-lapse studies at the pilot site. Synthetic inversion experiments suggest that, without accounting for anisotropy, the inverted density structures by isotropic FWI are damaged by strong trade-off artifacts. Anisotropic FWI can provide more accurate P-wave velocity, density, and valuable anisotropy models. Field data applications are then performed to validate the effectiveness and superiority of the proposed methods. Compared to the inversion outputs of isotropic FWI, the inverted P-wave velocity by anisotropic FWI matches trend variation of the well log more closely. In the inverted density model, the CO 2 injection formation can be clearly resolved. The inverted anisotropy parameters provide informative references to interpret the structures and lithology around the target CO 2 injection zone. [ABSTRACT FROM AUTHOR]

The array‐based frequency‐Bessel transform method has been demonstrated to effectively extract dispersion curves of higher‐mode surface waves from the empirical Green's functions (EGFs) of displacement fields reconstructed by ambient noise interferometry. Distributed acoustic sensing (DAS), a novel dense array observation technique, has been widely implemented in surface wave imaging to estimate subsurface velocity structure in practice. However, there is still no clear understanding in theory about how to accurately extract surface‐wave dispersion curves directly from DAS strain (or strain rate) data. To address this, we extend the frequency‐Bessel transform method by deriving Green's functions (GFs) for horizontal strain fields, making it applicable to DAS data. First, we test its performance using synthetic GFs and verify the correctness of extracted dispersion spectrograms with theoretical results. Then, we apply it to three field DAS ambient‐noise data sets, two recorded on land and one in the seabed. The reliability and advantages of the method are confirmed by comparing results with the widely used phase shift method. The results demonstrate that our extended frequency‐Bessel transform method is reliable and can provide more abundant and higher‐quality dispersion information of surface waves. Moreover, our method is also adaptable for active‐source DAS data with simple modifications to the derived transform formulas. We also find that the gauge length in the DAS system significantly impacts the polarity and value of extracted dispersion energy. Overall, our study provides a theoretical framework and practical tool for multimodal surface wave dispersion measurement using DAS data. Plain Language Summary: Ambient noise surface wave imaging is one of the most widely used methods for estimating the Earth's internal shear wave velocity structure, exploiting the dispersion characteristics of surface waves. The array‐based frequency‐Bessel transform method has been proven effective in extracting dispersion curves of higher‐mode surface waves from empirical Green's functions (EGFs) retrieved via ambient noise interferometry. Distributed acoustic sensing (DAS), which is a novel dense array observation technique, has become widely adopted in practical surface wave imaging. Nevertheless, there remains a theoretical gap in our understanding of how to accurately extract surface‐wave dispersion curves directly from DAS strain (or strain rate) data. To bridge this gap, starting from the perspective of strain field theory, we propose an extension of the frequency‐Bessel transform method, which can account for Green's functions of horizontal strain fields and the reconstructed EGFs from DAS ambient noise data. Both synthetic tests and applications to field DAS data demonstrate that our proposed frequency‐Bessel transform method can be confidently and effectively utilized for multimodal dispersion measurement of surface waves derived from DAS observation data. This work can offer a theoretical basis and practical tool for DAS‐based surface wave imaging. Key Points: We derive Green's functions for horizontal strain fields, extending the frequency‐Bessel transform method for applications in distributed acoustic sensing (DAS) dataThe extended frequency‐Bessel transform method reliably measures multimodal surface‐wave dispersion in both synthetic and field DAS recordsThe method excels in extracting high‐quality multimodal dispersion information from both active and passive DAS data for surface waves [ABSTRACT FROM AUTHOR]

SUMMARY: The determination of seismic event locations with sparse networks or single-borehole systems remains a significant challenge in observational seismology. Leveraging the advantages of the location approach HADES (eartHquake locAtion via Distance gEometry Solvers), which was initially developed for locating clustered seismicity recorded at two stations, through the solution of a Distance Geometry Problem, we present here an improved version of the methodology: HADES-R (HADES-Relative). Where HADES previously needed a minimum of four absolutely located master events, HADES-R solves a least-squares problem to find the relative inter-event distances in the cluster, and uses only a single master event to find the locations of all events and subsequently applies rotational optimizer to find the cluster orientation. It can leverage iterative station combinations if multiple receivers are available, to describe the cluster shape and orientation uncertainty with a bootstrap approach. The improved method requires P- and S-phase arrival picks, a homogeneous velocity model, a single master event with a known location, and an estimate of the cluster width. The approach is benchmarked on the 2019 Ridgecrest sequence recorded at two stations, and applied to two seismic clusters at the FORGE geothermal test site in Utah, USA, with a microseismic monitoring scenario with a Distributed Acoustic Sensing in a vertical borehole. Traditional procedures struggle in these settings due to the ill-posed network configuration. The azimuthal ambiguity in such a scenario is partially overcome by the assumption that all events belong to the same cluster around the master event and a cluster width estimate. We are able to find the cluster shape in both cases, although the orientation remains uncertain. HADES-R contributes to an efficient way to locate multiple events simultaneously with minimal prior information. The method's ability to constrain the cluster shape and location with only one well-located event offers promising implications, especially for environments where limited or specialized instrumentation is in use. [ABSTRACT FROM AUTHOR]

A 2D walkway vertical seismic profile (VSP) survey was conducted using a distributed acoustic sensing (DAS) system in southwest Utah, which is part of an enhanced geothermal system (EGS) project. The VSP was undertaken to obtain detailed structural information for a better understanding of the area's subsurface geology and associated fracture development. By combining a 3D composite velocity model from previous studies and considering the complex geological structure beneath this region, we processed the data to create P-P depth image. We also modified the interval Q calculation using a moving window over the gauge-length corrected DAS record to generate the velocity profile and the comparable interval attenuation curve. The correlated P-P images from two DAS records successfully indicate not only the main contact between shallow unconsolidated sediments and the metamorphic basement rocks at 2650 ft (807.72 m) but also several distinct reflections related to the geological contacts. The refined velocity profiles and the depth images can provide baseline results for further seismic modeling and time-lapse imaging. [ABSTRACT FROM AUTHOR]

The structure of fault zones and the ruptures they host are inextricably linked. Fault zones are narrow, which has made imaging their structure at seismogenic depths a persistent problem. Fiber‐optic seismology allows for low‐maintenance, long‐term deployments of dense seismic arrays, which present new opportunities to address this problem. We use a fiber array that crosses the Garlock Fault to explore its structure. With a multifaceted imaging approach, we peel back the shallow structure around the fault to see how the fault changes with depth in the crust. We first generate a shallow velocity model across the fault with a joint inversion of active source and ambient noise data. Subsequently, we investigate the fault at deeper depths using travel‐time observations from local earthquakes. By comparing the shallow velocity model and the earthquake travel‐time observations, we find that the fault's low‐velocity zone below the top few hundred meters is at most unexpectedly narrow, potentially indicating fault zone healing. Using differential travel‐time measurements from earthquake pairs, we resolve a sharp bimaterial contrast at depth that suggests preferred westward rupture directivity. Plain Language Summary: Fault zone structure is important because it influences the physics of earthquake ruptures. Imaging fault zones at depth, where large earthquakes typically happen, is challenging because fault zones are narrow and seismic imaging resolution degrades with depth. Dense seismic arrays deployed across faults can help resolve important properties of fault zones at depth. Fiber optic seismology allows for the deployment of dense arrays across faults for long periods of time with low logistical burden. We use a fiber optic array that crosses the Garlock Fault to explore important characteristics of the fault zone at different depths. We find that there is no extensive low velocity feature at depth, potentially suggesting healing of the fault damage zone. Additionally, when we remove the contribution of the complicated velocity structure of the shallow crust, we recover a sharp velocity contrast across the fault which may have implications for the propagation behavior of future ruptures. Key Points: We use a distributed acoustic sensing array that crosses the Garlock Fault to investigate its structureWe find that the low velocity zone around the fault is mostly shallow, suggesting the damage zone at depth is at most narrowWe find a clear bimaterial contrast at depth, which was hidden by the shallow crust, that suggests a preferred westward directivity [ABSTRACT FROM AUTHOR]

Shallow sediments can respond non‐linearly to large dynamic strains and undergo a subsequent healing phase as the material gradually recovers following the passing of seismic waves. This study focuses on the physical changes in the subsurface caused by the shaking from a buried chemical explosion detonated in a borehole in Nevada, USA, as a part of the Source Physics Experiment Phase II. The explosion damaged the shallow subsurface and modified the frequency content recorded by 491 geophones and 2240 Distributed Acoustic Sensing (DAS) channels within 2.5 km from surface ground zero. We observe a gradual shift of resonance frequencies in the 10–25 Hz frequency band in the hours following the explosion and develop a method to characterize the related logarithm‐type healing process of the shallow (i.e., upper ∼25 m) subsurface. We find that stronger levels of ground motion increase the relative degree of damage and duration of the subsurface healing; with the spall region exhibiting the largest degree of damage and longest healing recovery time. We observe coherent spatial patterns of damage with the region located to the southeast of the explosion exhibiting more damage than the southwest region. This study demonstrates that both DAS and co‐located geophones capture similar temporal changes associated with the physical processes occurring in the subsurface, with the high‐density sampling of DAS measurements enabling a new capability to monitor the fine‐scale changes of the Earth's shallow subsurface following the detonation of a buried explosion. Plain Language Summary: Strong seismic waves can damage the soft sediments that compose the shallow layers of the ground. A healing phase of the sediments generally follows the passing of the seismic waves as the medium gradually recovers with time. We study the spatio‐temporal response of the subsurface in the vicinity of a large buried chemical explosion that was detonated in a borehole at the Nevada National Security Site, USA. The explosion, which was part of the Source Physics Experiment Phase II, was well instrumented along a surface fiber‐optic cable with Distributed Acoustic Sensing (DAS) and hundreds of geophones. We find that the explosion, which generated a spallation of the shallow Earth, primarily damaged the upper ∼25 m of the subsurface. We characterize the healing of the sediments and find a correlation between the duration of the healing phase and the level of maximum shaking. The high density of sensors also allows us to study spatial variations in the response of the shallow subsurface. This study demonstrates that both DAS and geophone continuous data similarly capture the spatio‐temporal variations of the Earth's physical properties following strong ground motions, with DAS enabling meter‐scale measurements of the subsurface changes. Key Points: Shallow subsurface damage and subsequent healing caused by a buried chemical explosion are constrained with DAS and geophone dataThe explosion caused a relative drop of the average S‐wave velocity in the Earth's shallow layers of a few percentsThe logarithm‐type healing process of the subsurface exhibit a longer duration within the spall region [ABSTRACT FROM AUTHOR]

Railway track bed defects affect the normal operation of trains and pose great safety risks. In order to detect such issues early, we developed a railway track bed defect detection method which uses optical fibre sensors and an improved HMM (hidden Markov model) to detect the signals collected by a DAS (distributed acoustic sensing) system. First, by analysing the physical process of train operation and determining the number of hidden states, a waveform segmentation method based on average amplitude was used to solve the problem of unequal signal lengths. Second, an adaptive power spectrum energy ratio calculation method was employed to extract track fault features, a set of which was constructed by combining various quantity features. Then, normal and abnormal models were trained according to the sensor measurement area. Finally, the probability of detecting the signal with each model was compared to determine whether the signal was abnormal. Experiments were conducted to compare the applicability of the waveform segmentation method and the feature extraction method. The results show that the HMM based on both waveform segmentation and track bed defect feature sets had the highest recognition rate, the lowest number of false detection areas, and a greater impact on the signal in the early development stage of track bed defects. The proposed method, therefore, has strong recognition ability, which makes it suitable for track bed defect detection. [ABSTRACT FROM AUTHOR]

This article discusses the use of distributed acoustic sensing (DAS) for monitoring gas–liquid two-phase slug flow in horizontal pipes, using standard telecommunication fiber optics connected to a DAS integrator for data acquisition. The experiments were performed in a 14 m long, 5 cm diameter transparent PVC pipe with a fiber cable helically wrapped around the pipe. Using mineral oil and compressed air, the system captured various flow rates and gas–oil ratios. New algorithms were developed to characterize slug flow using DAS data, including slug frequency, translational velocity, and the lengths of slug body, slug unit, and the liquid film region that had never been discussed previously. This study employed a high-speed camera next to the fiber cable sensing section for validation purposes and achieved a good correlation among the measurements under all conditions tested. Compared to traditional multiphase flow sensors, this technology is non-intrusive and offers continuous, real-time measurement across long distances and in harsh environments, such as subsurface or downhole conditions. It is cost-effective, particularly where multiple measurement points are required. Characterizing slug flow in real time is crucial to many industries that suffer slug-flow-related issues. This research demonstrated the DAS's potential to characterize slug flow quantitively. It will offer the industry a more optimal solution for facility design and operation and ensure safer operational practices. [ABSTRACT FROM AUTHOR]

Distributed Acoustic Sensing (DAS) technology repurposes fiber optic cables (FOCs) into seismic arrays, offering unprecedented dense strain/strain-rate measurements. The metre-scale virtual sensor spacing is typically unattainable with standard seismological equipment. Consequently, DAS provides an extraordinary amount of suitable data for seismic monitoring applications. However, intrinsic characteristics of this technology, such as signal axial polarisation, coupling inhomogeneities, or sensitivity to site conditions, can affect seismic phase amplitudes and their coherence, potentially reducing the number of useful measurement points. To gain a deeper understanding on the relative importance of these phenomena, this study analyses 'real data' from various seismic events recorded by shallow-horizontal DAS deployments. Thus, we take advantage of the pool of different array dimensions and geometries to avoid biased observations. We focus on the spatial variability of P-wave amplitudes, signal-to-noise ratios and waveform correlation, ideally mimicking the usage of absolute and differential arrival times for seismological monitoring purposes. We observed significant amplitude variations, which cannot be fully explained by signal polarisation along the FOC. Additionally, waveform correlation often exhibits a complex and faster decay with interchannel distance. These findings suggest the importance of avoiding 'blind' usage of shallow-horizontal DAS arrays and emphasise the need for case-dependent data selection/weighting procedures. [ABSTRACT FROM AUTHOR]

Ambient noise tomography on the basis of distributed acoustic sensing (DAS) deployed on existing telecommunication networks provides an opportunity to image the urban subsurface at regional scales and high-resolution. This capability has important implications in the assessment of the urban subsurface's potential for sustainable and safe utilization, such as geothermal development. However, extracting coherent seismic signals from the DAS ambient wavefield in urban environments at low cost remains a challenge. One obstacle is the presence of complex sources of noise in urban environments, which may not be homogeneously distributed. Consequently, long recordings are required for the calculation of high-quality virtual shot gathers, which necessitates significant time and computational cost. In this paper, we present the analysis of 15 d of DAS data recorded on a pre-existing fibre optic cable (dark fibres), running along an 11-km-long major road in urban Berlin (Germany), hosting heavy traffic including vehicles and trains. To retrieve virtual shot gathers, we apply interferometric analysis based on the cross-correlation approach where we exclude low-quality virtual shot gathers to increase the signal-to-noise ratio of the stacked gathers. Moreover, we modify the conventional ambient noise interferometry workflow by incorporating a coherence-based enhancement approach designed for wavefield data recorded with large-N arrays. We then conduct multichannel analysis of surface waves to retrieve 1-D velocity models for two exemplary fibre subsegments, and compare the results of the conventional and modified workflows. The resulting 1-D velocity models correspond well with available lithology information. The modified workflow yields improved dispersion spectra, particularly in the low-frequency band (<1 Hz) of the signal. This leads to an increased investigation depth along with lower uncertainties in the inversion result. Additionally, these improved results were achieved using significantly less data than required using conventional approaches, thus opening the opportunity for shortening required acquisition times and accordingly lowering costs. [ABSTRACT FROM AUTHOR]

Abstract Effective use of the wealth of information provided by Distributed Acoustic Sensing (DAS) for mass movement monitoring remains a challenge. We propose a semi‐supervised neural network tailored to screen DAS data related to a series of rock collapses leading to a major failure of approximately 1.2 million m3 on 15 June 2023 in Brienz, Eastern Switzerland. Besides DAS, the dataset from 16 May to 30 June 2023 includes Doppler radar data for partially ground‐truth labeling. The proposed algorithm is capable of distinguishing between rock‐slope failures and background noise, including road and train traffic, with a detection precision of over 95%. It identifies hundreds of precursory failures and shows sustained detection hours before and during the major collapse. Event size and signal‐to‐noise ratio (SNR) are the key performance dependencies. As a critical part of our algorithm operates unsupervised, we suggest that it is suitable for general monitoring of natural hazards.

Abstract In the nearshore area, ocean current display intricate complexities due to interactions among tide, river, and coastline, which makes accurate current modeling challenging. Continuous in situ observation with high spatial and temporal resolution helps to better understand the dynamics of these currents. In this study, we used a 10‐km long submarine fiber‐optic cable with distributed acoustic sensing technology to record seismic signals associated with ocean waves. The current velocity and water depth were obtained from the velocity dispersion using frequency‐wave number analysis matched against theoretical ocean wave propagation equations. The results show remarkable agreement with observation of a nearby current meter, confirming the dominance of tidal currents as well as a small‐scale residual current. The temporal variation of water depth is consistent with observation by a nearby tidal gauge. This study demonstrates the potential of using submarine fiber‐optic cable for long‐term, high‐resolution, near real‐time nearshore current monitoring.

The Imperial Valley, CA, is a tectonically active transtensional basin located south of the Salton Sea; the area hosts numerous geothermal fields, including significant hidden hydrothermal resources without surface manifestations. Development of inexpensive, rugged, and highly sensitive exploration techniques for undiscovered geothermal systems is critical for accelerating geothermal power deployment as well as unlocking a low-carbon energy future. We present a case study utilizing distributed acoustic sensing (DAS) and ambient noise interferometry for geothermal reservoir imaging, utilizing unlit fiber-optic telecommunication infrastructure (dark fiber). The study exploits two days of passive DAS data acquired in early November 2020 over a ∼28-km section of fiber from Calipatria, CA to Imperial, CA. We apply ambient noise interferometry to retrieve coherent signals from DAS records and develop a bin stacking technique to attenuate the effects from persistent localized noise sources and to enhance retrieval of coherent surface waves. As a result, we are able to obtain high-resolution two-dimensional (2D) S wave velocity (Vs) structure to 3 km depth, based on joint inversion of both the fundamental and higher overtones. We observe a previously unmapped high Vs and low Vp/Vs ratio feature beneath the Brawley geothermal system, which we interpret to be a zone of hydrothermal mineralization and lower porosity. This interpretation is consistent with a host of other measurements including surface heat flow, gravity anomalies, and available borehole wireline data. These results demonstrate the potential utility of DAS deployed on dark fiber for geothermal system exploration and characterization in the appropriate geological settings.

Geothermal energy exploitation in urban areas necessitates robust real-time seismic monitoring for risk mitigation. While surface-based seismic networks are valuable, they are sensitive to anthropogenic noise. This study investigates the capabilities of borehole Distributed Acoustic Sensing (DAS) for local seismic monitoring of a geothermal field located in Munich, Germany. We leverage the operator's cloud infrastructure for DAS data management and processing. We introduce a comprehensive workflow for the automated processing of DAS data, including seismic event detection, onset time picking, and event characterization. The latter includes the determination of the event hypocenter, origin time, seismic moment, and stress drop. Waveform-based parameters are obtained after the automatic conversion of the DAS strain-rate to acceleration. We present the results of a 6-month monitoring period that demonstrates the capabilities of the proposed monitoring set-up, from the management of DAS data volumes to the establishment of an event catalog. The comparison of the results with seismometer data shows that the phase and amplitude of DAS data can be reliably used for seismic processing. This emphasizes the potential of improving seismic monitoring capabilities with hybrid networks, combining surface and downhole seismometers with borehole DAS. The inherent high-density array configuration of borehole DAS proves particularly advantageous in urban and operational environments. This study stresses that realistic prior knowledge of the seismic velocity model remains essential to prevent a large number of DAS sensing points from biasing results and interpretation. This study suggests the potential for a gradual extension of the network as geothermal exploitation progresses and new wells are equipped, owing to the scalability of the described monitoring system. [ABSTRACT FROM AUTHOR]

A 3D vertical seismic profiling (VSP) survey was acquired using a distributed acoustic sensing (DAS) system in the Permian Basin, West Texas. In total, 682 shot points from a pair of vibroseis units were recorded using optical fibers installed in a 9000 ft (2743 m) vertical part and 5000 ft (1524 m) horizontal reach of a well. Transmitted and reflected P, S, and converted waves were evident in the DAS data. From first-break P and S arrivals, we found average P-wave velocities of approximately 14,000 ft/s (4570 m/s) and S-wave velocities of 8800 ft/s (3000 m/s) in the deep section. We modified the conventional geophone VSP processing workflow and produced P–P reflection and P–S volumes derived from the well's vertical section. The Wolfcamp formation can be seen in two 3D volumes (P–P and P–S) from the vertical section of the well. They cover an area of 3000 ft (914 m) in the north–south direction and 1500 ft (460 m) in the west–east direction. Time slices showed coherent reflections, especially at 1.7 s (~11,000 ft), which was interpreted as the bottom of the Wolfcamp formation. Vp/Vs values from 2300 ft (701 m) –8800 ft (2682 m) interval range were between 1.7 and 2.0. These first data provide baseline images to compare to follow-up surveys after hydraulic fracturing as well as potential usefulness in extracting elastic properties and providing further indications of fractured volumes. [ABSTRACT FROM AUTHOR]

In distributed acoustic sensing (DAS), optical fibre is used as sensors, which enables us to observe strain over tens of kilometres at intervals of several metres. S -wave velocity (V s) structures of shallow sediments of high resolution have been obtained from surface wave dispersion curves by applying seismic interferometry to DAS data both onshore and offshore. However, it is known that there is a disadvantage to DAS seismic interferometry. In addition to Rayleigh waves, Love waves are also included. Consequently, the accuracy of the estimated phase velocities for Rayleigh waves is reduced due to the contamination of Love waves. To address this shortcoming, we suggest a spatial autocorrelation (SPAC) method between DAS and the vertical component of seismometer data. The SPAC method is equivalent to seismic interferometry and is useful for obtaining phase-velocity dispersion curves of surface waves from the cross-correlation functions (CCFs) between the records of two receivers. The CCFs obtained from a combination of DAS and vertical seismometer data should contain only Rayleigh waves because Love waves have no vertical component. CCFs between DAS and vertical seismometer data are therefore expected to give more accurate phase velocities of Rayleigh waves than CCFs with DAS data only. In this study, we first formulated analytical expressions of cross-spectra for DAS and three-component seismometer data because seismic observation is generally carried out using a three-component seismometer. A new SPAC method is presented in the form of analytical expressions. We showed that our formulation only includes Rayleigh and not Love waves in the cross-spectra with DAS and the vertical-component seismometer data. We applied our SPAC method to actual DAS and vertical seismometer data recorded on the seafloor. Then, we compared our new SPAC method for DAS and vertical seismometer data with a conventional SPAC method for only DAS data. The results reveal that our new SPAC method can estimate the phase velocities of Rayleigh waves more accurately than the conventional method. In addition, the analytical formulations of the cross-spectrum between DAS and three-component seismometer data, which we obtained in this study, are expected to be useful for the estimation of accurate 3-D structures in the future, although this is not available at the moment due to the lack of an applicable data set. [ABSTRACT FROM AUTHOR]

Combining elastic full waveform inversion (FWI) with rock physics holds promise for expanding the application of FWI beyond seismic imaging to predicting and monitoring reservoir properties. Distributed acoustic sensing (DAS), a rapidly developing seismic acquisition technology, is being explored for its potential in supporting FWI applications. In this study, we implement a sequential inversion scheme that integrates elastic FWI and Bayesian rock physics inversion, using a vertical seismic profile (VSP) data set acquired with accelerometer and collocated DAS fibre at the Carbon Management Canada's Newell County Facility. Our aim is to establish a baseline model of porosity and lithology parameters to support later monitoring of CO2 storage. Key strategies include an effective source approach for addressing near-surface complications, a modelling strategy to simulate DAS data comparable to field data, and a Gaussian mixture approach to capture the bimodality of rock properties. We conduct FWI tests on accelerometer, DAS, and combined accelerometer-DAS data. While our inversion results accurately reproduce either data set, the elastic models inverted from accelerometer data outperform the other two in matching well logs and identifying the target reservoir. We attribute this outcome to the limited complementarity of DAS data with accelerometer data in our experiment, along with the limitations imposed by single-component measurements on DAS. The porosity and lithology models predicted from accelerometer-derived elastic models are reasonably accurate at the well location and exhibit geologically meaningful spatial distribution. [ABSTRACT FROM AUTHOR]

Vehicle‐induced seismic waves, generated as vehicles traverse the ground surface, carry valuable information for imaging the underlying near‐surface structure. These waves propagate differently in the subsurface depending on soil properties at various spatial locations. By leveraging wave propagation characteristics, such as surface‐wave velocity and attenuation, this study presents a novel method for near‐surface monitoring. Our method employs passing vehicles as active, non‐dedicated seismic sources and leverages pre‐existing telecommunication fibers as large‐scale and cost‐effective roadside sensors empowered by Distributed Acoustic Sensing (DAS) technology. A specialized Kalman filter algorithm is integrated for automated DAS‐based traffic monitoring to accurately determine vehicles' location and speed. Then, our approach uniquely leverages vehicle trajectories to isolate space‐time windows containing high‐quality surface waves. With known vehicle (i.e., seismic source) locations, we can effectively mitigate artifacts associated with suboptimal distribution of sources in conventional ambient noise interferometry. Compared to ambient noise interferometry, our approach enables the synthesis of virtual shot gathers with a high signal‐to‐noise ratio and spatiotemporal resolution at reduced computational costs. We validate the effectiveness of our method using the Stanford DAS‐2 array, with a focus on capturing spatial heterogeneity and monitoring temporal variations in soil seismic properties during rainfall events. Specifically, in non‐built‐up areas, we observed an evident decrease in phase velocity and group velocity and an increase in attenuation due to the rainfall. Our findings illustrate our method's sensitivity and resolution in discerning variations across different spatial locations and demonstrate that our method is a promising advancement for high‐resolution near‐surface imaging in urban settings. Plain Language Summary: Continuous monitoring of near‐surface soil properties is important to various applications, including identifying subsidence and monitoring groundwater levels. Vehicles transiting in city streets excite seismic waves propagating in the subsurface, which can be analyzed to gain insights into the structures beneath the surface. In this study, we develop a novel near‐surface monitoring method utilizing traffic recordings of Distributed Acoustic Sensing (DAS) technology, transforming existing telecommunication cables into extensive ground motion sensors. To harness the vibrations generated by vehicles, our method employs a specialized algorithm, determining vehicle locations and speeds based on the ground deformation they induce. The identified vehicle location is precisely utilized to select specific traffic‐induced seismic waves to explore the subsurface. Our approach allows for the acquisition of clearer and more precise insights into underground structures in urban areas and is more computationally efficient than traditional methods based on ambient noises. Tested at Stanford University in 2023, our method adeptly identified variations in the seismic properties of the subsurface, particularly in non‐built‐up areas, during rainfall events. Such accurate detection is valuable for water resource management and urban development, enabling enhanced understanding and management of urban subsurface environments. Key Points: We utilize a large number of vehicles transiting on city streets as non‐dedicated seismic sources for continuous near‐surface monitoringOur method, tailored for environments with urban and suburban traffic, outperforms conventional interferometry in efficiency and resolution, leveraging selective cross‐correlation of surface wavesWe observed rain‐induced changes in seismic properties using time‐lapse analysis of kinematics and attenuation [ABSTRACT FROM AUTHOR]

In the immediate vicinity of a source, there are strong gradients in the seismic wavefield that are tamed and modified in distributed acoustic sensing (DAS) recording due to combined effects of gauge-length averaging and local stacking on the local strain field. Close to a source broadside propagation effects are significant, and produce a characteristic impact on the local DAS channels. In the presence of topography, of surface or cable, additional effects are introduced that modify the expected signal. All these influences mean that the results of tap tests used to calibrate the channel positions along a DAS cable may give a distorted view of the actual geometry. Such effects can be important for detailed mapping of faulting processes and comparable features. [ABSTRACT FROM AUTHOR]

Underwater pipelines are exposed to harsh environments, including high salinity, multi-modal vortex corrosion, and severe wave interference. Their safety is essential for the development and transportation of marine energy. Therefore, real-time safety monitoring of long-distance energy pipelines is of great strategic importance for ensuring the safety of life and property and energy security. With the rapid development of energy development, the corrosion and leakage mechanisms of natural gas pipelines, as well as their identification and early warning, have become the focus of attention. Optical fiber sensing technology has been applied to various energy safety monitoring fields. However, the mechanism of sound source fluctuations in pipeline leakage and the mutual coupling mechanism between distributed optical fiber sensing technology and leakage sound waves are not yet clear. This paper establishes a model based on sound wave propagation and leakage noise response, derives a quadratic fitting relationship between pipeline pressure fluctuations and leakage orifices and a relationship between leakage noise source standard deviation and orifices, and proposes a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) permutation entropy underwater natural gas pipeline leakage signal recognition method based on distributed optical fiber acoustic sensing technology. The results of theoretical analysis are verified by experiments. It shows that the signal processing method of CEEMDAN permutation entropy is superior to traditional noise reduction methods, which can better preserve the features of the original signal; the radial basis function (RBF) neural network model can accurately identify four different leakage features with an accuracy of 88.15%, and its recognition stability and generalization ability are superior to convolutional neural network, backpropagation, and random forest. Therefore, the research results of this paper provide a new method for safety monitoring in the application of energy pipeline transportation engineering, and expand the potential application scenarios of distributed acoustic sensing sensor systems and RBF neural networks. [ABSTRACT FROM AUTHOR]

Large earthquakes can trigger smaller seismic events, even at significant distances. The process of earthquake triggering offers valuable insights into the evolution of local stress states, deepening our understanding of the mechanisms of earthquake nucleation. However, our ability to detect these triggered events is limited by the quality and spatial density of local seismometers, posing significant challenges if the triggered event is hidden in the signal of a nearby larger earthquake. Distributed acoustic sensing (DAS) has the potential to enhance the monitoring capability of triggered earthquakes through its high spatial sampling and large spatial coverage. Here, we report on an uncatalogued magnitude (M) 5.1 event in northeast Turkey, which was likely dynamically and instantaneously triggered by the 2023 M7.8 earthquake in southeast Turkey, located 400 km away. This event was initially discovered on ∼1,100 km of active DAS recordings that are part of an 1,850‐km linear array. Subsequent validation using local seismometers confirmed the event's precise time, location, and magnitude. Interestingly, this dynamically triggered event exhibited precursory signals preceding its P arrivals on the nearby seismometers. It can be interpreted as the signal from other nearby, uncatalogued, smaller triggered events. Our results highlight the potential of high‐spatial‐density DAS in enhancing the local‐scale detection and the detailed analysis of earthquake triggering. Plain Language Summary: Large earthquakes can trigger smaller ones even far away. This helps us understand more about how earthquakes start and develop. However, finding these smaller earthquakes can be difficult as sometimes they are hidden in the chaos of a bigger, nearby earthquake. There is a novel technology called Distributed Acoustic Sensing (DAS) that might help. DAS can listen for earthquakes over large areas and gives a more detailed picture of what's happening underground. In this study, we discovered a moderate‐size earthquake with a magnitude of 5.1 in Northeast Turkey that was triggered by a large earthquake with a magnitude of 7.8 in Southeast Turkey, using a DAS system that stretched over a thousand kilometers. We then checked this finding with conventional seismometers to be sure. Interestingly, this triggered earthquake showed precursory signals before its main shaking started, which could be signs of other smaller earthquakes happening nearby. Our findings deepen our understanding of how earthquakes interact with each other and offer insights into how earthquakes start. Our results also suggest that DAS could help us find and understand these triggered earthquakes, providing us with invaluable information for future seismology studies. Key Points: We find an uncatalogued M5.1 earthquake in NE Turkey dynamically and instantaneously triggered by the 2023 Mw 7.8 SE Turkey earthquakeWe uncover this event on a thousand‐kilometer linear distributed acoustic sensing array, then confirm its precise time, location, and magnitude with seismometersPrecursory signals of the M5.1 event are observed and are most likely resulting from other nearby events rather than its nucleation phase [ABSTRACT FROM AUTHOR]

Distributed Acoustic Sensing (DAS) is an emerging sensing technology that records the strain-rate along fiber optic cables at high spatial and temporal resolution. This technique is becoming a popular tool in seismology, hydrology, and other subsurface monitoring applications. However, due to the large coverage (10’s of km) and high density of measurements (1m spacing at 100’s of Hz), a DAS installation could produce terabytes of data records per day. Because many DAS instruments are deployed in remote locations, this large data size poses significant challenges to its transfer and storage. In this paper, we explore lossless compression methods to reduce the storage requirement in both real-time and post-hoc scenarios. We propose a two-stage compression method to improve the compression ratio and compression speed. This two-stage compression method could reduce the storage requirement by 40%, which is 20% more than other lossless methods, such as ZSTD. We demonstrate that the compression method could complete its operation well before the DAS instrument needs to output the next file, making it suitable for real-time DAS acquisition. We also implement a parallel compression method for a post-hoc scenario and demonstrate that our method could effectively utilize a parallel computer. With 256 CPU cores, our parallel compression method achieves the speed of 26GB/second.

Abstract As megathrust earthquakes often have source areas in offshore regions, offshore seismic observations are important. However, the detection capability and resolution of offshore earthquake locations are low owing to the small number of permanent offshore seismic stations. Recently, distributed acoustic sensing (DAS) measurements, which use a fiber-optic cable as a high-density strain rate sensor, have been used for seismic observations. To evaluate the detectability of earthquakes using DAS measurements, locate earthquakes near the cable, and derive the empirical relationship between the magnitude and DAS S-wave strain rate amplitude, we conducted DAS measurements for 4 months using an offshore fiber-optic cable in the Tsugaru Strait, where various types of earthquakes were observed. In this observation, some earthquakes with magnitudes smaller than one or not listed in the earthquake catalog by the Japan Meteorological Agency (JMA) were observed. This suggests a high seismic detection capability for DAS measurements near the cable. We located earthquakes in the Tsugaru Strait by manually picking the arrivals of P- and S-waves. The hypocenters of events near the cable were located near those of the JMA catalog at a kilometer resolution; therefore, DAS data have the potential to locate earthquakes near the cable. In this study, an equation related to the maximum S-wave strain rate amplitude, hypocentral distance, and earthquake magnitude was derived. When the hypocentral distance increased by one order, the amplitude of the S-wave strain rate decreased by approximately 1.8 orders. This attenuation was larger than that derived mainly from inland DAS data in previous studies, which may be due to the difference in scattering or intrinsic attenuation between the inland and offshore regions. Using the derived equation, the magnitude of an earthquake can be estimated using the DAS data. We compared the S-wave amplitudes of the DAS strain rate and the acceleration of the permanent inland stations. The relationship between these two amplitudes is comparable to an apparent S-wave velocity of approximately 1500 m/s in the sediment. Graphical Abstract

Abstract The devastation caused by the January 2022 eruption of Hunga Tonga-Hunga Ha’apai volcano (HTHH) in the Tongan archipelago reminded us of the importance of monitoring shallow-sea volcanic activity. Seismic observations are essential for such monitoring, but there were no operational seismic stations in Tonga at the time of the eruption. There are only a few islands near Tongan volcanoes, and installation and maintenance of seismic stations on remote islands are expensive. Seismic observations based on distributed acoustic sensing (DAS) using a seafloor cable may provide a more practical and economical solution. To investigate the potential of this approach, we made preliminary DAS observations for 1 week using the seafloor domestic broadband telecommunications cable in Tonga. DAS equipment was installed at the landing station of the seafloor cable at Nuku’alofa on Tongatapu, the main island of Tonga. To provide reference data, we installed several seismometers on Tongatapu. The DAS data we obtained showed high noise levels in areas of shallow coral reef, but noise levels decreased greatly in deeper water areas, indicating that DAS is suitable for seismic observations of the deep seafloor. We detected many local and regional earthquakes during our week of observation and determined 17 earthquake hypocenters by picking P- and S-wave arrival times from the DAS and onshore seismic data. Although most of these were tectonic events related to the subduction of the Pacific plate along the Tonga trench, several events were detected around the volcanic chain of the Tongan archipelago including one event beneath the HTHH crater, implying that activity at HTHH has continued since the 2022 eruption. The much lower cost of installation of DAS equipment compared to that for pop-up type ocean-bottom seismometers and the ability of DAS systems to monitor seismic activity in real-time make it an attractive option for monitoring the activity of HTHH and other volcanoes near seafloor cables in the Tongan archipelago. Graphical Abstract

The straddle-type monorail is an electric-powered public vehicle widely known for its versatility and ease of maintenance. The finger-band is a critical connecting structure for the straddle-type monorail, but issues such as loose bolts are inevitable over time. Manual inspection is the primary method for detecting bolt looseness in the finger-band, but this approach could be more efficient and resistant to missed detections. In this study, we conducted a straddle-type monorail finger-band-anomaly-monitoring experiment using Distributed Acoustic Sensing (DAS), a distributed multi-point-monitoring system widely used in railway monitoring. We analyzed track vibration signals’ time-domain and frequency-domain characteristics under different monorail operating conditions. Our findings revealed the following: 1. DAS can effectively identify the monorail’s operating status, including travel direction, starting and braking, and real-time train speed measurement. 2. Time-domain signals can accurately pinpoint special track structures such as turnouts and finger-bands. Passing trains over finger-bands also results in notable energy reflections in the frequency domain. 3. After the finger-band bolts loosen, there is a significant increase in vibration energy at the finger-band position, with the degree of energy increase corresponding to the extent of loosening.

In cloud-based Distributed Acoustic Sensing (DAS) sensor data management, we are confronted with two primary challenges. First, the development of efficient storage mechanisms capable of handling the enormous volume of data generated by these sensors poses a challenge. To solve this issue, we propose a method to address the issue of handling the large amount of data involved in DAS by designing and implementing a pipeline system to efficiently send the big data to DynamoDB in order to fully use the low latency of the DynamoDB data storage system for a benchmark DAS scheme for performing continuous monitoring over a 100 km range at a meter-scale spatial resolution. We employ the DynamoDB functionality of Amazon Web Services (AWS), which allows highly expandable storage capacity with latency of access of a few tens of milliseconds. The different stages of DAS data handling are performed in a pipeline, and the scheme is optimized for high overall throughput with reduced latency suitable for concurrent, real-time event extraction as well as the minimal storage of raw and intermediate data. In addition, the scalability of the DynamoDB-based data storage scheme is evaluated for linear and nonlinear variations of number of batches of access and a wide range of data sample sizes corresponding to sensing ranges of 1–110 km. The results show latencies of 40 ms per batch of access with low standard deviations of a few milliseconds, and latency per sample decreases for increasing the sample size, paving the way toward the development of scalable, cloud-based data storage services integrating additional post-processing for more precise feature extraction. The technique greatly simplifies DAS data handling in key application areas requiring continuous, large-scale measurement schemes. In addition, the processing of raw traces in a long-distance DAS for real-time monitoring requires the careful design of computational resources to guarantee requisite dynamic performance. Now, we will focus on the design of a system for the performance evaluation of cloud computing systems for diverse computations on DAS data. This system is aimed at unveiling valuable insights into performance metrics and operational efficiencies of computations on the data in the cloud, which will provide a deeper understanding of the system’s performance, identify potential bottlenecks, and suggest areas for improvement. To achieve this, we employ the CloudSim framework. The analysis reveals that the virtual machine (VM) performance decreases significantly the processing time with more capable VMs, influenced by Processing Elements (PEs) and Million Instructions Per Second (MIPS). The results also reflect that, although a larger number of computations is required as the fiber length increases, with the subsequent increase in processing time, the overall speed of computation is still suitable for continuous real-time monitoring. We also see that VMs with lower performance in terms of processing speed and number of CPUs have more inconsistent processing times compared to those with higher performance, while not incurring significantly higher prices. Additionally, the impact of VM parameters on computation time is explored, highlighting the importance of resource optimization in the DAS system design for efficient performance. The study also observes a notable trend in processing time, showing a significant decrease for every additional 50,000 columns processed as the length of the fiber increases. This finding underscores the efficiency gains achieved with larger computational loads, indicating improved system performance and capacity utilization as the DAS system processes more extensive datasets.

Distributed Acoustic Sensing (DAS), widely adopted in hydraulic fracturing monitoring, continuously measures sound from perforation holes due to fluid flow through the perforation holes during fracturing treatment. DAS has the potential to monitor perforation Tulsa, OK 74136erosion, a phenomenon of increasing perforation size due to sand (referred to as proppant) injection during treatment. Because the sound generated by fluid flow at a perforation hole is negatively related to the perforation diameter, by detecting the decay of the DAS signal, the perforation erosion level can be estimated, which is critical information for fracture design. We used a Computation Fluid Dynamics (CFD) acoustic simulator to calculate the acoustic pressure induced by turbulence inside a wellbore and investigated the relationship between the acoustic response from fluid flow through a perforation and the perforation size by running the simulator for various perforation diameters and flow rates. The results show that if the perforation size is constant, the plot between the calculated sound pressure level and the logarithm of flow rate follows a straight line relationship. However, with different perforation sizes, the intercept of the linear relationship changes, reducing the sound pressure level. Lighthill’s power law indicates that the change in intercept corresponds to the logarithm of the ratio of the increased diameter to the original diameter. The reduction in sound pressure level observed in the CFD simulation correlates with the reduction in the DAS signal in field data. The findings of this study help to evaluate perforation diameter growth using DAS and interpret fluid distribution in fracture stimulation.

Over the past decade, distributed acoustic sensing has been utilized for structural health monitoring in various applications, owing to its continuous measurement capability in both time and space and its ability to deliver extensive data on the conditions of large structures using just a single optical cable. This work aims to evaluate the performance of distributed acoustic sensing for monitoring a multilayer structure on a laboratory scale. The proposed structure comprises four layers: a medium-density fiberboard and three rigid polyurethane foam slabs. Three different damages were emulated in the structure: two in the first layer of rigid polyurethane foam and another in the medium-density fiberboard layer. The results include the detection of the mechanical wave, comparing the response with point sensors used for reference, and evaluating how the measured signal behaves in time and frequency in the face of different damages in the multilayer structure. The tests demonstrate that evaluating signals in both time and frequency domains presents different characteristics for each condition analyzed. The supervised support vector machine classifier was used to automate the classification of these damages, achieving an accuracy of 93%. The combination of distributed acoustic sensing with this learning algorithm creates the condition for developing a smart tool for monitoring multilayer structures.

Investigating lightning is of key significance in understanding the lightning mechanism and mitigating lightning hazards. We reported an experiment of investigating lightning through three‐dimensional (3D) thunder locating using a Distributed Acoustic Sensing (DAS) array in Hefei, China. In this experiment, we recast a 7.7 km long urban telecom optical fiber cable as 3,850 sensors using the DAS technique. From dense DAS recording, we manually identified 101 thunder events during six positive cloud‐to‐ground (CG) lightning flashes within 20 min. The DAS recorded thunder signals are dominated by direct acoustic waves rather than air‐ground coupled surface waves. The thunder events were then located using the arrival times of thunder signals. The locations and amplitudes of thunder events are generally consistent with those from the conventional lightning detection data set and broadband magnetic field. There is likely a correlation between the maximum strength of thunder events and the highest peak current for individual CG flashes. Moreover, the comparison with weather radar observations indicates that lightning usually originated from areas of high reflectivity (e.g., ≥50dBz) with diffusely distributed (from ground surface to ∼5 km altitude) thunder events and extended in a narrow altitude range of 3–5 km to areas with low radar reflectivity. Plain Language Summary: Lightning is one of the most serious natural hazards. Locating lightning is of key importance in mitigating lightning hazards, which has been achieved by detecting lightning signals (either electromagnetic pulses or thunder bursts) at distributed detectors over various ranges. In this study, we reported an experiment of thunder observation with the emerging Distributed Acoustic Sensing (DAS) technique. We recast a 7.7 km long urban telecom optical fiber cable as 3,850 microphones using the DAS technique. Using the dense sensors, we identified a total of 101 thunder events within 20 min during the lifetime of thunderstorm. Based on the DAS signal, we can also triangulate the thunder sources with a fairly good accuracy, and the geometries of thunder events generally show good correlation with radar reflectivity. Nevertheless, our study demonstrates the feasibility of effectively tracking lightning with existing optic cables, and this technique has a broad application, especially in urban areas. Key Points: Urban telecom optic fiber cable was used as a Distributed Acoustic Sensing (DAS) array to detect and locate thunder events in 3‐D spaceThe DAS records of thunder events are dominated by direct acoustic waves rather than air‐ground coupled surface wavesLightning locations constructed with DAS observations are in good agreement with radar reflectivity [ABSTRACT FROM AUTHOR]

The unconsolidated near surface and large, daily temperature variations in the desert environment degrade the vertical seismic profiling (VSP) data, posing the need for rigorous quality control. Distributed acoustic sensing (DAS) VSP data are often benchmarked using geophone surveys as a gold standard. This study showcases a new simulation-based way to assess the quality of DAS VSP acquired in the desert without geophone data. The depth uncertainty of the DAS channels in the wellbore is assessed by calibrating against formation depth based on the concept of conservation of the energy flux. Using the 1D velocity model derived from checkshot data, we simulate both DAS and geophone VSP data via an elastic pseudo-spectral finite difference method, and estimate the source and receiver signatures using matching filters. These field geophone data show high amplitude variations between channels that cannot be replicated in the simulation. In contrast, the DAS simulation shows a high visual similarity with the field DAS first arrival waveforms. The simulated source and receiver signatures are visually indistinguishable from the field DAS data in this study. Since under perfect conditions, the receiver signatures should be invariant with depth, we propose a new DAS data quality control metric based on local variations of the receiver signatures which does not require geophone measurements. [ABSTRACT FROM AUTHOR]

As megathrust earthquakes often have source areas in offshore regions, offshore seismic observations are important. However, the detection capability and resolution of offshore earthquake locations are low owing to the small number of permanent offshore seismic stations. Recently, distributed acoustic sensing (DAS) measurements, which use a fiber-optic cable as a high-density strain rate sensor, have been used for seismic observations. To evaluate the detectability of earthquakes using DAS measurements, locate earthquakes near the cable, and derive the empirical relationship between the magnitude and DAS S-wave strain rate amplitude, we conducted DAS measurements for 4 months using an offshore fiber-optic cable in the Tsugaru Strait, where various types of earthquakes were observed. In this observation, some earthquakes with magnitudes smaller than one or not listed in the earthquake catalog by the Japan Meteorological Agency (JMA) were observed. This suggests a high seismic detection capability for DAS measurements near the cable. We located earthquakes in the Tsugaru Strait by manually picking the arrivals of P- and S-waves. The hypocenters of events near the cable were located near those of the JMA catalog at a kilometer resolution; therefore, DAS data have the potential to locate earthquakes near the cable. In this study, an equation related to the maximum S-wave strain rate amplitude, hypocentral distance, and earthquake magnitude was derived. When the hypocentral distance increased by one order, the amplitude of the S-wave strain rate decreased by approximately 1.8 orders. This attenuation was larger than that derived mainly from inland DAS data in previous studies, which may be due to the difference in scattering or intrinsic attenuation between the inland and offshore regions. Using the derived equation, the magnitude of an earthquake can be estimated using the DAS data. We compared the S-wave amplitudes of the DAS strain rate and the acceleration of the permanent inland stations. The relationship between these two amplitudes is comparable to an apparent S-wave velocity of approximately 710 m/s in the sediment. [ABSTRACT FROM AUTHOR]

The devastation caused by the January 2022 eruption of Hunga Tonga-Hunga Ha'apai volcano (HTHH) in the Tongan archipelago reminded us of the importance of monitoring shallow-sea volcanic activity. Seismic observations are essential for such monitoring, but there were no operational seismic stations in Tonga at the time of the eruption. There are only a few islands near Tongan volcanoes, and installation and maintenance of seismic stations on remote islands are expensive. Seismic observations based on distributed acoustic sensing (DAS) using a seafloor cable may provide a more practical and economical solution. To investigate the potential of this approach, we made preliminary DAS observations for 1 week using the seafloor domestic broadband telecommunications cable in Tonga. DAS equipment was installed at the landing station of the seafloor cable at Nuku'alofa on Tongatapu, the main island of Tonga. To provide reference data, we installed several seismometers on Tongatapu. The DAS data we obtained showed high noise levels in areas of shallow coral reef, but noise levels decreased greatly in deeper water areas, indicating that DAS is suitable for seismic observations of the deep seafloor. We detected many local and regional earthquakes during our week of observation and determined 17 earthquake hypocenters by picking P- and S-wave arrival times from the DAS and onshore seismic data. Although most of these were tectonic events related to the subduction of the Pacific plate along the Tonga trench, several events were detected around the volcanic chain of the Tongan archipelago including one event beneath the HTHH crater, implying that activity at HTHH has continued since the 2022 eruption. The much lower cost of installation of DAS equipment compared to that for pop-up type ocean-bottom seismometers and the ability of DAS systems to monitor seismic activity in real-time make it an attractive option for monitoring the activity of HTHH and other volcanoes near seafloor cables in the Tongan archipelago. [ABSTRACT FROM AUTHOR]

This paper presents a weakly supervised machine learning method, which we call DAS-N2N, for suppressing strong random noise in distributed acoustic sensing (DAS) recordings. DAS-N2N requires no manually produced labels (i.e. pre-determined examples of clean event signals or sections of noise) for training and aims to map random noise processes to a chosen summary statistic, such as the distribution mean, median or mode, whilst retaining the true underlying signal. This is achieved by splicing (joining together) two fibres hosted within a single optical cable, recording two noisy copies of the same underlying signal corrupted by different independent realizations of random observational noise. A deep learning model can then be trained using only these two noisy copies of the data to produce a near fully denoised copy. Once the model is trained, only noisy data from a single fibre is required. Using a data set from a DAS array deployed on the surface of the Rutford Ice Stream in Antarctica, we demonstrate that DAS-N2N greatly suppresses incoherent noise and enhances the signal-to-noise ratios (SNR) of natural microseismic icequake events. We further show that this approach is inherently more efficient and effective than standard stop/pass band and white noise (e.g. Wiener) filtering routines, as well as a comparable self-supervised learning method based on masking individual DAS channels. Our preferred model for this task is lightweight, processing 30 s of data recorded at a sampling frequency of 1000 Hz over 985 channels (approximately 1 km of fibre) in <1 s. Due to the high noise levels in DAS recordings, efficient data-driven denoising methods, such as DAS-N2N, will prove essential to time-critical DAS earthquake detection, particularly in the case of microseismic monitoring. [ABSTRACT FROM AUTHOR]