Distributed Acoustic Sensing Research Articles

Towards unsupervised fault detection for offshore wind turbine cable protection systems using contrastive learning

As offshore wind power expands globally, it is essential to ensure the reliable operation of components of such critical infrastructures. A less explored instance of such components, which are though essential in terms of operation, is found in subsea turbine cables and their protection systems, whose failure can incur prolonged shutdown periods and costly repairs. We propose a novel unsupervised machine learning approach exploiting use of Distributed Acoustic Sensing (DAS) data and contrastive learning for monitoring offshore wind turbine Cable Protection Systems (CPSs). A Transformer neural network adapted for time-series ingests the high-frequency, noisy DAS CPS time-series measurements, and is trained to learn a coherent representation of the data using a contrastive learning scheme that enforces temporal and positional consistency in the latent space. This latent representation can then be used to perform anomaly detection in an unsupervised manner, alleviating the need for costly labeled offshore anomaly data. We demonstrate that a coherent representation of the data is learnt by the model, which we then use to detect synthetic anomalies and an actual CPS stabilization event.

Enabling endogenous distributed acoustic sensing in a digital subcarrier coherent transmission system.

To monitor the health of the fiber network and its ambient environment in densely populated access/metro network areas, in this Letter, an endogenous distributed acoustic sensing (DAS) has been proposed and achieved in a coherent digital subcarrier multiplexing (DSCM) system. Rather than specially allocating a sensing probe in general integrated communication and sensing schemes, the fractional Fourier transformed (FrFT) training sequence (TS) designated for time/frequency synchronization in DSCM coherent communications has been repurposed for sensing. While achieving excellent synchronization performance of communication, the FrFT-based TS can also be concurrently utilized to perform distributed vibration sensing. Experimental results demonstrate that the FrFT-based timing/frequency synchronization sequence is repurposed to achieve a DAS sensitivity of 70 p ε/Hz at a spatial resolution of 5 m, along with 100-Gb/s 16 quadrature amplitude modulation (QAM) DSCM transmission, without a loss of spectral efficiency.

Characterization of Gas-Liquid Two-Phase Slug Flow Using Distributed Acoustic Sensing in Horizontal Pipes.

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.

Modeling and simulation of [formula omitted]-OTDR system suitable for underwater intrusion detection and classification

Security of certain locations is of utmost importance. The contemporary works cater to locate the perturbations for the land based installations using the fiber optic based sensors. Intrusion detection for underwater environment is quite critical because of the high false alarm rates. Phase-optical time domain reflectometer (Φ-OTDR) has recently become prominent as it supports distributed acoustic sensing (DAS) utilizing optical fiber as a sensor. The benefit of developing a numerical model of the actual sensing system is that it mimics the strain developed in the fiber due to external acoustic perturbation which in turn changes the characteristics of the Rayleigh backscattered wave. Hence, we propose a numerical model based on the coherent Φ-OTDR set-up for undersea perturbation detection and classification. An intruder model is developed that determines the received pressure level at the fiber (sea depth) by considering the total transmission loss due to the simultaneous ships sailing at a pre-established distance from one another. The efficacy of the numerical model of the Φ-OTDR system is determined by firstly matching it’s outcomes with the experimental results for multiple sinusoidal disturbances acting at different locations on the fiber. Further, the numerical model is used to detect different ship sounds in underwater environment using feature extraction and frequency–time–energy relation. In addition, a complete pipeline to classify type of intruders is stated in detail.

Research on the processing and interpretation methods of distributed fiber optic vibration signal logging injection profiles

The oil and gas industry currently faces the dual challenges of improving extraction efficiency and reducing environmental impacts. Traditional well monitoring technologies are increasingly unable to meet the demands of modern oil and gas extraction due to technological limitations and environmental concerns. Distributed Acoustic Sensing (DAS) technology, which utilizes optical fibers as sensing elements, enables real-time and accurate monitoring of the CO2 injection process in wells. However, DAS technology encounters issues such as weak amplitude signals and noise interference in practical applications. In response, a comprehensive method for processing and interpreting DAS logging data for CO2 injection wells has been proposed. This method involves restoring amplitude signals through wavefront energy compensation and applying Gaussian filtering and wavelet threshold denoising algorithms for noise reduction. The processed results are classified using the K-Medoids clustering algorithm and the CO2 absorption volume in the injection layer is calculated using the integral area method. The accuracy and practicality of this method have been validated through comparative analysis with pulse neutron oxygen activation logging results. This approach aims to address the challenges faced by DAS technology in the oil and gas industry, thereby providing technical support for the efficient, environmentally friendly, and intelligent management of oil and gas fields.

Interference fading suppression with a multi-subcarrier pulse in a distributed acoustic sensor

A low-complexity multi-subcarrier pulse generation scheme is proposed to suppress the interference fading in a phase-sensitive optical time-domain reflectometer (Φ-OTDR) based distributed acoustic sensor (DAS) with heterodyne coherent detection. The multi-subcarrier pulse is generated in the digital domain based on the proper clipping operation of a sine signal. The localization and recovery of the disturbance signal are realized by the spectrum extraction and rotated vector sum (SERVS) method. The experimental results show that the occurrences of interference fading can be significantly reduced. The intensity fluctuation is reduced from ∼75 dB to ∼25 dB. Multiple disturbance signals are successfully demodulated to verify the effectiveness of the proposed method.

Research on partial discharge detection based on distributed optical fiber acoustic sensor

Distributed acoustic sensors (DASs) have the advantage of long-distance and distributed monitoring of vibration signals. However, there are few studies on the application of DAS for partial discharge (PD) monitoring of power equipment. There is also a lack of related theoretical and methodological studies. In this paper, the DAS-measured phase signals are theoretically analyzed from the frequency and energy perspectives, and the method of PD broadband signal coverage using spectrum and energy accumulation is proposed. Fiber optic rings are employed for the detection of PD signals, and the precise localization of these signals is achieved by calculating the DAS energy spectrum. The fiber optic ring is wrapped around the cable joint to achieve the detection and locating of the PD signal. The results indicate that the use of a fiber optic ring can realize the sensitive detection and locating of PD. The signal strength is maximum when the length of the fiber ring is equal to the spatial resolution of DAS. The sensitivity of PD detection in cable joints can reach 40 pC, and the simultaneous detection and localization of PD at different locations can be realized. The research can provide support for theoretical and experimental for the monitoring of PD in power equipment.

Whale detection and microseismic monitoring via DAS using submarine telecommunications cables – a case study from the NWS, Western Australia

A monitoring trial of subsea distributed acoustic sensing (DAS) conducted in the marine waters of Australia is presented. This trial explores the concept of repurposing existing submarine telecommunications cables for remote monitoring of the environment and geophysical phenomena. The data were collected from a pre-existing fibre-optic cable, 50 km in length, that links two offshore hydrocarbon production platforms off the northwest coast of Australia. Initial data analyses confirmed the ability to detect underwater sounds from various sources, including marine animals (such as baleen whales), anthropogenic activities (such as vessels), and natural geophysical phenomena (such as earthquakes). The study underscores the efficacy of DAS for capturing and locating marine mammal vocalisations, specifically highlighting signals from pygmy blue whales – a species granted the highest protection status in Australia – and Omura’s whales, both of which migrate biannually through the offshore waters of Western Australia. These findings indicate the potential of subsea DAS for detecting and tracking marine fauna regionally. Moreover, they suggest its applicability for future monitoring in support of environmental impact assessments and the development of adaptive management strategies to prevent or minimise impacts on migratory whale species from offshore industries.

Illuminating Urban Near-Surface with Distributed Acoustic Sensing Multimodal Noise Surface-Wave Imaging

Abstract Urban subsurface exploration requires high spatial and temporal resolution, cost-effective operation, and minimal interference with urban activities. Distributed acoustic sensing (DAS)—an innovative seismic observation tool—emerges as a promising solution for urban surveys. In this study, we repurposed a 7.9 km telecommunication cable traversing Hefei into a seismic observation array with 3850 channels spaced at 2 m intervals. Noise cross-correlation functions (NCFs) were constructed from recordings by iDAS2 and ZD-DAS interrogators along the entire cable. Spatial variation in the NCFs was observed and attributed to different traffic conditions. Employing the recently developed modified frequency–Bessel transform method to NCFs from the 2 km southern subsection of the optic cable, we extracted broadband, high-resolution multimodal dispersion curves. The inverted near-surface structure beneath the cable unveiled a sediment thinning trend from the center to the periphery of the Hefei basin, consistent with borehole inspections. The three-station interferometry (C3) method and beamforming with the Bessel kernel function are applied to mitigate challenges arising from the weak coupling between the cable and the Earth, as well as persistent localized noise sources. These techniques facilitated the acquisition of broadband surface waves. Distinct secondary scatters are observed in NCFs near channels 2090 and 2287, accompanied by a substantial velocity contrast of 30%–40%, suggesting the existence of a blind fault. The study reaffirms the significant potential of DAS arrays for high-resolution imaging of subsurface structures in challenging urban environments, emphasizing the importance of advanced processing techniques to enhance imaging accuracy and robustness.

Seismic Monitoring of a Deep Geothermal Field in Munich (Germany) Using Borehole Distributed Acoustic Sensing.

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.

3D VSP Imaging Using DAS Recording of P- and S-Waves in Vertical and Lateral Well Sections in West Texas.

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.

Distributed acoustic sensing for seismic surface wave data acquisition in an intertidal environment

The application of distributed acoustic sensing (DAS) for shallow marine seismic investigations is assessed, in particular with respect to the collection of seismic surface wave data in an intertidal setting. Appropriate selection and directional sensitivity of fiber-optic cables is considered and the measured data is validated with respect to conventional seismic data acquisition approaches, using geophones and hydrophones, along with independent borehole and seismic cone penetration test (SCPT) data. In terms of cable selection, a reduction in amplitude and frequency response of an armored cable is observed, when compared with an unarmored cable. For seismic surface wave surveys in an offshore environment where the cable would need to withstand significant stresses, the use of the armored variant with limited loss in frequency response may be acceptable from a practical perspective. The DAS approach also has indicated good consistency with conventional means of surface wave data acquisition, and the inverted [Formula: see text] also is very consistent with downhole SCPT data. Observed differences in phase velocity between high tide (Scholte wave propagation) and low tide (Rayleigh wave propagation) are not thought to be related to the particular type of interface wave due to shallow water depth. These differences are more likely to be related to the development of capillary forces in the partially saturated granular medium at low tide. Overall, this study demonstrates that our novel approach of DAS using seabed fiber-optic cables in the intertidal environment is capable of rapidly providing near-surface S-wave velocity data across considerable spatial scales (multikilometer) at high resolution, which is beneficial for the design of subsea cables routes and landfall locations. The associated reduction in deployment and survey duration, when compared with conventional approaches, is particularly important when working in the marine environment due to potentially short weather windows and expensive downtime.

Seismic Magnitude Estimation Using Low-Frequency Strain Amplitudes Recorded by DAS Arrays at Far-Field Distances

ABSTRACT Downhole distributed acoustic sensing (DAS) data are now routinely acquired on fiber-optic cables deployed in wells for seismic imaging and microseismic monitoring. We develop a semiempirical workflow for estimating scalar seismic moment and moment magnitude of earthquakes using strain data recorded by downhole DAS arrays. At far-field distances, the time integral of axial strain is proportional to the displacement scaled by apparent slowness. Therefore, seismic moment can be directly estimated from the amplitude of the low-frequency plateau of the strain spectra divided by frequency, similar to the methodology commonly employed for far-field displacement spectra. The effect of polarization on strain amplitudes for different types of body waves is accounted for. Benefitting from the large spatial coverage provided by DAS arrays, moment estimates from multiple channels are averaged and an average radiation coefficient is assumed over the focal sphere. We validate the methods using data of microseismic events simultaneously recorded by a surface geophone array and by DAS on fiber deployed in two horizontal wells during a hydraulic fracturing experiment. For 106 microseismic events in the magnitude range ∼ −0.65 to ∼ +0.55, we find the DAS-derived magnitudes to be consistent with the magnitudes derived from the geophone array using traditional methods, with ∼95% of the magnitude estimates differing by less than ∼0.26 units. The workflow can be potentially extended to DAS arrays in vertical wells and to S waves recorded on dark fiber DAS arrays at the surface. This methodology does not require any calibration beyond knowledge of local seismic properties, and the use of the lowest possible frequencies reduces the influence of subsurface heterogeneities and the finite spatiotemporal extent of earthquake ruptures. The capacity to estimate robust seismic magnitudes from downhole DAS arrays allows improved evaluation and management of fracture growth and more effective mitigation of induced seismicity.

Comparison of Gas Signature and Void Fraction in Water- and Oil-Based Muds Using Fiber-Optic Distributed Acoustic Sensor, Distributed Temperature Sensor, and Distributed Strain Sensor

Summary Accurate estimation and prediction of gas rise velocity, length of the gas influx region, and void fraction are important for optimal gas kick removal, riser gas management, and well control planning. These parameters are also essential in monitoring and characterization of multiphase flow. However, gas dynamics in non-Newtonian fluids, such as drilling mud, which is essential for gas influx control, are poorly understood due to the inability to create full-scale annular flow conditions that approximate the conditions observed in the field. This results in a lack of understanding and poor prediction of gas kick behavior in the field. To bridge this gap, we use distributed fiber-optic sensors (DFOS) for real-time estimation of gas rise velocity, void fraction, and influx length in water and oil-based mud (OBM) at the well scale. DFOS can overcome a major limitation of downhole gauges and logging tools by enabling the in-situ monitoring of dynamic events simultaneously across the entire wellbore. This study is the first well-scale deployment of distributed acoustic sensor (DAS), distributed temperature sensor (DTS), and distributed strain sensor (DSS) for investigation of gas behavior in water and OBM. Gas void fraction, migration velocities, and gas influx lengths were analyzed across a 5,163-ft-deep wellbore for multiphase experiments conducted with nitrogen in water and nitrogen in synthetic-based mud, at similar operating conditions. An improved transient drift flux–based numerical model was developed to simulate the experimental processes and understand the gas dynamics in different wellbore fluid environments. The gas velocities, void fractions, and gas influx lengths estimated independently using DAS, DTS, and DSS showed good agreement with the simulation results, as well as the downhole gauge analysis.

Strain field reconstruction from helical-winding fiber distributed acoustic sensing and its application in anisotropic elastic reverse time migration

Optical fiber-based distributed acoustic sensing (DAS) technology has been a popular seismic acquisition tool due to its easy deployment, wide bandwidth, and dense sampling. However, the sensitivity of straight optical fiber to only single-axis strain presents challenges in fully characterizing multicomponent seismic wavefields, making it difficult to use these data in elastic reverse time migration (ERTM). The helical-winding fiber receives projecting signals projected onto the fiber from all seismic strain field components and has the potential to reconstruct those strain components for ERTM imaging. Here, we give detailed mathematical principles of helical fiber-based DAS with crucial parameters such as pitch angle, gauge length, and rotating angle. At least six points of DAS responses are required in one or several winding periods to rebuild the strain fields within the seismic wavelength. The projecting matrix of conventional regular helical-winding fiber is singular and ill conditioned, which results in computation challenges for the inverse of the Hessian matrix for strain component reconstruction. To tackle this problem, we develop a nonregular variant pitch-angle winding configuration for helical fiber. Our winding design is validated using the rank and condition number of the projecting matrix, which is proven to be an important tool in the reconstruction of the original seismic strains. The recovered strain components from the DAS response are then used to backward propagate the receiver wavefields in ERTM with an efficient P/S decoupled approach. To summarize, we develop a novel winding design of helical fiber to recover the strain fields and then develop an efficient 3D anisotropic P/S wave-mode decomposition method for generating vector P and S wavefields during their propagation. Both methods are applied to build an anisotropic DAS-ERTM workflow for producing PP and PS images. Two synthetic examples demonstrate the effectiveness of our approach.

3D DAS VSP for Coal Seam Exploration: A Case Study from Queensland, Australia.

Seismic methods are extensively used in coal mining for expanding resource discoveries and definition as well as for mine monitoring. However, the use of borehole seismic methods is relatively uncommon due to the high cost of borehole seismic acquisition using conventional downhole tools. The introduction of distributed acoustic sensing (DAS), which uses optical fibres to record seismic data, has dramatically increased the cost-effectiveness of borehole seismic methods. Fibre-optic cables are inexpensive and, once deployed in a borehole, can be abandoned or used later for further monitoring of the subsurface. The case study presented here concerns the use of DAS to record a 3D VSP (vertical seismic profiling) for coal seam exploration in Queensland, Australia. This study trialled effective strategies for deploying cables into boreholes and demonstrated how this technology could be incorporated into the standard coal exploration process. The final processing results produced a high-resolution 3D seismic cube where the coal seams below the basalt cover are clearly identifiable around the boreholes. Permanent installation of the fibre-optic cables into a set of boreholes provides immediate benefits of 3D seismic imaging and can create additional value in utilising these sensors for further discrete or continuous subsurface measurements, including stability monitoring of underground workings and detection of methane accumulations.

Urban subsurface exploration improved by denoising of virtual shot gathers from distributed acoustic sensing ambient noise

SUMMARY 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.

Characterizing shallow fault zones by integrating profile, borehole and array measurements of seismic data and distributed acoustic sensing

AbstractWithin the framework of the Intercontinental Scientific Drilling Programme (ICDP) ‘Drilling the Eger Rift’ project, five boreholes were drilled in the Vogtland (Germany) and West Bohemia (Czech Republic) regions. Three of them will be used to install high‐frequency three‐dimensional (3D) seismic arrays. The pilot 3D array is located 1.5 km south of Landwüst (Vogtland). The borehole, with a depth of 402 m, was equipped with eight geophones and a fibre optic cable behind the casing used for distributed acoustic sensing (DAS) measurements. The borehole is surrounded by a surface array consisting of 12 seismic stations with an aperture of 400 m. During drilling, a highly fractured zone was detected between 90 m and 165 m depth and interpreted as a possible fault zone. To characterize the fault zone, two vertical seismic profiling (VSP) experiments with drop weight sources at the surface were conducted. The aim of the VSP experiments was to estimate a local 3D seismic velocity tomography including the imaging of the steep fault zone. Our 3D tomography indicates P‐wave velocities between 1500 m/s and 3000 m/s at shallow depths (0–20 m) and higher P‐wave velocities of up to 5000 m/s at greater depths. In addition, the results suggest a NW–SE striking low‐velocity zone (LVZ; characterized by = 1500–3000 m/s), which crosses the borehole at a depth of about 90–165 m. This LVZ is inferred to be a shallow non‐tectonic, steep fault zone with a dip angle of about . The depth and width of the fault zone are supported by logging data as electrical conductivity, core recovery and changes in lithology. In this study, we present an example to test and verify 3D tomography and imaging approaches of shallow non‐tectonic fault zones based on active seismic experiments using simple surface drop weights as sources and borehole chains as well as borehole DAS behind casing as sensors, complemented by seismic stand‐alone surface arrays.

Using Vehicle‐Induced DAS Signals for Near‐Surface Characterization With High Spatiotemporal Resolution

AbstractVehicle‐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.

Nonlinear Mixture Signal Separation With the Extended Slow Feature Analysis (xSFA) in Fiber-Optic Distributed Acoustic Sensor (DAS)

Nonlinear Mixture Signal Separation With the Extended Slow Feature Analysis (xSFA) in Fiber-Optic Distributed Acoustic Sensor (DAS)