Steady-Sensitivity Distributed Acoustic Sensors

Distributed acoustic sensing (DAS), implemented through phase-sensitive optical time-domain reflectometry (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula>OTDR), has gained attention in geophysical and telecommunications environments. By utilizing the existing telecom infrastructure, DAS facilitates self-monitoring of networks and external events observation, from human activities to large-scale geophysical phenomena. However, integrating it with data signals on the same fiber is challenging, as DAS employs higher power signals that can interact with data channels. Although increasing DAS performance with higher bandwidths is possible, practical applications have mostly been limited to 1 GHz due to cost and complexity constraints. This paper investigates the coexistence conditions of DAS, specifically direct detection (DD) chirped-pulse-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula>OTDR (CP-<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\phi$</tex-math></inline-formula>OTDR), with intensity-modulated and DD (IM-DD) telecommunication systems, both operating at comparable complexities (5 GHz chirp and 10 Gbit/s), particularly interesting for metro/access contexts. We varied the frequency spacing of data and DAS channels, the DAS pulse peak power, and the receiver optical filter bandwidth, identifying the conditions for feasible coexistence. Our analysis shows that nonlinear Kerr-induced modulation instability (MI) is the main impairment affecting IM-DD channels co-propagating with DAS. In this scenario, MI cannot be approximated as originating from a single frequency source, and the observed nonlinear impairments vary along the pulse with its sweeping central frequency. Numerical simulations validate the experimental results, showing close alignment.

Distributed acoustic sensing (DAS) delivers real-time observation ofphysical perturbations such as vibrations or strain variations in conventional optical fibers with high sensitivity. The high density of sensing points and large network footprint provided by a single DAS system, along with the availability of a vast optical fiber network already deployed both in land and in oceanic regions, contrast with the high deployment and maintenance cost of conventional instrumentation networks for seismology. This situation has triggered a rapid growth of DAS deployments for seismic monitoring in recent years. Photonic engineers and geophysicists have joined efforts to prove the value of optical fibers as distributed seismometers, which has resulted in a wide panoply of tests demonstrating diverse applicability across the geosciences. For example, DAS has been successfully applied recording local to teleseismic earthquakes, monitoring glacial icequakes, and observing oceanographic phenomena at the sea floor. Most of the realized tests have been performed using commercially available optical fiber interrogators based on phase-sensitive optical time-domain reflectometry. Among them, DAS based on chirped pulse distributed acoustic sensing have provided optimized performance in terms of both range and sensitivity, particularly at low frequencies. In this communication, we provide a comprehensive review of the current situation of DAS for seismology applications, focusing on near surface monitoring, where already deployed optical fibers can be repurposed as sensor networks.

Hydraulic fracture height and width are key parameters for completion design and evaluation of unconventional resources. Traditional measurement technologies like microseismic, tilt-meters, chemical or radioactive tracers, pressure temperature gauges, etc. either have low resolution or rely on sensitive models which can cause a high degree of uncertainty. Recent frac-hit measurement methods include the use of Distributed Acoustic Sensing (DAS) deployed in far-field wellbores offset from the treatment well. The DAS system directly measures the fracture propagation every second through the completion, with a few meters' spatial resolution sampled at 0.25-meter intervals along the monitored fiber well. Cross-well fiber optic monitored far-field strain (FFS) results suggested elastic stress effects, intense inelastic fracture expansion and closure events which provide identification and measurement of frac height on a TVD plot, as well as width by measured depth along the wellbore laterals. Vertical wells or the heel-section of horizontal wells are suitable for frac-hit height (FHH) measurement; fracture azimuth and wellbore geometries need to be considered for precise evaluation. A specially designed engineered constellation fiber cable was tested and utilized in this method in combination with a true phase coherence DAS interrogator with a 20 dB improved sensitivity (Signal-to-Noise-Ratio (SNR)) for both low and high frequency ranges DAS. The optic fiber can be either permanently installed outside the casing or temporarily deployed inside a monitor well. Comparable results can be achieved by the engineered fiber system and have been presented within case studies for both horizontal and vertical well sections. In addition, Distributed Temperature Sensing (DTS) and data from downhole gauge can confirm any temperature or pressure changes resulting from frac driven interactions (FDI). With this approach, fracture azimuth, frac-hit corridor (FHC) width and FHH can be determined with a high degree of accuracy and resolution. Completion engineers were able to optimize frac models in real-time and further change completion schedules during the frac treatment.

Chirped pulse phase-sensitive optical time domain reflectometry (CP-ϕOTDR) presents a simple yet effective direct-detection approach for distributed acoustic sensing (DAS), offering distinct advantages and limitations compared to traditional phase-measuring techniques. Although CP-ϕOTDR has been widely adopted in both research and industry - typically in the ≈1 GHz chirp range - the use of large optical chirps has been mostly avoided due to increased cost and complexity. In this work a holistic study of the performance attainable with CP-ϕOTDR using large chirps (8GHz) is presented, allowing for a vast range of operation to be covered, from fast and large strain variations to quasi-static high sensitivity measurements. Under a single optical setup, operating at kHz acoustic sampling and with 10's of pϵ/√Hz ASD noise floor, the dynamic strain range is increased to single-shot variations of ±3μϵ (two orders of magnitude higher than traditional phase-measuring DAS for similar spatial resolutions), whilst readily demonstrating nanostrain measurements into the μHz regime. The long-term CP-ϕOTDR instrumental uncertainty is placed at sub-MHz, and differential measurement between adjacent fiber positions can be used for discounting cross-sensitivities to other effects (such as temperature), reasonably demonstrating the possibility for long-term nanostrain measurements under quiet fiber environments.

CloudSim is a versatile simulation framework for modeling cloud infrastructure components that supports customizable and extensible application provisioning strategies, allowing for the simulation of cloud services. On the other hand, Distributed Acoustic Sensing (DAS) is a ubiquitous technique used for measuring vibrations over an extended region. Data handling in DAS remains an open issue, as many applications need continuous monitoring of a volume of samples whose storage and processing in real time require high-capacity memory and computing resources. We employ the CloudSim tool to design and evaluate a cloud computing scheme for long-range, polarization-independent DAS using coherent detection of Rayleigh backscattering signals and uncover valuable insights on the evolution of the processing times for a diverse range of Virtual Machine (VM) capacities as well as sizes of blocks of processed data. Our analysis demonstrates that the choice of VM significantly impacts computational times in real-time measurements in long-range DAS and that achieving polarization independence introduces minimal processing overheads in the system. Additionally, the increase in the block size of processed samples per cycle results in diminishing increments in overall processing times per batch of new samples added, demonstrating the scalability of cloud computing schemes in long-range DAS and its capability to manage larger datasets efficiently.

HighlightsWhat are the main findings?The Distributed Acoustic Sensing (DAS) response is strongly influenced by the cable’s structural configuration, with the Microcable showing the highest strain sensitivity (≈0.025 a.u.) compared to the Duct and Anti-Rodent cables tested under identical laboratory conditions.Simplified and compact cable designs enable more effective strain transfer to the optical fiber, whereas multilayered or armored constructions significantly attenuate the transmitted strain signal.What are the implications of the main findings?The structural configuration of telecommunication cables should be carefully considered when repurposing them for DAS applications, as it directly impacts sensing efficiency and spatial response.Microcables are ideal for indoor or duct-protected environments requiring high sensitivity, while armored cables remain preferable for long-term or harsh-environment installations prioritizing durability.Distributed Acoustic Sensing (DAS) transforms conventional optical fibres into large-scale acoustic sensor arrays. While existing telecommunication cables are increasingly considered for DAS-based monitoring, their performance depends strongly on cable construction and strain transfer efficiency. In this study, the relative DAS signal amplitudes of three commercial telecommunication optical cables were experimentally compared using a benchtop Rayleigh backscattering-based interrogator under controlled laboratory conditions. By maintaining a constant temperature and ensuring no additional strain changes from the outside environment, we guaranteed that only strain-induced variations from acoustic excitations were measured. The results show clear differences in signal amplitude and signal-to-noise ratio (SNR) among the tested cables. The Microcable consistently produced the highest spatial peak amplitude (up to 0.029 a.u.) and SNR (up to 79), while the Duct cable reached 0.00268 a.u. with mean SNR ≈ 32. The Anti-Rodent cable showed low signal amplitude (0.0018 a.u.) but exhibited a high mean SNR (≈111) driven by an exceptional low noise floor in one of the runs. These findings reflect the variations in mechanical coupling between the fibre core and external perturbations and provide practical insights into the suitability of different telecom cable types for DAS applications, supporting informed choices for future deployments.

Distributed acoustic sensing (DAS) is a new tool with low cost, sensitive signal capture, and complete coverage for vertical seismic profile (VSP) acquisition. Although DAS has obvious advantages over geophones, some weaknesses may limit its application. The main challenge is that DAS is polluted by various types of noise, including optical abnormal noise, random background noise, fading noise, and so on. To suppress these novel noises, we developed a new denoising neural network based on singular spectrum analysis—multichannel singular value decomposition denoising convolutional neural network (SVDDCNN). The network can simultaneously extract data features from singular spectrum instead of the time domain, which can represent geophysical features more accurately and help separate signals from noises. Second, a multichannel input layer is designed, and the input is decomposed into three subspaces by singular spectrum analysis, which provides records of different signal-to-noise ratios (SNRs) for training and improves generalization ability of the network. Third, to enhance the quality of the data set, we added the noise subspace records removed by SVD into the training set to provide various forms of noise with different singular spectra. Both synthetic and field examples show that our network has achieved impressive denoising of DAS VSP and demonstrated competitive performance compared with other methods. Furthermore, the structure similarity (SSIM) map is introduced to evaluate the signal leakage by calculating the similarity between the denoised record and the removed noise record. The lowest SSIM index of the proposed network indicated superior signal preservation ability.

With extensive development of urban underground space, ground collapses occasionally occur, resulting in property damage, injuries, or fatalities. Therefore, establishing an effective ground monitoring and collapse prevention system is necessary for reducing collapsing risk. Distributed Acoustic Sensing (DAS) technology has great potential for ground collapse monitoring around rail system for capacity of long-distance high-density monitoring and immunity to electromagnetic interference (EMI). In this study, experimental investigations were conducted using a high-performance DAS interrogator integrated with a 4 km-long distributed optical fiber to record background ambient noise inside a subway tunnel. Noise2Noise (N2N) unsupervised denoising model was employed to preprocess the DAS data across different time spans, thereby enhancing the signal-to-noise ratio (SNR). Additionally, noise tomography method in terms of passive multichannel analysis of surface waves (MASW) algorithm was utilized to extract the surface waves and calculate the dispersion curves containing soil layer information Stacking methods, including Phase-Weighted Stacks (PWS) and Generalized Average of Signals (GAS), were applied to further improve the SNR of the dispersion curves. Ultimately, a 2D S-wave velocity model to 45m depth was constructed, revealing low-velocity soil defects such as hollow and settlement. The result demonstrates that integrating DAS with tomographic imaging using urban ambient noise can provide a reliable basis for preventing ground collapse hazards.

A major challenge in cryoseismology is that signals of interest are often buried within the high noise level emitted by a variety of environmental processes. Particular Distributed Acoustic Sensing (DAS) data often suffers from low signal‐to‐noise ratios (SNR) potentially resulting in a multitude of undetected events of interest, which further remain unanalyzed. To record seismicity, we deployed a DAS system on Rhône Glacier, Switzerland, using a 9 km long fiber‐optic cable that covered the entire glacier, from its accumulation to its ablation zone. The highly active and dynamic cryospheric environment, in combination with poor coupling, resulted in DAS data characterized by a low SNR. Our objective is to develop and evaluate a method to effectively denoise this cryoseismological DAS data set while comparing our approach to state‐of‐the‐art filtering and denoising methods. We propose the ‐invariantcryo denoiser, specifically trained on cryoseismological data and capable of separating incoherent environmental noise from temporally and spatially coherent signals of interest, based on a self‐supervised ‐invariant U‐Net autoencoder. The method enhances inter‐channel coherence, improves waveform similarity with co‐located seismometers, and increases SNR. The comparison of different methods shows that our approach obtains the highest gain in SNR and highest similarity with co‐located seismometers while suffering from denoising artifacts in rare cases. The proposed denoiser has the potential to enhance the detection capabilities of events of interest in cryoseismological DAS data, hence to improve the understanding of processes within Alpine glaciers.

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

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 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 . 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.

Distributed acoustic sensing (DAS) is a technology that uses optical fiber as a sensing unit to detect external vibration signals. Due to the high resolution and high sensitivity of DAS, it has great application potential in the detection of vibration events. However, high detection performance will bring limitations to DAS in multi-source detection. In order to solve the problem of poor multi-source and multi-target recognition ability of DAS in complex multi-source environments, a multi-target recognition processing method of DAS system is proposed. Firstly, the acquired signal is pretreated to suppress the influence of system noise. Secondly, based on the independence of different events, a multi-source vibration location identification method based on the principal eigenvalue analysis of the observation matrix is proposed to realize the identification of the number and location of the multi-source vibration. Finally, the dimension of the acquired signal is reduced according to the number of sound sources, and the multi-source vibration signal recognition method based on amplitude uncertainty suppression and fast independent component analysis is proposed to realize the multi-source vibration signal recognition. The experimental results show that the proposed method has achieved good processing effect in both simulation and actual measurement, and the relative error is kept below 0.3. In comparison experiments, the proposed method is superior to principal component analysis, EMD and EWT in location recognition and signal recognition effect. This method provides technical support for DAS application in complex multi-source environment and improves the practical application value of DAS.

Large-scale seismic acquisition, versatility, flexibility, automation, and scalability are the objectives of future oil and gas exploration technology. An example of emerging technology for seismic monitoring is distributed acoustic sensing (DAS). The significant amount of data produced by DAS is a challenge that necessitates the development of new technologies for its efficient handling and processing. A typical seismic survey, on the one hand, can generate hundreds of terabytes of raw seismic data per day. The demand for wireless seismic data transmission, on the other hand, remains enormous. The massive amount of data transmission from geophones to the on-site data collection center and its storage poses significant challenges. A lightweight compression procedure is required in order to reduce the data traffic and the storage size at the data center without putting an extra burden on a geophone. In this brief, an efficient implementation of a 1D convolutional neural network (CNN) together with 1-bit adaptive delta modulation is presented for in-field seismic data compression. It is worth mentioning here that the training of CNN is done offline on synthetic data set and hence, the proposed approach has potential for real-time implementation. Furthermore, no assumption on the underlying statistics of noise or the seismic signal is imposed and consequently, the proposed method is suitable for a wide range of seismic data. Furthermore, the proposed method works in the time-domain, unlike existing transform-domain methods, making it suitable for quick diagnosis of bad traces at the data center. Simulation results with real data set reveal that the proposed approach achieves a signal-to-noise ratio (SNR) of approximately 30 dB with a compression gain of 35: 1. Finally, significant superiority in terms of compression gain and reconstruction quality is demonstrated when compared to the existing methods.

Due to the power and transmission rate limitations, it is challenging to achieve underwater acoustic (UWA) communications over long distances from the unmanned underwater vehicle (UUV) to the shore station. As an alternative solution, the distributed acoustic sensing (DAS) underwater communication uses submarine optical cable to acquire and demodulate acoustic communication signals. There is little research on the channel and noise of DAS underwater communication. This paper analyzes the channel characteristics and the background noise of a DAS underwater communication system with armored optical cable based on the experimental measurements. Channel analysis shows that the envelope amplitudes of impulse responses follow the Burr distribution. The amplitudes get increased concentration at lower values as the sound source goes closer to the position right above the optical cable. Moreover, we observe that the received signals are influenced by the adjacent segments, leading to multiple arrivals within each acoustic bounce. Noise analysis shows that for frequencies lower than 1 kHz, unlike the Wenz noise spectrum primarily caused by shipping and wind, the −18 dB/octave decrease in power spectral density (PSD) of DAS noise is mainly caused by the equipment and cable themselves. The noise also exhibits full-band fluctuations. In underwater communications, full-band noise poses significant challenges for signal design. As far as we know, this article is the first report on the field experiment using a DAS underwater communication system equipped with armored optical cable, providing a reference for future underwater applications.

Time-gated digital optical frequency domain reflectometry (TGD-OFDR) overcomes the inherent trade-off between spatial resolution and sensing distance in conventional phase-sensitive optical time domain reflectometry (&Phi;-OTDR) systems through pulse compression technology, thereby achieving both high spatial resolution and large dynamic range. However, interference fading-induced phase demodulation distortion results in sensing dead zones that significantly compromise system reliability. To address this limitation, we propose a novel fading-resistant TGD-OFDR system based on bipolar multiband nonlinear frequency-modulated (NLFM) pulse multiplexing. Multiband signals are generated via a phase modulator and converted into bipolar pulses with nonlinear chirp characteristics using an acousto-optic modulator. During coherent detection, the spectral folding effect from negative to positive frequency bands is exploited and combined with a digital matched filter bank to enable multi-channel parallel pulse compression. Subsequently, the rotation vector sum (RVS) algorithm is applied for signal multiplexing to effectively suppress interference fading effects. In the demonstration experiment, we achieved vibration phase demodulation without fading at a sensing distance of 30 km with a spatial resolution of 1.2 m. The proposed approach innovatively reformulates the convolution of bipolar chirp pulses with the sensing fiber impulse response as an optimization problem in frequency-domain resource multiplexing, reducing spectral occupancy by 50% while maintaining equivalent performance metrics.