Improved Rotational Vector Summation Algorithm for Fade Suppression in Distributed Acoustic Sensing

In order to meet the length calibration requirements of coherent optical time domain reflectometer(COTDR) in state monitoring of long-distance communication, a new coherent optical time domain reflectometer length calibration method was proposed. The method utilized the principle that light circulated continuously in the fiber ring. And based on the principle, the COTDR length calibration device was developed. The device used the pulse frequency and pulse width emitted by the function generator to control the breaking time of the optical switch. Thereby it could realize the control of the propagation time of the light in the fiber loop. If a fiber amplifier was added to the fiber loop, simulation of fiber links of different lengths could be realized. Finally, the length calibration of the coherent optical time domain reflectometer was achieved. The experimental results showed that the length of the fiber used in the device is 150km, and the optical pulse could be rotated up to 7 times in the fiber loop. Therefore, the maximum range of the coherent optical time domain reflectometer length calibration was 1050km. Its extended uncertainty was 7.0m, and the containing factor was 2. This method could not only promote the development of coherent optical time domain reflectometry technology, but also provided a good idea for COTDR length calibration.

Fiber optic distributed acoustic sensing (DAS) is a potential method to achieve partial discharge (PD) detection and fast localization in gas insulated metal enclosed transmission lines (GIL). In order to detect and locate the weak partial discharge signals in the GIL as early as possible, this paper proposes an integrated distributed fiber optic sensor installation method for the GIL, as well as a distributed partial discharge acoustic detection and localization system. Based on the fully insulating nature of the optical fiber, the optical fiber is installed inside the GIL to improve the sensitivity to partial discharge acoustic signals, and the length of the sensor unit is increased by repeated winding to further improve the detection performance of the acoustic signals. A true-to-life GIL partial discharge test platform was built to investigate the multi-point distributed sensing performance of the proposed coherent optical time domain reflectometer system for metal tip discharge. The test results show that the sensing distance of the sensing system can reach 120m, which can achieve the multipoint detection and localization of the partial discharge acoustic signal of 29.7pC. The proposed sensing method and system provide a new solution for GIL weak partial discharge optical fiber distributed acoustic signal detection.

Optical fiber distributed acoustic sensing (DAS) is a potential approach to realize on-line monitoring and rapid localization of power cable partial discharges (PDs). Distributed detection of discharges requires a DAS system with a wide frequency band and low noise level. For this purpose, an improved coherent optical time-domain reflectometry (COTDR) method based on dual-frequency probe pulses was developed. The method doubled the length-bandwidth product (LBP) of traditional DAS systems without significantly increasing the complexity of signal demodulation. A discharge signal extraction algorithm was also proposed based on the detection results of the dual-frequency pulses to exclude the effects of fading noises and vibrations. The improved COTDR system demonstrated the frequency band extension by detecting and recovering the 50 kHz sinusoidal vibration. The sensing distance was 1.1 km and the spatial resolution was 7.15 m. Afterward, the multi-point weak spark discharge acoustic signals were correctly measured and localized, and the frequency components up to 71.36 kHz were detected. The proposed COTDR method provides a prospective solution for distributed PD detection for power cables.

Distributed acoustic sensors (DAS) based in coherent optical time-domain reflectometry (C-OTDR) provide a cost-effective solution for intrusion monitoring of large civil infrastructures like pipelines, railways or motorways. Although detection of events is well demonstrated, an estimation of the amplitude of these events is difficult to achieve. We propose a new method to recover the amplitude of the vibration from the conventional C-OTDR backscattered power traces. It is based on the FFT analysis of the DAS signals. Using a discrete accelerometer as a reference, we have calibrated the response of an optical fiber DAS using known stimuli. A correlation between the amplitude of the vibration and the ratios of the amplitudes of harmonics to the fundamental of the DAS signals is demonstrated. This analysis overcomes the main issues of the amplitude measurement, related to the interaction between the stimulus and the interference pattern.

The nonlinear effects that limit the performance of the multi-frequency probe (MFP) based coherent optical time domain reflectometry (C-OTDR) are investigated. Based on theoretical analysis and experimental results, compared with conventional C-OTDR, when the probe pulse has power gradient within the pulse width, self-phase modulation (SPM) and cross-phase modulation (XPM) are strengthened in the new C-OTDR scheme. The generation of four-wave mixing (FWM) is dependent on SPM and XPM, and with modulation frequency of phase modulator higher than 40 MHz, the stimulated Brillouin scattering (SBS) threshold can be enhanced by more than 5 dB, which benefits the maximum dynamic range of the MFP C-OTDR.

Summary Distributed acoustic-sensing technology is a diagnostic method that has been implemented in the oil and gas industry for flow monitoring during injection or production. One of the applications is to estimate fluid distribution during hydraulic fracturing treatment. This is an invaluable diagnostic tool for multistage fracture treatments because of the large number of parameters and high uncertainty involved in these fracture treatments. Distributed acoustic sensor (DAS) measurements are based on data extracted from a fiber-optic cable installed in a wellbore. Fiber-optic cable strain is sensitive to temperature and acoustic variations induced by fluid flow with different fluid properties and fracture geometries. These parameters are evaluated from the backscattered laser pulse through the fiber using coherent Rayleigh backscattering for DAS and Raman backscattering for distributed temperature sensing (DTS). The DAS interrogation system acquires strain response to acoustic variations along the measured depth of the wellbore for all periods of time. This study presents a method of interpretation of flow-rate distribution from acoustic signals from DAS measurements. Raw acoustic measurements are transformed into energy-attribute distribution for all necessary depth locations and time frame. It allows calculating energy for each perforation cluster location. This calculation demands proper depth-interval consideration for each perforation cluster. A depth-windowing procedure allowing location of intervals where DAS channel measurements must be integrated for each timestep is applied to all stages. Based on previous experimental and computational fluid-dynamic investigations, the correlation between acoustic signal and flow rate is applied to interpret the measured DAS data to flow distribution. In this paper, the system of equations that connects acoustic energy response with clusters flow-rate distribution during the injection period using these correlations is introduced. The solution of this system allows calculating cumulative volume for each perforation cluster on each timestep and at the end of fracture treatment. As additional verification of flow-rate distribution, the results of DAS interpretation were compared with the interpretation results of DTS. A field example is used to illustrate the interpretation procedure. From this work, it is concluded that, besides qualitative analysis, the DAS interpretation method provides a quantitative estimation of flow distribution. Based on current assumptions, the interpretation results from DAS and DTS are comparable with a satisfactory agreement. The combined DAS and DTS interpretations help in understanding cluster efficiency in multistage fracture treatments.

The integration of state-of-the-art fibre optic sensing technologies with telecommunication systems has not yet been achieved. There are many challenges which need to be overcome to allow for a pan-continental research instrument, all of which requires international collaboration. Such international collaborations would allow for the creation of novel applications and research into Earth science, such as cetology and abiotic and biotic marine interactions, oceanography, seismology, volcanology, and soundscape monitoring, to name but a few.Over the past 5 years, research teams from National seismic and oceanographic infrastructures, together with National Research and Education Networks (NRENs), and partners from universities, research institutes and industry have pioneered sensing techniques to use submarine optical telecommunication fibres to monitor the Earth and its systems.Fibre sensing technology and collaborations created by developing these techniques have now reached a level where a new paradigm shift can occur. This presentation will discuss the SUBMERSE project (SUBMarinE cables for ReSearch and Exploration), which is creating and delivering a pilot research instrument which could serve as a blueprint for continuous monitoring upon many more existing submarine fibre optic cables in the future.The SUBMERSE project, which started in May 2023, is a Horizon Europe-funded, 36-month long initiative which is investigating the combined acquisition of SOP (State-of-Polarisation) and DAS (Distributed Acoustic Sensing) data from live telecom cables, with the aim to then make that data available to researchers globally and F.A.I.R. The project team, consisting of 24 organisations, uses existing fibre-optic infrastructure deployed across multiple national research infrastructures to create a pan-European research instrument.Our presentation will discuss the latest field deployments of DAS and SOP technologies across 5 geographic locations on 3 cable systems, which are spread across the European continent and Atlantic Ocean. It will offer a first glimpse of the effects of running a DAS in the L-and C- Bands on a live DWDM telecoms system, in combination with SOP, in a submarine and terrestrial environment.The aim of the SUBMERSE project is to disseminate the data following FAIR principles through established community data centres. The main challenges we have faced relate to ensuring compliance to security restrictions and handling huge data quantities generated by DAS.  The approaches to down sampling, frequency filtering, and potentially time-and-space-gating will also be presented.  We will discuss the approaches taken for acquiring, streaming from remote sites, data staging, pre-processing and raw file retention. We will also highlight the tools and approaches that we have adopted to develop best practices for running such a multi-national, distributed, sensing instrument which must take these elements into account.Our work has shown that a pragmatic approach, with collaboration at heart, is needed to address these challenges. Without a strong commitment and collaboration between research communities and research infrastructure providers, the potential to lose valuable research data is high. This risk can be mitigated by focusing on datasets which are valuable to communities and ensuring the long-term availability of those data sets in a F.A.I.R manner.

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.

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.

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.

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.

Distributed acoustic sensors (DAS) based on phase-sensitive optical time-domain reflectometry (φOTDR) have demonstrated interesting performance for many applications ranging from seismology to pipeline protection. However, the sensitivity of traditional DAS relying on coherent detection is strongly dependent on the system noise and trace fading points, offering poor reliability of the results in the spatial dimension. In this manuscript, we evaluate the statistical performance of a recently proposed DAS technique, namely, chirped-pulse φOTDR, in terms of sensitivity and signal-to-noise ratio (SNR). Our results show behavioral trends that significantly differ from those of traditional DAS. In particular, the acoustic SNR distribution in chirped-pulse DAS is notably narrower than that in the traditional case, allowing to ensure a large system dynamic range across all the points of the optical trace. Hence, chirped-pulse φOTDR offers localized perturbation detection with very high reliability, almost independent of trace fading points, along the complete reachable range of the sensor.

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.