Distributed fiber-optic sensing(DFOS)can turn the worldwide fiber network into a sensing array,which may immensely extend the sensing range and approaches for hazard assessment,earth observation,and human activity mea...Distributed fiber-optic sensing(DFOS)can turn the worldwide fiber network into a sensing array,which may immensely extend the sensing range and approaches for hazard assessment,earth observation,and human activity measurement.However,most existing DFOS schemes cannot simultaneously give dual attention to the detection ability(for example,sensing distance)and multipoint localizing function.A mirror-image correlation method is proposed and can precisely extract the time delay between two original signals from their composite detected signal.This method enables the distributed vibration sensing function of the laser interferometer and maintains its high detection ability.We demonstrate its feasibility by simultaneously localizing multiple knocking vibrations on a 250-km round-trip fiber and distinguishing traffic vibrations at two urban positions in a field test.The localizing precision is analyzed and satisfies the requirements for fiber network sensing.展开更多
Curved geostructures,such as tunnels,are commonly encountered in geotechnical engineering and are critical to maintaining structural stability.Ensuring their proper performance through field monitoring during their se...Curved geostructures,such as tunnels,are commonly encountered in geotechnical engineering and are critical to maintaining structural stability.Ensuring their proper performance through field monitoring during their service life is essential for the overall functionality of geotechnical infrastructure.Distributed Brillouin sensing(DBS)is increasingly applied in geotechnical projects due to its ability to acquire spatially continuous strain and temperature distributions over distances of up to 150 km using a single optical fibre.However,limited by the complex operations of distributed optic fibre sensing(DFOS)sensors in curved structures,previous reports about exploiting DBS in geotechnical structural health monitoring(SHM)have mostly been focused on flat surfaces.The lack of suitable DFOS installation methods matched to the spatial characteristics of continuous monitoring is one of the major factors that hinder the further application of this technique in curved structures.This review paper starts with a brief introduction of the fundamental working principle of DBS and the inherent limitations of DBS being used on monitoring curved surfaces.Subsequently,the state-of-the-art installation methods of optical fibres in curved structures are reviewed and compared to address the most suitable scenario of each method and their advantages and disadvantages.The installation challenges of optical fibres that can highly affect measurement accuracy are also discussed in the paper.展开更多
The Carter model is used to characterize the dynamic behaviors of fracture growth and fracturing fluid leakoff.A thermo-fluid coupling temperature response forward model is built considering the fluid flow and heat tr...The Carter model is used to characterize the dynamic behaviors of fracture growth and fracturing fluid leakoff.A thermo-fluid coupling temperature response forward model is built considering the fluid flow and heat transfer in wellbore,fracture and reservoir.The influences of fracturing parameters and fracture parameters on the responses of distributed temperature sensing(DTS)are analyzed,and a diagnosis method of fracture parameters is presented based on the simulated annealing algorithm.A field case study is introduced to verify the model’s reliability.Typical V-shaped characteristics can be observed from the DTS responses in the multi-cluster fracturing process,with locations corresponding to the hydraulic fractures.The V-shape depth is shallower for a higher injection rate and longer fracturing and shut-in time.Also,the V-shape is wider for a higher fracture-surface leakoff coefficient,longer fracturing time and smaller fracture width.Additionally,the cooling effect near the wellbore continues to spread into the reservoir during the shut-in period,causing the DTS temperature to decrease instead of rise.Real-time monitoring and interpretation of DTS temperature data can help understand the fracture propagation during fracturing operation,so that immediate measures can be taken to improve the fracturing performance.展开更多
Distributed acoustic sensing(DAS)is increasingly used in seismic exploration owing to its wide frequency range,dense sampling and real-time monitoring.DAS radiation patterns help to understand angle response of DAS re...Distributed acoustic sensing(DAS)is increasingly used in seismic exploration owing to its wide frequency range,dense sampling and real-time monitoring.DAS radiation patterns help to understand angle response of DAS records and improve the quality of inversion and imaging.In this paper,we solve the 3D vertical transverse isotropic(VTI)Christoffel equation and obtain the analytical,frst-order,and zero-order Taylor expansion solutions that represent P-,SV-,and SH-wave phase velocities and polarization vectors.These analytical and approximated solutions are used to build the P/S plane-wave expression identical to the far-feld term of seismic wave,from which the strain rate expressions are derived and DAS radiation patterns are thus extracted for anisotropic P/S waves.We observe that the gauge length and phase angle terms control the radiating intensity of DAS records.Additionally,the Bond transformation is adopted to derive the DAS radiation patterns in title transverse isotropic(TTI)media,which exhibits higher complexity than that of VTI media.Several synthetic examples demonstrate the feasibility and effectiveness of our theory.展开更多
The distribution of shear-wave velocities in the subsurface is generally used to assess the potential forseismic liquefaction and soil amplification effects and to classify seismic sites. Newly developeddistributed ac...The distribution of shear-wave velocities in the subsurface is generally used to assess the potential forseismic liquefaction and soil amplification effects and to classify seismic sites. Newly developeddistributed acoustic sensing (DAS) technology enables estimation of the shear-wave distribution as ahigh-density seismic observation system. This technology is characterized by low maintenance costs,high-resolution outputs, and real-time data transmission capabilities, albeit with the challenge ofmanaging massive data generation. Rapid and efficient interpretation of data is the key to advancingapplication of the DAS technology. In this study, field tests were carried out to record ambient noise overa short period using DAS technology, from which the surface-wave dispersion curves were extracted. Inorder to reduce the influence of directional effects on the results, an unsupervised clustering method isused to select appropriate clusters to extract the Green's function. A combination of a genetic algorithmand Monte Carlo (GA-MC) simulation is proposed to invert the subsurface velocity structure. Thestratigraphic profiles obtained by the GA-MC method are in agreement with the borehole profiles.Compared to other methods, the proposed optimization method not only improves the solution qualitybut also reduces the solution time.展开更多
Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contr...Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contribute to the identification of defects in underground structures,this study conducted a four-point bending test of a reinforced concrete(RC)beam and uniaxial loading tests of an RC specimen with local cavities.The experimental results revealed the disparity in DFOS strain spike profiles between these two structural anomalies.The effectiveness of DFOS in the quantification of crack opening displacement(COD)was also demonstrated,even in cases where perfect bonding was not achievable between the cable and structures.In addition,DFOS strain spikes observed in two diaphragm wall panels of a twin circular shaft were also reported.The most probable cause of those spikes was identified as the mechanical behavior associated with local concrete contamination.With the utilization of the strain profiles obtained from laboratory tests and field monitoring,three types of multi-classifiers,based on support vector machine(SVM),random forest(RF),and backpropagation neural network(BP),were employed to classify strain profiles,including crack-induced spikes,non-crack-induced spikes,and non-spike strain profiles.Among these classifiers,the SVM-based classifier exhibited superior performance in terms of accuracy and model robustness.This finding suggests that the SVM-based classifier holds promise as a potential solution for the automatic detection and classification of defects in underground structures during long-term monitoring.展开更多
The principle of optical time-domain reflection localization limits the sensing spatial resolution of Raman distributed optical fiber sensing.We provide a solution for a Raman distributed optical fiber sensing system ...The principle of optical time-domain reflection localization limits the sensing spatial resolution of Raman distributed optical fiber sensing.We provide a solution for a Raman distributed optical fiber sensing system with kilometer-level sensing distance and submeter spatial resolution.Based on this,we propose a Raman distributed optical fiber sensing scheme based on chaotic pulse cluster demodulation.Chaotic pulse clusters are used as the probe signal,in preference to conventional pulsed or chaotic single-pulse lasers.Furthermore,the accurate positioning of the temperature variety region along the sensing fiber can be realized using chaotic pulse clusters.The proposed demodulation scheme can enhance the signal-to-noise ratio by improving the correlation between the chaotic reference and the chaotic Raman anti-Stokes scattering signals.The experiment achieved a sensing spatial resolution of 30 cm at a distributed temperature-sensing distance of∼6.0 km.Furthermore,we explored the influence of chaotic pulse width and detector bandwidth on the sensing spatial resolution.In addition,the theoretical experiments proved that the sensing spatial resolution in the proposed scheme was independent of the pulse width and sensing distance.展开更多
Phase-sensitive Optical Time-Domain Reflectometer(φ-OTDR)technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals.This technology...Phase-sensitive Optical Time-Domain Reflectometer(φ-OTDR)technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals.This technology is widely used in applications such as intrusion monitoring and structural health assessments.Traditional signal processing methods,such as Support Vector Machines(SVM)and K-Nearest Neighbors(KNN),have limitations in feature extraction and classification in complex environments.Conversely,a single deep learning model often struggles with capturing long time-series dependencies and mitigating noise interference.In this study,we propose a deep learning model that integrates Convolutional Neural Network(CNN),Long Short-Term Memory Network(LSTM),and Transformer modules,leveraging φ-OTDR technology for distributed fiber vibration sensing event recognition.The hybrid model combines the CNN's capability to extract local features,the LSTM's ability to model temporal dynamics,and the Transformer's proficiency in capturing global dependencies.This integration significantly enhances the accuracy and robustness of event recognition.In experiments involving six types of vibration events,the model consistently achieved a validation accuracy of 0.92,and maintained a validation loss of approximately 0.2,surpassing other models,such as TAM+BiLSTM and CNN+CBAM.The results indicate that the CNN+LSTM+Transformer model is highly effective in handling vibration signal classification tasks in complex scenarios,offering a promising new direction for the application of fiber optic vibration sensing technology.展开更多
Fiber-optic distributed strain sensing(FO-DSS)has been successful in monitoring strain changes along horizontal wellbores in hydraulically fractured reservoirs.However,the mechanism driving the various FO-DSS response...Fiber-optic distributed strain sensing(FO-DSS)has been successful in monitoring strain changes along horizontal wellbores in hydraulically fractured reservoirs.However,the mechanism driving the various FO-DSS responses associated with near-wellbore hydraulic fracture properties is still unclear.To address this knowledge gap,we use coupled wellbore-reservoir-geomechanics simulations to study measured strain-change behavior and infer hydraulic fracture characteristics.The crossflow among fractures is captured through explicit modeling of the transient wellbore flow.In addition,local grid refinement is applied to accurately capture strain changes along the fiber.A Base Case model was designed with four fractures of varying properties,simulating strain change signals when the production well is shut-in for 10 d after 240 d of production and reopened for 2 d.Strain-pressure plots for different fracture clusters were used to gain insights into inferring fracture properties using DSS data.When comparing the model with and without the wellbore,distinct strain change signals were observed,emphasizing the importance of incorporating the wellbore in FO-DSS modeling.The effects of fracture spacing and matrix permeability on strain change signals were thoroughly investigated.The results of our numerical study can improve the understanding of the relation between DSS signals and fracture hydraulic properties,thus maximizing the value of the dataset for fracture diagnostics and characterization.展开更多
Distributed Acoustic Sensing(DAS) is an emerging technique for ultra-dense seismic observation, which provides a new method for high-resolution sub-surface seismic imaging. Recently a large number of linear DAS arrays...Distributed Acoustic Sensing(DAS) is an emerging technique for ultra-dense seismic observation, which provides a new method for high-resolution sub-surface seismic imaging. Recently a large number of linear DAS arrays have been used for two-dimensional S-wave near-surface imaging in urban areas. In order to explore the feasibility of three-dimensional(3D) structure imaging using a DAS array, we carried out an active source experiment at the Beijing National Earth Observatory. We deployed a 1 km optical cable in a rectangular shape, and the optical cable was recast into 250 sensors with a channel spacing of 4 m. The DAS array clearly recorded the P, S and surface waves generated by a hammer source. The first-arrival P wave travel times were first picked with a ShortTerm Average/Long-Term Average(STA/LTA) method and further manually checked. The P-wave signals recorded by the DAS are consistent with those recorded by the horizontal components of short-period seismometers. At shorter source-receiver distances, the picked P-wave arrivals from the DAS recording are consistent with vertical component recordings of seismometers, but they clearly lag behind the latter at greater distances.This is likely due to a combination of the signal-to-noise ratio and the polarization of the incoming wave. Then,we used the Tomo DD software to invert the 3D P-wave velocity structure for the uppermost 50 m with a resolution of 10 m. The inverted P-wave velocity structures agree well with the S-wave velocity structure previously obtained through ambient noise tomography. Our study indicates the feasibility of 3D near-surface imaging with the active source and DAS array. However, the inverted absolute velocity values at large depths may be biased due to potential time shifts between the DAS recording and seismometer at large source-receiver distances.展开更多
Presented here is long-range distributed vibration sensing based on internal-modulation optical frequency domain reflectometry(OFDR).In the proposed system with internal modulation,a silicon-based photonic-chip laser i...Presented here is long-range distributed vibration sensing based on internal-modulation optical frequency domain reflectometry(OFDR).In the proposed system with internal modulation,a silicon-based photonic-chip laser is used as the laser source,and by controlling the output voltage curve of an arbitrary waveform generator to induce temperature change in the external cavity of the laser,a 10-GHz optical frequency tuning range is achieved.The complexity of the proposed internal-modulation system is lower than that of the traditional external-modulation OFDR system that combines a narrow-linewidth laser with a single-sideband modulator to achieve wavelength tuning.Cross-correlation analysis is used as a sensing mechanism to evaluate the similarity between Rayleigh scatter signals and to achieve vibration event localization.Experimental comparison is made of the vibration sensing performance of the external-and internal-modulation systems,and for a vibration event generated at a distance of 100.95 km,they locate it with a sensing spatial resolution of 43.0 m and 16.8 m,respectively.The results indicates that the proposed distributed vibration sensing based on internal modulation has better sensing performance and lower complexity compared to the traditional external-modulation system.In addition,the proposed system is single-ended and involves no optical amplification,which makes it very suitable for ultra-long-range sensing.展开更多
The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibratio...The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibration signals induced by external events and to effectively provide early warnings of potential intrusion activities.Due to the complexity and diversity of external intrusion events,traditional deep learning methods can achieve event recognition with an average accuracy exceeding 90%.However,these methods rely on large-scale datasets,leading to significant time and labor costs during the data collection process.Additionally,traditional methods perform poorly when faced with the scarcity of low-frequency event samples,making it challenging to address these rare occurrences.To address this issue,this paper proposes a small-sample learning model based on triplet learning for intrusion event recognition.The model employs a 6-way 20-shot support set configuration and utilizes the KNN clustering algorithm to assess the model's performance.Experimental results indicate that the model achieves an average accuracy of 91.6%,further validating the superior performance of the triplet learning model in classifying external intrusion events.Compared to traditional methods,this approach not only effectively reduces the dependence on large-scale datasets but also better addresses the classification of low-frequency event samples,demonstrating significant application potential.展开更多
The rapid development of Internet Plus Smart Energy requires further strengthening of three kinds of interconnections based on traditional power systems: physical interconnection, information interconnection, and comm...The rapid development of Internet Plus Smart Energy requires further strengthening of three kinds of interconnections based on traditional power systems: physical interconnection, information interconnection, and commercial interconnection. Due to the integration of renewable energy, the reform of the electricity market, and the deployment of the Smart Grid, a large amount of data will be generated. Thus, it is necessary to establish a Ubiquitous Power Internet of Things (UPIoT) to realize connections among people and things, things and things, and people and people in power systems. This paper studies the concept and architecture of the UPIoT and indicates the deployment of the perception layer and network layer as the key to building UPIoT in the initial stage. As UPIoT tends to cover a wide area and produce massive and distributed data, signal processing and data analytics theories and techniques are needed to handle the data and observe the state of the large-scale system. Further studies on distributed sensing and cooperative estimation theories and techniques of UPIoT are also required. Finally, the application prospects of UPIoT and the directions for future research are discussed.展开更多
Distributed acoustic sensing(DAS) is one recently developed seismic acquisition technique that is based on fiber-optic sensing. DAS provides dense spatial spacing that is useful to image shallow structure with surface...Distributed acoustic sensing(DAS) is one recently developed seismic acquisition technique that is based on fiber-optic sensing. DAS provides dense spatial spacing that is useful to image shallow structure with surface waves.To test the feasibility of DAS in shallow structure imaging,the PoroTomo team conducted a DAS experiment with the vibroseis truck T-Rex in Brady’s Hot Springs, Nevada, USA.The Rayleigh waves excited by the vertical mode of the vibroseis truck were analyzed with the Multichannel Analysis of Surface Waves(MASW) method. Phase velocities between5 and 20 Hz were successfully extracted for one segment of cable and were employed to build a shear-wave velocity model for the top 50 meters. The dispersion curves obtained with DAS agree well with the ones extracted from co-located geophones data and from the passive source Noise Correlation Functions(NCF). Comparing to the co-located geophone array, the higher sensor density that DAS arrays provides help reducing aliasing in dispersion analysis, and separating different surface wave modes. This study demonstrates the feasibility and advantage of DAS in imaging shallow structure with surface waves.展开更多
Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distr...Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distributed Wavelet Basis Generation(DWBG) algorithm performing at the sink to obtain the distributed wavelet basis in WSN.And on this basis,a Wavelet Transform-based Distributed Compressed Sensing(WTDCS) algorithm is proposed to compress and reconstruct the sensed data with spatial correlation.Finally,we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio,the channel Signal-to-Noise Ratio(SNR),the observation noise power and the correlation decay parameter by simulation.The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy,as compared to the conventional distributed wavelet transform algorithm.展开更多
This paper investigates the deformation and fracture propagation of sandstone specimen under uniaxial compression using the distributed fiber optic strain sensing(DFOSS)technology.It shows that the DFOSS-based circumf...This paper investigates the deformation and fracture propagation of sandstone specimen under uniaxial compression using the distributed fiber optic strain sensing(DFOSS)technology.It shows that the DFOSS-based circumferential strains are in agreement with the data monitored with the traditional strain gage.The DFOSS successfully scans the full-field view of axial and circumferential strains on the specimen surface.The spatiotemporal strain measurement based on DFOSS manifests crack closure and elastoplastic deformation,detects initialization of microcrack nucleation,and identifies strain localization within the specimen.The DFOSS well observes the effects of rock heterogeneity on rock deformation.The advantage of DFOSS-based strain acquisition includes the high spatiotemporal resolution of signals and the ability of full-surface strain scanning.The introduction to the DFOSS technology yields a better understanding of the rock damage process under uniaxial compression.展开更多
Confinement of rock bolts by the surrounding rock formation has long been recognized as a positive contributor to the pull-out behavior,yet only a few experimental works and analytical models have been reported,most o...Confinement of rock bolts by the surrounding rock formation has long been recognized as a positive contributor to the pull-out behavior,yet only a few experimental works and analytical models have been reported,most of which are based on the global rock bolt response evaluated in pull-out tests.This paper presents a laboratory experimental setup aiming to capture the rock formation effect,while using distributed fiber optic sensing to quantify the effect of the confinement and the reinforcement pull-out behavior on a more local level.It is shown that the behavior along the sample itself varies,with certain points exhibiting stress drops with crack formation.Some edge effects related to the kinematic freedom of the grout to dilate are also observed.Regardless,it was found that the mid-level response is quite similar to the average response along the sample.The ability to characterize the variation of the response along the sample is one of the many advantages high-resolution fiber optic sensing allows in such investigations.The paper also offers a plasticity-based hardening load transfer function,representing a"slice"of the anchor.The paper describes in detail the development of the model and the calibration/determination of its parameters.The suggested model captures well the coupled behavior in which the pull-out process leads to an increase in the confining stress due to dilative behavior.展开更多
With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the ac...With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.展开更多
This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy model...This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation.We provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH IoT.Moreover,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)system.These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability level.Our results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.展开更多
A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion mo...A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion model （BEM） of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing （DCS） problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing （CS） theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62171249)the National Key R&D Program of China(Grant No.2021YFA1402102)the Tsinghua Initiative Scientific Research Program.
文摘Distributed fiber-optic sensing(DFOS)can turn the worldwide fiber network into a sensing array,which may immensely extend the sensing range and approaches for hazard assessment,earth observation,and human activity measurement.However,most existing DFOS schemes cannot simultaneously give dual attention to the detection ability(for example,sensing distance)and multipoint localizing function.A mirror-image correlation method is proposed and can precisely extract the time delay between two original signals from their composite detected signal.This method enables the distributed vibration sensing function of the laser interferometer and maintains its high detection ability.We demonstrate its feasibility by simultaneously localizing multiple knocking vibrations on a 250-km round-trip fiber and distinguishing traffic vibrations at two urban positions in a field test.The localizing precision is analyzed and satisfies the requirements for fiber network sensing.
基金support provided by Science Foundation Ireland Frontiers for the Future Programme,21/FFP-P/10090.
文摘Curved geostructures,such as tunnels,are commonly encountered in geotechnical engineering and are critical to maintaining structural stability.Ensuring their proper performance through field monitoring during their service life is essential for the overall functionality of geotechnical infrastructure.Distributed Brillouin sensing(DBS)is increasingly applied in geotechnical projects due to its ability to acquire spatially continuous strain and temperature distributions over distances of up to 150 km using a single optical fibre.However,limited by the complex operations of distributed optic fibre sensing(DFOS)sensors in curved structures,previous reports about exploiting DBS in geotechnical structural health monitoring(SHM)have mostly been focused on flat surfaces.The lack of suitable DFOS installation methods matched to the spatial characteristics of continuous monitoring is one of the major factors that hinder the further application of this technique in curved structures.This review paper starts with a brief introduction of the fundamental working principle of DBS and the inherent limitations of DBS being used on monitoring curved surfaces.Subsequently,the state-of-the-art installation methods of optical fibres in curved structures are reviewed and compared to address the most suitable scenario of each method and their advantages and disadvantages.The installation challenges of optical fibres that can highly affect measurement accuracy are also discussed in the paper.
基金Supported by the National High-Tech Research Project(GJSCB-HFGDY-2024-004)National Natural Science Foundation of China(12402305)+2 种基金Postdoctoral Fellowship Program of CPSF(GZC20232200)China Postdoctoral Science Foundation(2024M762703)Sichuan Science and Technology Program(2025ZNSFSC1352)。
文摘The Carter model is used to characterize the dynamic behaviors of fracture growth and fracturing fluid leakoff.A thermo-fluid coupling temperature response forward model is built considering the fluid flow and heat transfer in wellbore,fracture and reservoir.The influences of fracturing parameters and fracture parameters on the responses of distributed temperature sensing(DTS)are analyzed,and a diagnosis method of fracture parameters is presented based on the simulated annealing algorithm.A field case study is introduced to verify the model’s reliability.Typical V-shaped characteristics can be observed from the DTS responses in the multi-cluster fracturing process,with locations corresponding to the hydraulic fractures.The V-shape depth is shallower for a higher injection rate and longer fracturing and shut-in time.Also,the V-shape is wider for a higher fracture-surface leakoff coefficient,longer fracturing time and smaller fracture width.Additionally,the cooling effect near the wellbore continues to spread into the reservoir during the shut-in period,causing the DTS temperature to decrease instead of rise.Real-time monitoring and interpretation of DTS temperature data can help understand the fracture propagation during fracturing operation,so that immediate measures can be taken to improve the fracturing performance.
基金supported by the National Key R&D Program of China under grant No.2021YFA0716800。
文摘Distributed acoustic sensing(DAS)is increasingly used in seismic exploration owing to its wide frequency range,dense sampling and real-time monitoring.DAS radiation patterns help to understand angle response of DAS records and improve the quality of inversion and imaging.In this paper,we solve the 3D vertical transverse isotropic(VTI)Christoffel equation and obtain the analytical,frst-order,and zero-order Taylor expansion solutions that represent P-,SV-,and SH-wave phase velocities and polarization vectors.These analytical and approximated solutions are used to build the P/S plane-wave expression identical to the far-feld term of seismic wave,from which the strain rate expressions are derived and DAS radiation patterns are thus extracted for anisotropic P/S waves.We observe that the gauge length and phase angle terms control the radiating intensity of DAS records.Additionally,the Bond transformation is adopted to derive the DAS radiation patterns in title transverse isotropic(TTI)media,which exhibits higher complexity than that of VTI media.Several synthetic examples demonstrate the feasibility and effectiveness of our theory.
基金supported by the National Natural Science Foundation of China(Grant Nos.42225702 and 42077235)the Natural Science Foundation of Jiangsu Province(Grant No.BK20211086)the open fund of the Key Laboratory of Earth Fissures Geological Disaster,Ministry of Natural Resources.
文摘The distribution of shear-wave velocities in the subsurface is generally used to assess the potential forseismic liquefaction and soil amplification effects and to classify seismic sites. Newly developeddistributed acoustic sensing (DAS) technology enables estimation of the shear-wave distribution as ahigh-density seismic observation system. This technology is characterized by low maintenance costs,high-resolution outputs, and real-time data transmission capabilities, albeit with the challenge ofmanaging massive data generation. Rapid and efficient interpretation of data is the key to advancingapplication of the DAS technology. In this study, field tests were carried out to record ambient noise overa short period using DAS technology, from which the surface-wave dispersion curves were extracted. Inorder to reduce the influence of directional effects on the results, an unsupervised clustering method isused to select appropriate clusters to extract the Green's function. A combination of a genetic algorithmand Monte Carlo (GA-MC) simulation is proposed to invert the subsurface velocity structure. Thestratigraphic profiles obtained by the GA-MC method are in agreement with the borehole profiles.Compared to other methods, the proposed optimization method not only improves the solution qualitybut also reduces the solution time.
基金support from the Open Research Project Programme of the State Key Laboratory of Internet of Things for Smart City,University of Macao (Grant No.SKL-IoTSC (UM)-2021-2023/ORPF/A19/2022)the General Research Fund project from Research Grants Council of Hong Kong Special Administrative Region Government of China (Grant No.15214722)the Start-up Fund from The Hong Kong Polytechnic University (Grant No.BD88).
文摘Despite the extensive use of distributed fiber optic sensing(DFOS)in monitoring underground structures,its potential in detecting structural anomalies,such as cracks and cavities,is still not fully understood.To contribute to the identification of defects in underground structures,this study conducted a four-point bending test of a reinforced concrete(RC)beam and uniaxial loading tests of an RC specimen with local cavities.The experimental results revealed the disparity in DFOS strain spike profiles between these two structural anomalies.The effectiveness of DFOS in the quantification of crack opening displacement(COD)was also demonstrated,even in cases where perfect bonding was not achievable between the cable and structures.In addition,DFOS strain spikes observed in two diaphragm wall panels of a twin circular shaft were also reported.The most probable cause of those spikes was identified as the mechanical behavior associated with local concrete contamination.With the utilization of the strain profiles obtained from laboratory tests and field monitoring,three types of multi-classifiers,based on support vector machine(SVM),random forest(RF),and backpropagation neural network(BP),were employed to classify strain profiles,including crack-induced spikes,non-crack-induced spikes,and non-spike strain profiles.Among these classifiers,the SVM-based classifier exhibited superior performance in terms of accuracy and model robustness.This finding suggests that the SVM-based classifier holds promise as a potential solution for the automatic detection and classification of defects in underground structures during long-term monitoring.
基金supported by the National Natural Science Foundation of China(Grant Nos.U23A20375 and 62075151)the National Key Research and Development Program of China(Grant No.202103021223042).
文摘The principle of optical time-domain reflection localization limits the sensing spatial resolution of Raman distributed optical fiber sensing.We provide a solution for a Raman distributed optical fiber sensing system with kilometer-level sensing distance and submeter spatial resolution.Based on this,we propose a Raman distributed optical fiber sensing scheme based on chaotic pulse cluster demodulation.Chaotic pulse clusters are used as the probe signal,in preference to conventional pulsed or chaotic single-pulse lasers.Furthermore,the accurate positioning of the temperature variety region along the sensing fiber can be realized using chaotic pulse clusters.The proposed demodulation scheme can enhance the signal-to-noise ratio by improving the correlation between the chaotic reference and the chaotic Raman anti-Stokes scattering signals.The experiment achieved a sensing spatial resolution of 30 cm at a distributed temperature-sensing distance of∼6.0 km.Furthermore,we explored the influence of chaotic pulse width and detector bandwidth on the sensing spatial resolution.In addition,the theoretical experiments proved that the sensing spatial resolution in the proposed scheme was independent of the pulse width and sensing distance.
基金Supported by Key Laboratory of Space Active Optical-Electro Technology of Chinese Academy of Sciences(2021ZDKF4)。
文摘Phase-sensitive Optical Time-Domain Reflectometer(φ-OTDR)technology facilitates the real-time detection of vibration events along fiber optic cables by analyzing changes in Rayleigh scattering signals.This technology is widely used in applications such as intrusion monitoring and structural health assessments.Traditional signal processing methods,such as Support Vector Machines(SVM)and K-Nearest Neighbors(KNN),have limitations in feature extraction and classification in complex environments.Conversely,a single deep learning model often struggles with capturing long time-series dependencies and mitigating noise interference.In this study,we propose a deep learning model that integrates Convolutional Neural Network(CNN),Long Short-Term Memory Network(LSTM),and Transformer modules,leveraging φ-OTDR technology for distributed fiber vibration sensing event recognition.The hybrid model combines the CNN's capability to extract local features,the LSTM's ability to model temporal dynamics,and the Transformer's proficiency in capturing global dependencies.This integration significantly enhances the accuracy and robustness of event recognition.In experiments involving six types of vibration events,the model consistently achieved a validation accuracy of 0.92,and maintained a validation loss of approximately 0.2,surpassing other models,such as TAM+BiLSTM and CNN+CBAM.The results indicate that the CNN+LSTM+Transformer model is highly effective in handling vibration signal classification tasks in complex scenarios,offering a promising new direction for the application of fiber optic vibration sensing technology.
基金funding support from the National Natural Science Foundation of China(Grant No.52204030)Youth Innovation and Technology Support Program for Higher Education Institutions of Shandong Province,China(Grant No.2022KJ070)the National Natural Science Foundation of China Enterprise Innovation and Development Joint Fund Project(Grant No.U19B6003).
文摘Fiber-optic distributed strain sensing(FO-DSS)has been successful in monitoring strain changes along horizontal wellbores in hydraulically fractured reservoirs.However,the mechanism driving the various FO-DSS responses associated with near-wellbore hydraulic fracture properties is still unclear.To address this knowledge gap,we use coupled wellbore-reservoir-geomechanics simulations to study measured strain-change behavior and infer hydraulic fracture characteristics.The crossflow among fractures is captured through explicit modeling of the transient wellbore flow.In addition,local grid refinement is applied to accurately capture strain changes along the fiber.A Base Case model was designed with four fractures of varying properties,simulating strain change signals when the production well is shut-in for 10 d after 240 d of production and reopened for 2 d.Strain-pressure plots for different fracture clusters were used to gain insights into inferring fracture properties using DSS data.When comparing the model with and without the wellbore,distinct strain change signals were observed,emphasizing the importance of incorporating the wellbore in FO-DSS modeling.The effects of fracture spacing and matrix permeability on strain change signals were thoroughly investigated.The results of our numerical study can improve the understanding of the relation between DSS signals and fracture hydraulic properties,thus maximizing the value of the dataset for fracture diagnostics and characterization.
基金supported by the National Key R&D Program of China(2022YFC3102202)the Chinese Academy of Sciences (CAS) Project for Young Scientists in Basic Research (YSBR-020)。
文摘Distributed Acoustic Sensing(DAS) is an emerging technique for ultra-dense seismic observation, which provides a new method for high-resolution sub-surface seismic imaging. Recently a large number of linear DAS arrays have been used for two-dimensional S-wave near-surface imaging in urban areas. In order to explore the feasibility of three-dimensional(3D) structure imaging using a DAS array, we carried out an active source experiment at the Beijing National Earth Observatory. We deployed a 1 km optical cable in a rectangular shape, and the optical cable was recast into 250 sensors with a channel spacing of 4 m. The DAS array clearly recorded the P, S and surface waves generated by a hammer source. The first-arrival P wave travel times were first picked with a ShortTerm Average/Long-Term Average(STA/LTA) method and further manually checked. The P-wave signals recorded by the DAS are consistent with those recorded by the horizontal components of short-period seismometers. At shorter source-receiver distances, the picked P-wave arrivals from the DAS recording are consistent with vertical component recordings of seismometers, but they clearly lag behind the latter at greater distances.This is likely due to a combination of the signal-to-noise ratio and the polarization of the incoming wave. Then,we used the Tomo DD software to invert the 3D P-wave velocity structure for the uppermost 50 m with a resolution of 10 m. The inverted P-wave velocity structures agree well with the S-wave velocity structure previously obtained through ambient noise tomography. Our study indicates the feasibility of 3D near-surface imaging with the active source and DAS array. However, the inverted absolute velocity values at large depths may be biased due to potential time shifts between the DAS recording and seismometer at large source-receiver distances.
基金supported by the 34th Research Institute of CETC Funding(Grant No.K134002021S604)the New Technology Research University Cooperation Project of the 34th Research Institute of CETC(Grant No.2021-1200-05-001900).
文摘Presented here is long-range distributed vibration sensing based on internal-modulation optical frequency domain reflectometry(OFDR).In the proposed system with internal modulation,a silicon-based photonic-chip laser is used as the laser source,and by controlling the output voltage curve of an arbitrary waveform generator to induce temperature change in the external cavity of the laser,a 10-GHz optical frequency tuning range is achieved.The complexity of the proposed internal-modulation system is lower than that of the traditional external-modulation OFDR system that combines a narrow-linewidth laser with a single-sideband modulator to achieve wavelength tuning.Cross-correlation analysis is used as a sensing mechanism to evaluate the similarity between Rayleigh scatter signals and to achieve vibration event localization.Experimental comparison is made of the vibration sensing performance of the external-and internal-modulation systems,and for a vibration event generated at a distance of 100.95 km,they locate it with a sensing spatial resolution of 43.0 m and 16.8 m,respectively.The results indicates that the proposed distributed vibration sensing based on internal modulation has better sensing performance and lower complexity compared to the traditional external-modulation system.In addition,the proposed system is single-ended and involves no optical amplification,which makes it very suitable for ultra-long-range sensing.
基金Supported by the Scientific Research and Technology Development Project of Petrochina Southwest Oil and Gas Field Company(20230307-02)。
文摘The distributed fiber optic sensing system,known for its high sensitivity and wide-ranging measurement capabilities,has been widely used in monitoring underground gas pipelines.It primarily serves to perceive vibration signals induced by external events and to effectively provide early warnings of potential intrusion activities.Due to the complexity and diversity of external intrusion events,traditional deep learning methods can achieve event recognition with an average accuracy exceeding 90%.However,these methods rely on large-scale datasets,leading to significant time and labor costs during the data collection process.Additionally,traditional methods perform poorly when faced with the scarcity of low-frequency event samples,making it challenging to address these rare occurrences.To address this issue,this paper proposes a small-sample learning model based on triplet learning for intrusion event recognition.The model employs a 6-way 20-shot support set configuration and utilizes the KNN clustering algorithm to assess the model's performance.Experimental results indicate that the model achieves an average accuracy of 91.6%,further validating the superior performance of the triplet learning model in classifying external intrusion events.Compared to traditional methods,this approach not only effectively reduces the dependence on large-scale datasets but also better addresses the classification of low-frequency event samples,demonstrating significant application potential.
基金Supported by National Key Research and DevelopmentProgram of China(2016YFB0900100).
文摘The rapid development of Internet Plus Smart Energy requires further strengthening of three kinds of interconnections based on traditional power systems: physical interconnection, information interconnection, and commercial interconnection. Due to the integration of renewable energy, the reform of the electricity market, and the deployment of the Smart Grid, a large amount of data will be generated. Thus, it is necessary to establish a Ubiquitous Power Internet of Things (UPIoT) to realize connections among people and things, things and things, and people and people in power systems. This paper studies the concept and architecture of the UPIoT and indicates the deployment of the perception layer and network layer as the key to building UPIoT in the initial stage. As UPIoT tends to cover a wide area and produce massive and distributed data, signal processing and data analytics theories and techniques are needed to handle the data and observe the state of the large-scale system. Further studies on distributed sensing and cooperative estimation theories and techniques of UPIoT are also required. Finally, the application prospects of UPIoT and the directions for future research are discussed.
基金partially supported by the Geothermal Technologies Office of the USA Department of Energy (No. DE-EE0006760)the State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodey and Geophysics, Chinese Academy of Sciences (No. SKLGED2019-5-4-E)
文摘Distributed acoustic sensing(DAS) is one recently developed seismic acquisition technique that is based on fiber-optic sensing. DAS provides dense spatial spacing that is useful to image shallow structure with surface waves.To test the feasibility of DAS in shallow structure imaging,the PoroTomo team conducted a DAS experiment with the vibroseis truck T-Rex in Brady’s Hot Springs, Nevada, USA.The Rayleigh waves excited by the vertical mode of the vibroseis truck were analyzed with the Multichannel Analysis of Surface Waves(MASW) method. Phase velocities between5 and 20 Hz were successfully extracted for one segment of cable and were employed to build a shear-wave velocity model for the top 50 meters. The dispersion curves obtained with DAS agree well with the ones extracted from co-located geophones data and from the passive source Noise Correlation Functions(NCF). Comparing to the co-located geophone array, the higher sensor density that DAS arrays provides help reducing aliasing in dispersion analysis, and separating different surface wave modes. This study demonstrates the feasibility and advantage of DAS in imaging shallow structure with surface waves.
基金the National Basic Research Program of China，the National Natural Science Foundation of China，the open research fund of National Mobile Communications Research Laboratory,Southeast University，the Postdoctoral Science Foundation of Jiangsu Province，the University Natural Science Research Program of Jiangsu Province，the Basic Research Program of Jiangsu Province (Natural Science Foundation)
文摘Wireless Sensor Networks(WSN) are mainly characterized by a potentially large number of distributed sensor nodes which collectively transmit information about sensed events to the sink.In this paper,we present a Distributed Wavelet Basis Generation(DWBG) algorithm performing at the sink to obtain the distributed wavelet basis in WSN.And on this basis,a Wavelet Transform-based Distributed Compressed Sensing(WTDCS) algorithm is proposed to compress and reconstruct the sensed data with spatial correlation.Finally,we make a detailed analysis of relationship between reconstruction performance and WTDCS algorithm parameters such as the compression ratio,the channel Signal-to-Noise Ratio(SNR),the observation noise power and the correlation decay parameter by simulation.The simulation results show that WTDCS can achieve high performance in terms of energy and reconstruction accuracy,as compared to the conventional distributed wavelet transform algorithm.
基金support from the Institute of Crustal Dynamics,China Earthquake Administration(Grant No.ZDJ2016-20 and ZDJ2019-15)。
文摘This paper investigates the deformation and fracture propagation of sandstone specimen under uniaxial compression using the distributed fiber optic strain sensing(DFOSS)technology.It shows that the DFOSS-based circumferential strains are in agreement with the data monitored with the traditional strain gage.The DFOSS successfully scans the full-field view of axial and circumferential strains on the specimen surface.The spatiotemporal strain measurement based on DFOSS manifests crack closure and elastoplastic deformation,detects initialization of microcrack nucleation,and identifies strain localization within the specimen.The DFOSS well observes the effects of rock heterogeneity on rock deformation.The advantage of DFOSS-based strain acquisition includes the high spatiotemporal resolution of signals and the ability of full-surface strain scanning.The introduction to the DFOSS technology yields a better understanding of the rock damage process under uniaxial compression.
基金funding support from the Israeli Ministry of Housing and Construction(Grant No.2028286).
文摘Confinement of rock bolts by the surrounding rock formation has long been recognized as a positive contributor to the pull-out behavior,yet only a few experimental works and analytical models have been reported,most of which are based on the global rock bolt response evaluated in pull-out tests.This paper presents a laboratory experimental setup aiming to capture the rock formation effect,while using distributed fiber optic sensing to quantify the effect of the confinement and the reinforcement pull-out behavior on a more local level.It is shown that the behavior along the sample itself varies,with certain points exhibiting stress drops with crack formation.Some edge effects related to the kinematic freedom of the grout to dilate are also observed.Regardless,it was found that the mid-level response is quite similar to the average response along the sample.The ability to characterize the variation of the response along the sample is one of the many advantages high-resolution fiber optic sensing allows in such investigations.The paper also offers a plasticity-based hardening load transfer function,representing a"slice"of the anchor.The paper describes in detail the development of the model and the calibration/determination of its parameters.The suggested model captures well the coupled behavior in which the pull-out process leads to an increase in the confining stress due to dilative behavior.
基金the financial support of this work by the National Natural Science Foundation of Hebei Province China under Grant E2020208052.
文摘With the development of multi-signal monitoring technology,the research on multiple signal analysis and processing has become a hot subject.Mechanical equipment often works under variable working conditions,and the acquired vibration signals are often non-stationary and nonlinear,which are difficult to be processed by traditional analysis methods.In order to solve the noise reduction problem of multiple signals under variable speed,a COT-DCS method combining the Computed Order Tracking(COT)based on Chirplet Path Pursuit(CPP)and Distributed Compressed Sensing(DCS)is proposed.Firstly,the instantaneous frequency(IF)is extracted by CPP,and the speed is obtained by fitting.Then,the speed is used for equal angle sampling of time-domain signals,and angle-domain signals are obtained by COT without a tachometer to eliminate the nonstationarity,and the angledomain signals are compressed and reconstructed by DCS to achieve noise reduction of multiple signals.The accuracy of the CPP method is verified by simulated,experimental signals and compared with some existing IF extraction methods.The COT method also shows good signal stabilization ability through simulation and experiment.Finally,combined with the comparative test of the other two algorithms and four noise reduction effect indicators,the COT-DCS based on the CPP method combines the advantages of the two algorithms and has better noise reduction effect and stability.It is shown that this method is an effective multi-signal noise reduction method.
基金This work has been supported by the National Key R&D Program of China(Grant No.2018YFE0207600)EPSRC Research Grant(EP/K033700/1,EP/K033166/1)+2 种基金the Natural Science Foundation of China(61671046,61911530216,U1834210)the Beijing Natural Science Foundation(4182050)the FWO(Grants G0A2617N and G093817N).
文摘This paper advocates the use of the distributed compressed sensing(DCS)paradigm to deploy energy harvesting(EH)Internet of Thing(IoT)devices for energy self-sustainability.We consider networks with signal/energy models that capture the fact that both the collected signals and the harvested energy of different devices can exhibit correlation.We provide theoretical analysis on the performance of both the classical compressive sensing(CS)approach and the proposed distributed CS(DCS)-based approach to data acquisition for EH IoT.Moreover,we perform an in-depth comparison of the proposed DCSbased approach against the distributed source coding(DSC)system.These performance characterizations and comparisons embody the effect of various system phenomena and parameters including signal correlation,EH correlation,network size,and energy availability level.Our results unveil that,the proposed approach offers significant increase in data gathering capability with respect to the CS-based approach,and offers a substantial reduction of the mean-squared error distortion with respect to the DSC system.
基金Supported by the National Natural Science Foundation of China(61077022)
文摘A sparse channel estimation method is proposed for doubly selective channels in multiple- input multiple-output （ MIMO ） orthogonal frequency division multiplexing （ OFDM ） systems. Based on the basis expansion model （BEM） of the channel, the joint-sparsity of MIMO-OFDM channels is described. The sparse characteristics enable us to cast the channel estimation as a distributed compressed sensing （DCS） problem. Then, a low complexity DCS-based estimation scheme is designed. Compared with the conventional compressed channel estimators based on the compressed sensing （CS） theory, the DCS-based method has an improved efficiency because it reconstructs the MIMO channels jointly rather than addresses them separately. Furthermore, the group-sparse structure of each single channel is also depicted. To effectively use this additional structure of the sparsity pattern, the DCS algorithm is modified. The modified algorithm can further enhance the estimation performance. Simulation results demonstrate the superiority of our method over fast fading channels in MIMO-OFDM systems.
