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
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
Railroad condition monitoring is paramount due to frequent passage through densely populated regions.This significance arises from the potential consequences of accidents such as train derailments,hazardous materials ...Railroad condition monitoring is paramount due to frequent passage through densely populated regions.This significance arises from the potential consequences of accidents such as train derailments,hazardous materials leaks,or collisions which may have far-reaching impacts on communities and the surrounding areas.As a solution to this issue,the use of distributed acoustic sensing(DAS)-fiber optic cables along railroads provides a feasible tool for monitoring the health of these extended infrastructures.Nevertheless,analyzing DAS data to assess railroad health or detect potential damage is a challenging task.Due to the large amount of data generated by DAS,as well as the unstructured patterns and substantial noise present,traditional analysis methods are ineffective in interpreting this data.This paper introduces a novel approach that harnesses the power of deep learning through a combination of CNNs and LSTMs,augmented by sliding window techniques(CNN-LSTM-SW),to advance the state-of-the-art in the railroad condition monitoring system.As well as it presents the potential for DAS and fiber optic sensing technologies to revolutionize the proposed CNN-LSTM-SW model to detect conditions along the rail track networks.Extracting insights from the data of High tonnage load(HTL)-a 4.16 km fiber optic and DAS setup,we were able to distinguish train position,normal condition,and abnormal conditions along the railroad.Notably,our investigation demonstrated that the proposed approaches could serve as efficient techniques for processing DAS signals and detecting the condition of railroad infrastructures at any remote distance with DAS-Fiber optic cable setup.Moreover,in terms of pinpointing the train's position,the CNN-LSTM architecture showcased an impressive 97%detection rate.Applying a sliding window,the CNN-LSTM labeled data,the remaining 3%of misclassified labels have been improved dramatically by predicting the exact locations of each type of condition.Altogether,these proposed models exhibit promising potential for accurately identifying various railroad conditions,including anomalies and discrepancies that warrant thorough exploration.展开更多
Distributed fiber sensors based on forward stimulated Brillouin scattering(F-SBS)have attracted special attention because of their capability to detect the acoustic impedance of liquid material outside fiber.However,t...Distributed fiber sensors based on forward stimulated Brillouin scattering(F-SBS)have attracted special attention because of their capability to detect the acoustic impedance of liquid material outside fiber.However,the reported results were based on the extraction of a 1st-order local spectrum,causing the sensing distance to be restricted by pump depletion.Here,a novel post-processing technique was proposed for distributed acoustic impedance sensing by extracting the 2nd-order local spectrum,which is beneficial for improving the sensing signal-to-noise ratio(SNR)significantly,since its pulse energy penetrates into the fiber more deeply.As a proof-of-concept,distributed acoustic impedance sensing along~1630 m fiber under moderate spatial resolution of~20 m was demonstrated.展开更多
针对光纤直接探测声波时灵敏度低的问题,提出一种超弱光纤光栅缠绕式薄壁圆筒的声波传感探头增敏方法。理论分析了薄壁圆筒半径、壁厚、弹性模量等参数对光纤探头声增敏的影响,仿真分析了光纤缠绕方式对探头谐振频率的影响,优化设计了...针对光纤直接探测声波时灵敏度低的问题,提出一种超弱光纤光栅缠绕式薄壁圆筒的声波传感探头增敏方法。理论分析了薄壁圆筒半径、壁厚、弹性模量等参数对光纤探头声增敏的影响,仿真分析了光纤缠绕方式对探头谐振频率的影响,优化设计了弹性管式探头的结构。搭建了基于超弱光纤光栅声波传感系统,并对探头的声敏特性进行了测试。实验结果显示,探头的声压灵敏度最高可达到6.39746 rad/Pa(-103.8798 dB re rad/μPa),在1000~2000 Hz的平均声压灵敏度为3.30341 rad/Pa(-109.6208 dB re rad/μPa);相较于未增敏的裸光纤,探头的平均声压灵敏度提高了约31 dB。展开更多
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supported by funding from The Association of American Railroads(AAR)-MxV Rail(Award number:21-0825-007538)Impact Area Accelerator Award Grant 2023 from Georgia Southern University's Office of Research.
文摘Railroad condition monitoring is paramount due to frequent passage through densely populated regions.This significance arises from the potential consequences of accidents such as train derailments,hazardous materials leaks,or collisions which may have far-reaching impacts on communities and the surrounding areas.As a solution to this issue,the use of distributed acoustic sensing(DAS)-fiber optic cables along railroads provides a feasible tool for monitoring the health of these extended infrastructures.Nevertheless,analyzing DAS data to assess railroad health or detect potential damage is a challenging task.Due to the large amount of data generated by DAS,as well as the unstructured patterns and substantial noise present,traditional analysis methods are ineffective in interpreting this data.This paper introduces a novel approach that harnesses the power of deep learning through a combination of CNNs and LSTMs,augmented by sliding window techniques(CNN-LSTM-SW),to advance the state-of-the-art in the railroad condition monitoring system.As well as it presents the potential for DAS and fiber optic sensing technologies to revolutionize the proposed CNN-LSTM-SW model to detect conditions along the rail track networks.Extracting insights from the data of High tonnage load(HTL)-a 4.16 km fiber optic and DAS setup,we were able to distinguish train position,normal condition,and abnormal conditions along the railroad.Notably,our investigation demonstrated that the proposed approaches could serve as efficient techniques for processing DAS signals and detecting the condition of railroad infrastructures at any remote distance with DAS-Fiber optic cable setup.Moreover,in terms of pinpointing the train's position,the CNN-LSTM architecture showcased an impressive 97%detection rate.Applying a sliding window,the CNN-LSTM labeled data,the remaining 3%of misclassified labels have been improved dramatically by predicting the exact locations of each type of condition.Altogether,these proposed models exhibit promising potential for accurately identifying various railroad conditions,including anomalies and discrepancies that warrant thorough exploration.
基金Project supported by the Sichuan Science and Technology Program(Grant No.2019YJ0530)Scientific Research Fund of Sichuan Provincial Education Department,China(Grant No.18ZA0401)the National Natural Science Foundation of China(Grant No.61205079).
文摘Distributed fiber sensors based on forward stimulated Brillouin scattering(F-SBS)have attracted special attention because of their capability to detect the acoustic impedance of liquid material outside fiber.However,the reported results were based on the extraction of a 1st-order local spectrum,causing the sensing distance to be restricted by pump depletion.Here,a novel post-processing technique was proposed for distributed acoustic impedance sensing by extracting the 2nd-order local spectrum,which is beneficial for improving the sensing signal-to-noise ratio(SNR)significantly,since its pulse energy penetrates into the fiber more deeply.As a proof-of-concept,distributed acoustic impedance sensing along~1630 m fiber under moderate spatial resolution of~20 m was demonstrated.
文摘针对光纤直接探测声波时灵敏度低的问题,提出一种超弱光纤光栅缠绕式薄壁圆筒的声波传感探头增敏方法。理论分析了薄壁圆筒半径、壁厚、弹性模量等参数对光纤探头声增敏的影响,仿真分析了光纤缠绕方式对探头谐振频率的影响,优化设计了弹性管式探头的结构。搭建了基于超弱光纤光栅声波传感系统,并对探头的声敏特性进行了测试。实验结果显示,探头的声压灵敏度最高可达到6.39746 rad/Pa(-103.8798 dB re rad/μPa),在1000~2000 Hz的平均声压灵敏度为3.30341 rad/Pa(-109.6208 dB re rad/μPa);相较于未增敏的裸光纤,探头的平均声压灵敏度提高了约31 dB。
