3D near-surface P-wave velocity structure imaging with Distributed Acoustic Sensing and electric hammer source

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.

Imaging shallow structure with active-source surface wave signal recorded by distributed acoustic sensing arrays

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.

Distributed analysis of forward stimulated Brillouin scattering for acoustic impedance sensing by extraction of a 2nd-order local spectrum

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.

Remote condition monitoring of rail tracks using distributed acoustic sensing(DAS):A deep CNN-LSTM-SW based model

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.

Feature Extraction and Identification in Distributed Optical-Fiber Vibration Sensing System for Oil Pipeline Safety Monitoring

High sensitivity of a distributed optical-fiber vibration sensing （DOVS） system based on the phase-sensitivity optical time domain reflectometry （Ф-OTDR） technology also brings in high nuisance alarm rates （NARs） in real applications. In this paper, feature extraction methods of wavelet decomposition （WD） and wavelet packet decomposition （WPD） are comparatively studied for three typical field testing signals, and an artificial neural network （ANN） is built for the event identification. The comparison results prove that the WPD performs a little better than the WD for the DOVS signal analysis and identification in oil pipeline safety monitoring. The identification rate can be improved up to 94.4%, and the nuisance alarm rate can be effectively controlled as low as 5.6% for the identification network with the wavelet packet energy distribution features.

Distributed Acoustic Sensing With Michelson Interferometer Demodulation

The distributed acoustic sensing （DAS） has been extensively studied and widely used. A distributed acoustic sensing system based on the unbalanced Michelson interferometer with phase generated carrier （PGC） demodulation was designed and tested. The system could directly obtain the phase, amplitude, frequency response, and location information of sound wave at the same time and measurement at all points along the sensing fiber simultaneously. Experiments showed that the system successfully measured the acoustic signals with a phase-pressure sensitivity about -148dB （re rad/μPa） and frequency response ripple less than 1.5 dB. The further field experiment showed that the system could measure signals at all points along the sensing fiber simultaneously.

分布式光纤压裂监测技术研究及应用

随着页岩油气等非常规油气勘探开发走向深层以及日趋精细化,微地震等传统压裂监测手段愈发无法满足压裂效果评价的精度要求。作为分布式光纤传感(DFOS)技术的最新研究进展,分布式光纤声波传感(DAS)近年来成为国内外压裂监测方向的研究热点。文中介绍了井下光缆布设、光缆深度标定、DAS信号处理、压裂期间DAS响应提取和解释的方法,并结合涪陵地区某页岩气井组的套管外光纤压裂监测实例,展示了DAS技术给非常规油气水力压裂监测带来的新视角和新认识,为我国压裂监测新技术的发展和推广应用提供参考。

分布式光纤传感技术在水力压裂中的研究进展

分布式光纤传感技术作为最新的水力压裂监测技术,应用于各大油田的水力压裂过程中,并且能够实现实时监测,已取得了显著的应用效果。为使业界进一步了解不同类型传感技术的基本原理、理论模型研究进展、现场应用情况,从分布式光纤温度传感技术和声波传感技术在水力压裂过程中的监测基本原理出发,系统总结了各类传感技术的理论模型研究进展和在产液剖面、裂缝扩展形态监测等方面的应用现状,最后提出了未来分布式光纤传感技术的发展方向。研究结果表明:①分布式光纤传感技术可以利用温度或者声波信号转换得到周围环境温度或应变的变化情况,从而实现水力压裂过程中的实时监测;②与分布式光纤声波传感技术相比,温度传感技术的相关理论模型相对较为成熟,能够实现产液剖面及裂缝形态的相关计算;③分布式光纤传感技术主要用于水力压裂过程中压裂液的注入、裂缝扩展等方面的监测。结论认为:分布式光纤传感技术可以有效地推动中国非常规储层的勘探和开发,同时提高水力压裂效果评价技术水平,这对中国油气行业的可持续发展具有重要推动作用。

采用非稳相偏振滤波的DAS-VSP数据P/S波分离方法及其应用

分布式光纤声学传感(DAS)因成本低、易布设以及高密度采样等优势正成为重要的地震观测技术,尤其是越来越多地与垂直地震剖面(VSP)结合,用于主动地震勘探或被动地震监测.DAS传感器通过感知弹性波场产生的轴向应变或应变速率来观测地震波场振动.然而,目前单分量DAS-VSP数据未完整地记录地下弹性波场的三维矢量振动信号,因此如何从中分离出P波或S波用于后续地震成像与参数反演是重要且很有挑战的课题.以弹性波传播理论为基础,本文根据P波和S波的频散关系估算接收点处各自的偏振方向,通过随频率和空间位置变化的偏振滤波实现DAS-VSP数据的P/S波分离.理论模型合成数据与东海实际DAS-VSP数据实验结果表明,该方法能有效地将P波和S波信号从单分量DAS-VSP数据中分离出来,可为后续纵横波速度反演、PP与PS波成像提供关键的数据预条件处理.

分布式光纤监测技术在水平井分段压裂中的应用

水平井分段压裂过程中,涉及大量繁杂的数据监测与处理工作,同时,压裂过程的不确定性给实时监测带来了更大的难度。传统的井下监测方式存在监测精确度低、受电磁干扰、成本高昂等问题,分布式光纤传感技术的产生,为这些亟待解决的难题提供了新的解决思路。分布式光纤传感技术以其成本低、耐高温、性质稳定、不受电磁干扰等优势,逐渐在油田得到应用。对水平井分段压裂的应用现状进行研究,分析了分布式光纤传感技术原理,总结了分布式光纤的井下安装方式,归纳了分布式光纤传感技术在水平井分段压裂中的应用现状,最后提出了分布式光纤监测技术的发展建议。研究认为:目前,分布式光纤传感技术已受到广泛关注,将成为井下监测的重要工具;分布式光纤传感在水平井分段压裂中的应用主要有压裂液注入分布诊断、人工裂缝起裂及延伸诊断、封隔器泄漏监测和DTS/DAS数据定量解释;对未来温度和声学监测数据的解释方法将向定量解释的应用方向发展。

基于分布式光纤声传感火车噪声属性的车型识别及运行规律

火车运行时产生强烈的移动噪声传播至地下空间,形成一种绿色环保、强能量的人工地震波.由于载荷系统形态基本固定,火车运行时间规律,震源重复性高,这种火车震源被广泛应用于轨道安全监测、近地表成像和城市地下空间探测等领域.分布式光纤声传感是一种新兴的地震采集技术,拥有密集采样、布设简单、抗电磁干扰能力强等优点,近年来在城市交通事件微震监测、裂隙探测和地下空间成像领域逐步应用.本研究基于分布式光纤声传感技术获得的秦皇岛试验场火车移动噪声数据,拾取了30个火车交通事件,开展火车噪声视速度、波形和频谱、能量时空变化的属性分析.结果表明:试验区光纤传感的火车噪声具有明显的属性差异,低速度重载荷的火车比高铁引起的地震振幅更大、能量更强,低速火车产生的噪声包含了丰富的低频信息;根据已知的视速度、振幅属性、时频属性,划分了4种类型的火车,并揭示了它们的进出站规律.本研究拓展了分布式光纤传感的应用场景,证明分布式光纤声传感有望应用于不同车型的通勤监测.

高铁地震波场的高精度密集观测及应用前景:基于通讯光缆的观测和处理结果

利用高清分布式光纤声波传感(HD-DAS)系统,进行高铁地震波场的高精度分布式采集。传感单元采用结构简单、成本低廉的通信光缆即可实现很好的监测效果,可以方便地根据实际环境进行布设,大大提高了野外观测的效率。采用多光缆并行布放,信号叠加后可有效地抑制噪声,提高高铁地震信号的信噪比。对HD-DAS系统采集到的高铁地震信号进行数据分析,发现高铁地震信号呈现分立谱特性。通过分析地震波信号的衰减规律,并通过频率-波数谱得到地震波在近地表的传输速度,表明光纤采集的高铁地震波信号可用于探测地下结构,并进一步实现对介质状态变化的监测,从而保障高铁的运行安全。

基于SOA光纤环形激光器的分布式声传感系统

为了降低传统分布式声学传感器(DAS)系统复杂度并提高信噪比(SNR),提出了一种基于半导体光放大器的光纤环形激光器(SOA-FRL)的DAS系统。该系统用SOA-FRL取代了传统的DAS系统中的窄线宽激光器和脉冲调制器,实现了高稳定的光脉冲输出;采用双脉冲外差探测方法,实现了精确的声信号探测和声源定位。实验结果表明:在频率为1 kHz、幅值为1.14 rad的声信号作用下,所提系统的解调SNR高达38.25 dB,空间分辨率为12 m。

基于超弱光纤光栅的声波传感探头增敏方法研究

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。

分布式光纤声学传感系统在地球物理勘探领域内的研究进展

分布式声波传感(DAS)技术作为最先进的声场检测技术之一,能够对与光纤相互作用的环境振动和声场信息进行分布式、长距离、高精度的实时检测。DAS技术中的光纤勘探系统解决了常规检波器在复杂地质环境中成本高、布设难度大等问题。近年来,DAS技术得到了快速的发展,尤其在需要长期、大规模布设的监测应用场景中得到了迅速的发展,但其相关研究成果较为发散,系统性认识较少。为深入了解DAS技术在地球物理勘探领域内的研究进展,更好地开展后期研究,本文通过文献调研,对DAS技术自身的发展进程与其近期在地球物理勘探领域内取得的研究成果,按照油气、海洋和环境工程3个不同的应用场景进行系统的分类总结,着重关注DAS技术近年来在不同方向上的发展进程、数据处理方面的研究进展以及已经取得成果的相关文献;最后,对现阶段基于DAS采集系统的发展趋势和亟待解决的问题进行了概括,对未来DAS的发展前景进行了分析。

基于超弱光纤布拉格光栅的声速测量方法

利用超弱光纤布拉格光栅对振动的敏感特性,提出了一种基于分布式光纤声学传感的声速测量方法。通过测量声速传递过程中不同超弱光纤布拉格光栅传感器接收到声音信号的时差来计算声速。该方法可以测量不同温度下的声速,其测量值与理论值误差小于0.5%。为声速测量提供了新的方法。

基于异构并行的DAS高密度数据实时解调技术

针对分布式光纤声波传感(Distributed optical fiber acoustic sensing,DAS)系统中高密度数据实时解调的需求,提出了基于中央处理器(Central processing unit,CPU)和图形处理器(Graphic processing unit,GPU)的异构并行计算架构,完成了实时解调双通道外差型DAS系统传感数据,可满足同时对两个通道共5000个等效阵元实时解调处理需求。此系统每秒需处理的数据量高达400 MB,相较于仅使用CPU运算的225.5 s运算时间,采用异构并行计算架构的运算时间优化到了468.2 ms,运算速度提升了482倍,且该方案仍有巨大的算力冗余空间,可为后续DAS系统整体实时性能的提升提供算力支持。

基于分布式光纤声波传感技术的管道泄漏探测应用

文章介绍了分布式光纤声波传感(distributed fiber optic acoustic sensing,DAS)技术的工作原理及其在管道泄漏检测中的应用。DAS系统通过光纤中背向瑞利散射光的变化来检测光纤上沿途感知到的声波信息,再结合光时域反射技术获取外部扰动事件所处的位置,从而通过检测和分析背向散射的光信号,提取外界扰动对应的位置和相位。文章搭建了一套试验系统,通过试验检测了DAS运用于管道泄漏探测的可靠性。试验表明,DAS技术可以检测并识别不同位置和类型的泄漏事件。为了进一步提高泄漏检测效果,文章设计了一种分析方法,通过数据预处理、特征提取、事件检测和泄漏识别与分类,实现了对泄漏事件的精准识别和定位,并提高了其在泄漏检测中的灵敏度和实时性。该方法结合DAS的技术特点,可以在长距离和大范围内提供高效、可靠的泄漏监测。

基于ICEEMDAN和小波阈值的Φ-OTDR信号去噪算法

针对分布式光纤声波传感系统信号信噪比较低的问题,提出了一种改进的自适应噪声完全集成经验模态分解方法。改进方法利用样本熵和小波阈值去噪算法,从高噪声分量中提取有效成分。通过改进的自适应噪声完全集成经验模态分解(ICEEMDAN,Intrinsic Computing Expressive Empirical Mode Decomposition With Adaptive Noise)对实际采集的信号进行分解,计算样本熵,将其中的含噪分量进行小波阈值去噪,最后与未处理的信号分量进行重构。实验结果表明,对实采的信号进行降噪处理后,信噪比提高了5.34 dB,均方误差降低了0.0148,波形互相关系数提高了5.7%。与其他常用的去噪方法相比,该方法不仅在信噪比方面表现更优秀,而且在均方误差和波形互相关系数方面也具有更好的性能,能够更好地保留有用信号。

基于分布式光纤声波传感的光缆状态监测研究

针对目前分布式光纤声波传感技术监测光缆灵敏度低的问题,提出了一种基于优化分布式光纤声波传感的光缆状态监测模型。首先通过理论分析了不同结构电缆的灵敏度,从而在理论层面分析影响灵敏度因素。其次基于优化分布式光纤声波传感测试不同传感电缆的灵敏度。实验结果表明,直径越小、杨氏模量越小的光缆可以有效提高灵敏度。结果与理论结果一致,表明所提方案对电缆状态监测的发展具有一定借鉴作用。