Characterizing Vehicle-Induced Distributed Acoustic Sensing Signals for Accurate Urban Near-Surface Imaging

Jingxiao Liu, Haipeng Li, Siyuan Yuan, Hae Young Noh, Biondo Biondi
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Abstract

Continuous seismic monitoring of the near-surface structure is crucial for urban infrastructure safety, aiding in the detection of sinkholes, subsidence, and other seismic hazards. Utilizing existing telecommunication optical fibers as Distributed Acoustic Sensing (DAS) systems offers a cost-effective method for creating dense seismic arrays in urban areas. DAS leverages roadside fiber-optic cables to record vehicle-induced surface waves for near-surface imaging. However, the influence of roadway vehicle characteristics on their induced surface waves and the resulting imaging of near-surface structures is poorly understood. We investigate surface waves generated by vehicles of varying weights and speeds to provide insights into accurate and efficient near-surface characterization. We first classify vehicles into light, mid-weight, and heavy based on the maximum amplitudes of quasi-static DAS records. Vehicles are also classified by their traveling speed using their arrival times at DAS channels. To investigate how vehicle characteristics influence the induced surface waves, we extract phase velocity dispersion and invert the subsurface structure for each vehicle class by retrieving virtual shot gathers (VSGs). Our results reveal that heavy vehicles produce higher signal-to-noise ratio surface waves, and a sevenfold increase in vehicle weight can reduce uncertainties in phase velocity measurements from dispersion spectra by up to 3X. Thus, data from heavy vehicles better constrain structures at greater depths. Additionally, with driving speeds ranging from 5 to 30 meters per second in our study, differences in the dispersion curves due to vehicle speed are less pronounced than those due to vehicle weight. Our results suggest judiciously selecting and processing surface wave signals from certain vehicle types can improve the quality of near-surface imaging in urban environments.
表征车辆诱发的分布式声学传感信号,实现精确的城市近地成像
对近地表结构进行连续地震监测对城市基础设施安全至关重要,有助于检测沉井、沉降和其他地震危险。利用现有的电信光纤作为分布式声学传感(DAS)系统,为在城市地区建立密集的地震阵列提供了一种具有成本效益的方法。DAS 利用路边光纤电缆记录车辆引起的表面波,用于近地表成像。然而,人们对道路车辆特性对其诱导面波以及由此产生的近地表结构成像的影响知之甚少。我们对不同重量和速度的车辆产生的表面波进行了研究,以便为准确、高效的近地表特征描述提供见解。我们首先根据准静态 DAS 记录的最大振幅将车辆分为轻型、中型和重型车辆。此外,我们还根据车辆到达 DAS 信道的时间,按其行驶速度对车辆进行分类。为了研究车辆特征如何影响诱导面波,我们提取了相位速度频散,并通过检索虚拟拍摄集合(VSGs)反演了每类车辆的次表层结构。我们的研究结果表明,重型车辆产生的表面波信噪比较高,车辆重量增加七倍可将频散谱相速度测量的不确定性降低 3 倍。因此,来自重型车辆的数据可以更好地约束更深的结构。此外,在我们的研究中,车辆的行驶速度从每秒 5 米到每秒 30 米不等,车辆速度造成的频散曲线差异没有车辆重量造成的差异那么明显。我们的研究结果表明,明智地选择和处理某些类型车辆的表面波信号,可以提高城市环境中近地表成像的质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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