Vehicle-induced seismic waves, generated as vehicles traverse the ground surface, carry valuable information for imaging the underlying near-surface structure. These waves propagate differently in the subsurface depending on soil properties at various spatial locations. By leveraging wave propagation characteristics, such as surface-wave velocity and attenuation, this study presents a novel method for near-surface monitoring. Our method employs passing vehicles as active, non-dedicated seismic sources and leverages pre-existing telecommunication fibers as large-scale and cost-effective roadside sensors empowered by Distributed Acoustic Sensing (DAS) technology. A specialized Kalman filter algorithm is integrated for automated DAS-based traffic monitoring to accurately determine vehicles' location and speed. Then, our approach uniquely leverages vehicle trajectories to isolate space-time windows containing high-quality surface waves. With known vehicle (i.e., seismic source) locations, we can effectively mitigate artifacts associated with suboptimal distribution of sources in conventional ambient noise interferometry. Compared to ambient noise interferometry, our approach enables the synthesis of virtual shot gathers with a high signal-to-noise ratio and spatiotemporal resolution at reduced computational costs. We validate the effectiveness of our method using the Stanford DAS-2 array, with a focus on capturing spatial heterogeneity and monitoring temporal variations in soil seismic properties during rainfall events. Specifically, in non-built-up areas, we observed an evident decrease in phase velocity and group velocity and an increase in attenuation due to the rainfall. Our findings illustrate our method's sensitivity and resolution in discerning variations across different spatial locations and demonstrate that our method is a promising advancement for high-resolution near-surface imaging in urban settings.

中文翻译：

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使用车辆感应 DAS 信号进行高时空分辨率的近地表表征

当车辆穿过地表时产生的车辆诱发的地震波携带着对底层近地表结构进行成像的有价值的信息。这些波在地下的传播方式不同，具体取决于不同空间位置的土壤特性。通过利用波传播特性，例如表面波速度和衰减，本研究提出了一种近地表监测的新方法。我们的方法采用经过的车辆作为主动、非专用地震源，并利用现有的电信光纤作为分布式声学传感 (DAS) 技术支持的大规模且经济高效的路边传感器。集成了专门的卡尔曼滤波器算法，用于基于 DAS 的自动化交通监控，以准确确定车辆的位置和速度。然后，我们的方法独特地利用车辆轨迹来隔离包含高质量表面波的时空窗口。利用已知的车辆（即地震源）位置，我们可以有效地减轻与传统环境噪声干涉测量中源的次优分布相关的伪影。与环境噪声干涉测量相比，我们的方法能够以较低的计算成本合成具有高信噪比和时空分辨率的虚拟炮集。我们使用斯坦福 DAS-2 阵列验证了我们方法的有效性，重点是捕获空间异质性并监测降雨事件期间土壤地震特性的时间变化。具体来说，在非建筑区域，我们观察到相速度和群速度明显下降，并且由于降雨而导致衰减增加。我们的研究结果说明了我们的方法在辨别不同空间位置的变化方面的灵敏度和分辨率，并证明我们的方法对于城市环境中的高分辨率近地表成像来说是一个有前途的进步。