论文标题
使用路边分布的声传感阵列的近地表表征
Near-surface Characterization Using a Roadside Distributed Acoustic Sensing Array
论文作者
论文摘要
得益于分布式声感应（DAS）测量的宽带性质，斯坦福DAS-2阵列的路边部分可以记录来自各种来源的地震信号。例如，它测量了由汽车的重量（<0.8 Hz）引起的地球静态变形，以及地震（<3 Hz）引起的瑞利波和动态汽车相互作用（3-20 Hz）。我们直接利用激发的表面波进行浅剪切波速度反转。通过汽车引起的瑞利波具有一致的基本模式和嘈杂的第一模式。通过堆叠33辆经过汽车的分散图像，我们获得了稳定的分散图像。可以通过添加低频地震引起的雷利波来扩展基本模式的频率范围。由于扩展频率范围，我们可以实现更好的深度覆盖范围和剪切波速度反演的分辨率。为了确保与爱波的明确分离并与相位速度保持一致的速度，我们选择了与阵列大致一致的地震。倒置的模型与由斯坦福大学合同使用的岩土技术服务公司进行的常规地球调查进行了匹配，该调查使用了从表面上的主动来源直至约50米。为了自动化与反转过程，我们引入了一个新的目标函数，以避免手动分散曲线选择。我们通过沿着光纤在多个位置进行独立的1-D反转来构建2-D VS轮廓。从低频准静电失真记录中，我们在沿纤维的每个位置都取代单个泊松比。我们观察到VS和Poisson比率曲线的空间异质性。我们的方法比环境场的干涉法便宜，并且可以更频繁地获得可靠的估计，因为不需要冗长的互相关。
Thanks to the broadband nature of the Distributed Acoustic Sensing (DAS) measurement, a roadside section of the Stanford DAS-2 array can record seismic signals from various sources. For example, it measures the earth's quasi-static distortion caused by the weight of cars (<0.8 Hz), and Rayleigh waves induced by earthquakes (<3 Hz) and by dynamic car-road interactions (3-20 Hz). We directly utilize the excited surface waves for shallow shear-wave velocity inversion. Rayleigh waves induced by passing cars have a consistent fundamental mode and a noisier first mode. By stacking dispersion images of 33 passing cars, we obtain stable dispersion images. The frequency range of the fundamental mode can be extended by adding the low-frequency earthquake-induced Rayleigh waves. Thanks to the extended frequency range, we can achieve better depth coverage and resolution for shear-wave velocity inversion. In order to assure clear separation from Love waves and aligning apparent velocity with phase velocity, we choose an earthquake that is approximately in line with the array. The inverted models match those obtained by a conventional geophone survey performed by a geotechnical service company contracted by Stanford University using active sources from the surface until about 50 meters. In order to automate the Vs inversion process, we introduce a new objective function that avoids manual dispersion curve picking. We construct a 2-D Vs profile by performing independent 1-D inversions at multiple locations along the fiber. From the low-frequency quasi-static distortion recordings, we invert for a single Poisson's ratio at each location along the fiber. We observe spatial heterogeneity of both Vs and Poisson's ratio profiles. Our approach is dramatically cheaper than ambient field interferometry and reliable estimates can be obtained more frequently as no lengthy cross-correlations are required.
