论文标题
通过分散数据和加速度序列的联合倒置，在井下阵列处进行场地表征
Site characterization at downhole arrays by joint inversion of dispersion data and acceleration time series
论文作者
论文摘要
我们提出了基于集成kalman倒置的顺序数据同化算法，以估计近距表面剪切波速度曲线和抑制性，当异质数据和可以以（物理）平等和不等式约束的形式表示的先验信息时，可以在反向问题中表示。尽管非侵入性方法（例如表面波测试）是推断VS配置文件的有效且具有成本效益的方法，但应确认使用逆分析的站点表征可以产生与逆问题非独立性相关的错误结果。减轻逆问题不良的一种可行解决方案是通过补充观察来丰富先验知识和/或数据空间。在非侵入性方法的情况下，相关数据是表面波的分散曲线，通常通过高频处的活动源方法和低频下的被动方法解决。为了改善逆问题的良好状态，水平与垂直光谱比（HVSR）数据通常与反转中的分散数据共同使用。在本文中，我们表明，分散和强运动井下阵列数据的关节反转也可以减少VS曲线估计中不确定性的边缘。这是因为在井下阵列中记录的加速度序列包括身体和表面波，因此可以在反问题设置中丰富观察性数据空间。我们还展示了如何将所提出的算法修改为系统地纳入物理约束，从而进一步增强其良好的体现。我们使用合成数据和实际数据来检查拟议框架在估计VS曲线和在Garner Valley Downhole阵列中进行阻尼的性能，并将它们与以前研究中的VS估计进行比较。
We present a sequential data assimilation algorithm based on the ensemble Kalman inversion to estimate the near-surface shear wave velocity profile and damping when heterogeneous data and a priori information that can be represented in forms of (physical) equality and inequality constraints in the inverse problem are available. Although non-invasive methods, such as surface wave testing, are efficient and cost effective methods for inferring Vs profile, one should acknowledge that site characterization using inverse analyses can yield erroneous results associated with the inverse problem non-uniqueness. One viable solution to alleviate the inverse problem ill-posedness is to enrich the prior knowledge and/or the data space with complementary observations. In the case of non-invasive methods, the pertinent data are the dispersion curve of surface waves, typically resolved by means of active source methods at high frequencies and passive methods at low frequencies. To improve the inverse problem well-posedness, horizontal to vertical spectral ratio (HVSR) data are commonly used jointly with the dispersion data in the inversion. In this paper, we show that the joint inversion of dispersion and strong motion downhole array data can also reduce the margins of uncertainty in the Vs profile estimation. This is because acceleration time-series recorded at downhole arrays include both body and surface waves and therefore can enrich the observational data space in the inverse problem setting. We also show how the proposed algorithm can be modified to systematically incorporate physical constraints that further enhance its well-posedness. We use both synthetic and real data to examine the performance of the proposed framework in estimation of Vs profile and damping at the Garner Valley downhole array, and compare them against the Vs estimations in previous studies.
