Many recent studies have shown that we are generally unable to accurately replicate recorded ground motions at most borehole array sites using available subsurface geotechnical information and one-dimensional (1D) ground response analyses (GRAs). When 1D GRAs fail to accurately predict recorded site response, the site is often considered too complex to be effectively modeled as 1D. While three-dimensional (3D) numerical GRAs are possible and believed to be more accurate, there is rarely a 3D subsurface model available for these analyses. The lack of affordable and reliable site characterization methods to quantify spatial variability in subsurface conditions, particularly regarding shear wave velocity (Vs) measurements needed for GRAs, has pushed researchers to adopt stochastic approaches, such as Vs randomization and spatially correlated random fields. However, these stochastically generated models require the assumption of generic, or guessed, input parameters, introducing significant uncertainties into the site response predictions. This article describes a new geostatistical approach that can be used for building pseudo-3D Vs models as a means to rationally account for spatial variability in GRAs, increase model accuracy, and reduce uncertainty. Importantly, it requires only a single measured Vs profile and a number of simple, cost-effective, horizontal-to-vertical spectral ratio (H/V) noise measurements. Using Gaussian geostatistical regression, irregularly sampled estimates of fundamental site frequency from H/V measurements (f_0,H/V) are used to generate a uniform grid of f_0,H/V across the site with accompanying Vs profiles that have been scaled to match each f_0,H/V value, thereby producing a pseudo-3D Vs model. This approach is demonstrated at the Treasure Island and Delaney Park Downhole Array sites (TIDA and DPDA, respectively). While the pseudo-3D Vs models can be used to incorporate spatial variability into 1D, two-dimensional (2D), or 3D GRAs, their implementation in 1D GRAs at TIDA and DPDA is discussed in a companion paper.

最近的许多研究表明，使用可用的地下岩土工程信息和一维（1D）地面响应分析（GRAs），我们通常无法在大多数钻孔阵列站点上准确地复制记录的地面运动。当1D GRA无法准确预测所记录的站点响应时，通常认为该站点过于复杂而无法有效地建模为1D。尽管三维（3D）数字GRAs是可能的并且被认为更准确，但很少有3D地下模型可用于这些分析。缺乏负担得起且可靠的站点表征方法来量化地下条件下的空间变异性，尤其是在GRAs所需的横波速度（Vs）测量方面，促使研究人员采用了随机方法，例如Vs随机化和空间相关的随机字段。但是，这些随机生成的模型需要假设通用或猜测的输入参数，这给现场响应预测带来了很大的不确定性。本文介绍了一种新的地统计方法，该方法可用于构建伪3D Vs模型，以此作为合理考虑GRA中空间变异性，提高模型准确性并减少不确定性的一种方法。重要的是，它仅需要单个测得的Vs曲线和许多简单的，具有成本效益的水平与垂直光谱之比（H / V）噪声测量。使用高斯地统计回归，从H / V测量中不定期采样基本站点频率的估计值（输入参数，将大量不确定性引入站点响应预测中。本文介绍了一种新的地统计方法，该方法可用于构建伪3D Vs模型，以此作为合理考虑GRA中空间变异性，提高模型准确性并减少不确定性的一种方法。重要的是，它仅需要单个测得的Vs曲线和许多简单的，具有成本效益的水平与垂直光谱之比（H / V）噪声测量。使用高斯地统计回归，从H / V测量中不定期采样基本站点频率的估计值（输入参数，将大量不确定性引入站点响应预测中。本文介绍了一种新的地统计方法，该方法可用于构建伪3D Vs模型，以此作为合理考虑GRA中空间变异性，提高模型准确性并减少不确定性的一种方法。重要的是，它仅需要单个测得的Vs曲线和许多简单的，具有成本效益的水平与垂直光谱之比（H / V）噪声测量。使用高斯地统计回归，从H / V测量中不定期采样基本站点频率的估计值（提高模型准确性，并减少不确定性。重要的是，它仅需要一个测量的Vs曲线和多个简单的，具有成本效益的水平/垂直频谱比（H / V）噪声测量。使用高斯地统计回归，从H / V测量中不定期采样基本站点频率的估计值（提高模型准确性，并减少不确定性。重要的是，它仅需要单个测得的Vs曲线和许多简单的，具有成本效益的水平与垂直光谱之比（H / V）噪声测量。使用高斯地统计回归，从H / V测量中不定期采样基本站点频率的估计值（f _{0，H / V}）用于在整个站点上生成均匀的f _{0，H / V}网格，并附带缩放的Vs轮廓以匹配每个f _{0，H / V}值，从而生成伪3D Vs模型。在金银岛和德莱尼公园井下阵列现场（分别为TIDA和DPDA）演示了此方法。虽然伪3D Vs模型可用于将空间可变性合并到1D，二维（2D）或3D GRA中，但在随附的论文中讨论了它们在TIDA和DPDA的1D GRA中的实现。