This study presents the development and application of a scalable non-ergodic ground motion model (NGMM) for the Los Angeles area. The NGMM is trained and validated on physics-based simulated ground-motion data from a recent Statewide California Earthquake Center (SCEC) CyberShake study. The NGMM is formulated as a Gaussian Process (GP) regression model, where the prior median is defined as the ASK14 ergodic ground-motion model and the posterior median is obtained by learning the non-ergodic effects embedded in the training data. These non-ergodic effects include systematic site and path effects, which are represented in the GP using Matérn and specialized covariance kernels that explicitly characterize path vectors. Implementing the NGMM requires hyperparameter tuning and inference on large datasets (on the order of one million data points or more), posing significant computational challenges for conventional GP approaches. To address this scalability issue, this paper presents a suite of computational strategies, including sparse Cholesky inversion, parallel computing, GPU acceleration, and stochastic gradient descent minimization. Despite these advances, the full CyberShake dataset (on the order of hundreds of millions of data points) remains computationally prohibitive. Therefore, aleatory variability is modeled separately using a mixed-effects formulation to represent within-event and between-event variability. The developed NGMM has two primary applications: interpolation of partially observed ground-motion fields and predictive modeling for ground motions in unobserved earthquake scenarios. Validation results on independent datasets demonstrate accurate performance in both applications. A case study of power transmission network assessment in an Mw 6.7 Puente Hill scenario further demonstrated that the developed NGMM closely reproduces physics-based simulation results.

翻译：本研究展示了洛杉矶地区可扩展非遍历地面运动模型（NGMM）的构建与应用。该模型基于近期加州地震中心（SCEC）CyberShake研究中物理模拟的地震动数据进行训练与验证。NGMM被构建为高斯过程（GP）回归模型，其中先验中位数定义为ASK14遍历地面运动模型，后验中位数通过学习训练数据中蕴含的非遍历效应获得。这些非遍历效应包括系统性场地效应和路径效应，在GP中采用Matérn协方差核与专门表征路径向量的协方差核进行建模。NGMM的实施需要对大规模数据集（百万量级数据点或更多）进行超参数调优与推断，这对传统GP方法构成重大计算挑战。为解决可扩展性问题，本文提出一套计算策略，包括稀疏Cholesky逆运算、并行计算、GPU加速与随机梯度下降优化。尽管取得这些进展，完整CyberShake数据集（数亿量级数据点）仍超出计算承受范围。因此，采用混合效应模型分别对事件内与事件间变异性进行随机变异性建模。所开发的NGMM具有两大应用方向：部分观测地震动场的插值，以及未观测地震场景中地震动的预测建模。在独立数据集上的验证结果表明，该模型在两项应用中均表现精确。针对Mw 6.7 Puente Hill场景的输电网评估案例研究表明，所开发的NGMM能高度复现物理模拟结果。