We present a data-driven model for ground-motion synthesis using a Generative
Adversarial Neural Operator (GANO) that combines recent advancements in machine
learning and open access strong motion data sets to generate three-component
acceleration time histories conditioned on moment magnitude ($M$), rupture
distance ($R_{rup}$), time-average shear-wave velocity at the top $30m$
($V_{S30}$), and tectonic environment or style of faulting. We use Neural
Operators, a resolution invariant architecture that guarantees that the model
training is independent of the data sampling frequency. We first present the
conditional ground-motion synthesis algorithm (referred to heretofore as
cGM-GANO) and discuss its advantages compared to previous work. Next, we verify
the cGM-GANO framework using simulated ground motions generated with the
Southern California Earthquake Center (SCEC) Broadband Platform (BBP). We
lastly train cGM-GANO on a KiK-net dataset from Japan, showing that the
framework can recover the magnitude, distance, and $V_{S30}$ scaling of Fourier
amplitude and pseudo-spectral accelerations. We evaluate cGM-GANO through
residual analysis with the empirical dataset as well as by comparison with
conventional Ground Motion Models (GMMs) for selected ground motion scenarios.
Results show that cGM-GANO produces consistent median scaling with the GMMs for
the corresponding tectonic environments. The largest misfit is observed at
short distances due to the scarcity of training data. With the exception of
short distances, the aleatory variability of the response spectral ordinates is
also well captured, especially for subduction events due to the adequacy of
training data. Applications of the presented framework include generation of
risk-targeted ground motions for site-specific engineering applications.

我们提出了一个使用生成对抗神经运算器（GANO）、结合最新的机器学习技术和开放接入强动态数据集的数据驱动模型，能够根据震级（M）、破裂距离（Rrup）、顶部 30 米时间平均剪切波速度（VS30）以及构造环境或断层风格条件下生成三分量振动加速度时间历程的算法。我们使用神经运算器，这是一种具有分辨率不变性的架构，保证了模型训练与数据采样频率无关。我们首先介绍了条件地面运动合成算法（以下简称 cGM-GANO），并讨论了与以前工作相比的优势。接下来，我们使用南加州地震中心（SCEC）宽频平台（BBP）生成的模拟地面运动进行了 cGM-GANO 框架的验证。最后，我们在一个来自日本的 KiK-net 数据集上训练了 cGM-GANO，结果显示该框架能够恢复傅里叶幅度和伪谱加速度的震级、距离和 VS30 尺度。我们通过与经验数据集的残差分析以及与选定地面运动场景的传统地面运动模型（GMMs）进行比较来评估 cGM-GANO。结果显示 cGM-GANO 为相应构造环境产生一致的中位数尺度。在训练数据稀缺的情况下，在短距离上观察到最大的误差。除了短距离外，响应谱序列的混合变异性也能够很好地捕捉到，特别是对于俯冲事件由于训练数据的充分性。该框架的应用包括生成面向特定场地工程应用的风险定位地面运动。