The steady increase of ground‐motion data not only allows new possibilities but also comes with new challenges in the development of ground‐motion models (GMMs). Data classification techniques (e.g., cluster analysis) do not only produce deterministic classifications but also probabilistic classifications (e.g., probabilities for each datum to belong to a given class or cluster). One challenge is the integration of such continuous classification in regressions for GMM development such as the widely used mixed‐effects model. We address this issue by introducing an extension of the mixed‐effects model to incorporate data weighting. The parameter estimation of the mixed‐effects model, that is, fixed‐effects coefficients of the GMMs and the random‐effects variances, are based on the weighted likelihood function, which also provides analytic uncertainty estimates. The data weighting permits for earthquake classification beyond the classical, expert‐driven, binary classification based, for example, on event depth, distance to trench, style of faulting, and fault dip angle. We apply Angular Classification with Expectation–maximization, an algorithm to identify clusters of nodal planes from focal mechanisms to differentiate between, for example, interface‐ and intraslab‐type events. Classification is continuous, that is, no event belongs completely to one class, which is taken into account in the ground‐motion modeling. The theoretical framework described in this article allows for a fully automatic calibration of ground‐motion models using large databases with automated classification and processing of earthquake and ground‐motion data. As an example, we developed a GMM on the basis of the GMM by Montalva et al. (2017) with data from the strong‐motion flat file of Bastías and Montalva (2016) with ∼2400 records from 319 events in the Chilean subduction zone. Our GMM with the data‐driven classification is comparable to the expert‐classification‐based model. Furthermore, the model shows temporal variations of the between‐event residuals before and after large earthquakes in the region.

中文翻译：

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地面运动模型开发中机器学习与优化之间的联系：数据驱动的概率地震分类的加权混合效应回归

地面运动数据的不断增加不仅为地面运动模型（GMM）的开发带来了新的可能性，而且还带来了新的挑战。数据分类技术（例如，聚类分析）不仅会产生确定性分类，而且还会产生概率分类（例如，每个数据属于给定类或聚类的概率）。挑战之一是在GMM开发的回归中如何集成此类连续分类法，例如广泛使用的混合效应模型。我们通过引入混合效应模型的扩展来解决此问题，以纳入数据加权。混合效应模型的参数估计（即GMM的固定效应系数和随机效应方差）基于加权似然函数，它还提供了分析不确定性估计。数据加权可以根据经典的，专家驱动的二进制分类进行地震分类，例如，基于事件深度，与沟槽的距离，断层类型和断层倾角进行分类。我们应用“期望最大化”的角度分类法，该算法可从焦点机制中识别节点平面的簇，以区分例如界面型事件和板内型事件。分类是连续的，也就是说，没有事件完全属于一个类别，在地面运动建模中要考虑到这一点。本文所述的理论框架允许使用大型数据库对地震模型进行全自动校准，并具有对地震和地面运动数据的自动分类和处理功能。举个例子，我们在Montalva等人的GMM的基础上开发了GMM。（2017年）的数据来自Bastías和Montalva（2016年）的强运动平面文件，智利俯冲带319次事件的2400条记录。我们具有数据驱动分类的GMM可与基于专家分类的模型相媲美。此外，该模型还显示了该地区大地震前后事件间残差的时间变化。