A variety of nonparametric models have been proposed for estimating earthquake triggering. We investigate the ability of the model‐independent stochastic declustering method developed by Marsan and Lengliné (2008) to estimate variable spatial triggering that can vary with direction, magnitude, and region. We develop an approach for local fault estimation and demonstrate forecasting methods that use the nonparametric estimates. Simulation studies are conducted to verify the effectiveness of the method, and the nonparametric estimates are applied to a California earthquake catalog. Model forecast performance is evaluated retrospectively by comparing our models with the long‐term forecast of Helmstetter et al. (2007), using both deviance and Voronoi residuals. We show improved performance compared with Helmstetter et al. (2007) in various regions while using a full nonparametric estimation and forecasting approach.

中文翻译：

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用于预测加利福尼亚地震的非参数 Hawkes 模型

已经提出了多种非参数模型来估计地震触发。我们研究了由 Marsan 和 Lengliné (2008) 开发的独立于模型的随机去聚类方法估计随方向、幅度和区域而变化的可变空间触发的能力。我们开发了一种局部故障估计方法，并演示了使用非参数估计的预测方法。进行了仿真研究以验证该方法的有效性，并将非参数估计应用于加利福尼亚地震目录。通过将我们的模型与 Helmstetter 等人的长期预测进行比较，对模型预测性能进行了回顾性评估。(2007)，同时使用偏差和 Voronoi 残差。与 Helmstetter 等人相比，我们展示了改进的性能。