Hawkes process is one of the most commonly used models for investigating the self-exciting nature of earthquake occurrences. However, seismicity patterns have complicated characteristics due to heterogeneous geology and stresses, for which existing methods with Hawkes process cannot fully capture. This study introduces novel nonparametric Hawkes process models that are flexible in three distinct ways. First, we incorporate the spatial inhomogeneity of the self-excitation earthquake productivity. Second, we consider the anisotropy in aftershock occurrences. Third, we reflect the space-time interactions between aftershocks with a non-separable spatio-temporal triggering structure. For model estimation, we extend the model-independent stochastic declustering (MISD) algorithm and suggest substituting its histogram-based estimators with kernel methods. We demonstrate the utility of the proposed methods by applying them to the seismicity data in regions with active seismic activities.
翻译:霍克斯过程是调查地震发生时自我刺激性质的最常用模型之一。然而,地震模式由于地质和压力的多元性而具有复杂的特征,对此,霍克斯过程的现有方法无法完全捕捉。本研究采用了新颖的非对数霍克斯过程模型,这些模型在三种不同方面是灵活的。首先,我们结合了自振地震生产力的空间不相容性。第二,我们考虑了震后发生的动静。第三,我们反映了与非可分离的时空触发结构的余震后的时空相互作用。关于模型估计,我们扩展了依赖模型的光学离聚算法(MISSD),并建议用内核方法取代其基于直方天线的测算法。我们通过在地震活动活跃的区域将这些方法应用于地震数据的地震数据,从而展示了拟议方法的效用。