Earthquake occurrence modeling of large subduction events involves significant uncertainty, stemming from the scarcity of geological data and inaccuracy of dating techniques. The previous research on statistical modeling of full-margin ruptures of the Cascadia subduction zone attempted to address these issues. However, the adopted resampling method to account for the uncertain marine turbidite age data from the Cascadia subduction zone was not sufficient in the sample size. This study presents a statistical approach based on the Gaussian mixture model applied to significantly larger resampled Cascadia age data. The results suggest that the 3-component Gaussian mixture model outperforms the 2-component Gaussian mixture model and the 1-component renewal models by capturing the long gap and short-term clustering. The developed Gaussian mixture model is well suited to apply to probabilistic seismic and tsunami hazard analysis and the calculation of long-term probability of the future full-margin Cascadia events by considering the elapsed time since the last event.

中文翻译：

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使用事件时间重采样和高斯混合模型对卡斯卡迪亚俯冲带全边缘破裂复发进行统计表征

由于地质数据的稀缺和测年技术的不准确，大型俯冲事件的地震发生模型存在很大的不确定性。先前对卡斯卡迪亚俯冲带全边缘破裂统计模型的研究试图解决这些问题。然而，采用重采样方法来解释卡斯卡迪亚俯冲带不确定的海相浊积岩年龄数据，样本量不足。本研究提出了一种基于高斯混合模型的统计方法，该方法应用于更大的重采样卡斯卡迪亚年龄数据。结果表明，通过捕获长间隙和短期聚类，3 分量高斯混合模型优于 2 分量高斯混合模型和 1 分量更新模型。开发的高斯混合模型非常适合应用于概率地震和海啸灾害分析，以及通过考虑自上次事件以来经过的时间来计算未来满裕度卡斯卡迪亚事件的长期概率。