传统的基于概率统计的方法采用的是静态模型，即根据历史上事件出现的频率来计算新事件的异常值。
Traditional statistics-based approach utilizes a stationary model, in which anomaly value is calculated according to events frequencies in history.
某一沉积事件周期性出现的频繁程度可用时间序列中的频率来表示。
The periodic occurrence of any sedimentary event may be expressed by the frequency in the time series.
现有平滑技术虽然已有效地对数据稀疏问题进行了处理，但对已出现事件频率分布的合理性并没有作出有效的分析。
The present smoothing techniques have solved the data sparse problem effectively but have not further analyzed the reasonableness for the frequency distribution of events occurring.
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