Rhoades David A, J Rastin Sepideh, Christophersen Annemarie
GNS Science, 1 Fairway Drive, Avalon, P.O. Box 30-368, Lower Hutt 5040, New Zealand.
Entropy (Basel). 2020 Nov 6;22(11):1264. doi: 10.3390/e22111264.
'Every Earthquake a Precursor According to Scale' (EEPAS) is a catalogue-based model to forecast earthquakes within the coming months, years and decades, depending on magnitude. EEPAS has been shown to perform well in seismically active regions like New Zealand (NZ). It is based on the observation that seismicity increases prior to major earthquakes. This increase follows predictive scaling relations. For larger target earthquakes, the precursor time is longer and precursory seismicity may have occurred prior to the start of the catalogue. Here, we derive a formula for the completeness of precursory earthquake contributions to a target earthquake as a function of its magnitude and lead time, where the lead time is the length of time from the start of the catalogue to its time of occurrence. We develop two new versions of EEPAS and apply them to NZ data. The Fixed Lead time EEPAS (FLEEPAS) model is used to examine the effect of the lead time on forecasting, and the Fixed Lead time Compensated EEPAS (FLCEEPAS) model compensates for incompleteness of precursory earthquake contributions. FLEEPAS reveals a space-time trade-off of precursory seismicity that requires further investigation. Both models improve forecasting performance at short lead times, although the improvement is achieved in different ways.
“按震级划分的每次地震前震”（EEPAS）是一种基于目录的模型，用于根据震级预测未来数月、数年和数十年内的地震。EEPAS已被证明在新西兰（NZ）等地震活跃地区表现良好。它基于这样的观察结果：大地震之前地震活动会增加。这种增加遵循预测比例关系。对于较大的目标地震，前震时间更长，且前震活动可能在目录开始之前就已发生。在此，我们推导了一个公式，用于计算前震对目标地震贡献的完整性，该完整性是目标地震震级和提前时间的函数，其中提前时间是从目录开始到目标地震发生的时间长度。我们开发了EEPAS的两个新版本，并将它们应用于新西兰的数据。固定提前时间EEPAS（FLEEPAS）模型用于研究提前时间对预测的影响，固定提前时间补偿EEPAS（FLCEEPAS）模型则补偿前震贡献的不完整性。FLEEPAS揭示了前震活动的时空权衡，这需要进一步研究。尽管两种模型以不同方式实现了改进，但它们在短提前时间内都提高了预测性能。
