Fan Jingfang, Zhou Dong, Shekhtman Louis M, Shapira Avi, Hofstetter Rami, Marzocchi Warner, Ashkenazy Yosef, Havlin Shlomo
Blaustein Institutes for Desert Research, Ben-Gurion University of the Negev, Midreshet Ben-Gurion 84990, Israel.
Department of Physics, Bar Ilan University, Ramat Gan 52900, Israel.
Phys Rev E. 2019 Apr;99(4-1):042210. doi: 10.1103/PhysRevE.99.042210.
Earthquakes are one of the most devastating natural disasters that plague society. Skilled, reliable earthquake forecasting remains the ultimate goal for seismologists. Using the detrended fluctuation analysis (DFA) and conditional probability (CP) methods, we find that memory exists not only in interoccurrence seismic records but also in released energy as well as in the series of the number of events per unit time. Analysis of a standard epidemic-type aftershock sequences (ETAS) earthquake model indicates that the empirically observed earthquake memory can be reproduced only for a narrow range of the model's parameters. This finding therefore provides tight constraints on the model's parameters and can serve as a testbed for existing earthquake forecasting models. Furthermore, we show that by implementing DFA and CP results, the ETAS model can significantly improve the short-term forecasting rate for the real (Italian) earthquake catalog.
地震是困扰社会的最具破坏性的自然灾害之一。熟练、可靠的地震预测仍然是地震学家的最终目标。使用去趋势波动分析(DFA)和条件概率(CP)方法,我们发现记忆不仅存在于地震发生间隔记录中,也存在于释放的能量以及单位时间内事件数量序列中。对标准的震后余震序列(ETAS)地震模型的分析表明,只有在模型参数的狭窄范围内才能重现经验观察到的地震记忆。因此,这一发现为模型参数提供了严格限制,并可作为现有地震预测模型的试验台。此外,我们表明,通过应用DFA和CP结果,ETAS模型可以显著提高真实(意大利)地震目录的短期预测率。