Detrended fluctuation analysis of earthquake data
Detrended fluctuation analysis of earthquake data
复制标题
地震数据的去趋势波动分析
DOI：
10.1103/physrevresearch.3.033081
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发表时间：
2021
影响因子：
4.2
通讯作者：
Takumi Kataoka; Tomoshige Miyaguchi; Takuma Akimoto
中科院分区：
文献类型：
--
作者：
島尻裕巳;堀江宏太;坂元啓紀;糸井千岳;守田智;Takumi Kataoka; Tomoshige Miyaguchi; Takuma Akimoto
去趋势波动分析(DFA)在揭示长期相关性的随机过程中有着广泛的应用。在这里，我们将DFA应用于模拟地震数据的点过程。点过程由类似于流行病类型余震序列模型的模型合成，我们将DFA应用于点过程的时间序列，其中是截至时间的累积事件数。在这些时间序列的DFA中发现了交叉现象，大量的数值模拟表明这种交叉现象是时间序列中非平稳性的特征。我们还发现，交叉时间代表了嵌入在时间序列中的非平稳过程的特征时间尺度。因此，当时间序列是平稳信号和非平稳信号的叠加时，点过程的DFA特别适用于提取非平稳过程的信息。此外，我们将DFA应用于日本真实地震的累积次数，我们发现了与合成数据相似的交叉现象。
The detrended fluctuation analysis (DFA) is extensively useful in stochastic processes to unveil the long-term correlation. Here, we apply the DFA to point processes that mimic earthquake data. The point processes are synthesized by a model similar to the epidemic-type aftershock sequence model, and we apply the DFA to time seriesof the point processes, whereis the cumulative number of events up to time. Crossover phenomena are found in the DFA for these time series, and extensive numerical simulations suggest that the crossover phenomena are signatures of nonstationarity in the time series. We also find that the crossover time represents a characteristic time scale of the nonstationary process embedded in the time series. Therefore, the DFA for point processes is especially useful in extracting information of nonstationary processes when time series are superpositions of stationary and nonstationary signals. Furthermore, we apply the DFA to the cumulative numberof real earthquakes in Japan, and we find a crossover phenomenon similar to that found for the synthesized data.
DOI：
10.1016/j.physa.2008.10.023
发表时间：
2009-02-15
影响因子：
3.3
作者：
Hasumi, Tomohiro;Akimoto, Takuma;Aizawa, Yoji
通讯作者：
Aizawa, Yoji
影响因子：
4.4
作者：
Kuno, M;Fromm, DP;Nesbitt, DJ
通讯作者：
Nesbitt, DJ