Abstract
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.
Highlights
The process through which earthquakes occur is complex involving spatio-temporal dynamics [1, 2] and has previously been characterized as a paradigm of selforganized criticality [3, 4]
Using the detrended fluctuation analysis (DFA) and conditional probability (CP) methods we find that memory exists in inter-occurrence seismic records, and in released energy as well as in the series of the number of events per unit time
We use the DFA and CP methods to quantify the level of memory and long-term correlations
Summary
The process through which earthquakes occur is complex involving spatio-temporal dynamics [1, 2] and has previously been characterized as a paradigm of selforganized criticality [3, 4]. Perhaps the most promising observation is that a rescaling involving region size and magnitude threshold, produces data collapse onto a universal gamma distribution for many worldwide regions [6]. This observation is of great importance for the development of physical and statistical models of earthquake dynamics. An analysis on the ETAS model, have indicated that this distribution is not universal [9, 10], but instead it is a bimodal mixture distribution [11] These modeling studies have captured much of the earthquake dynamics through the distribution of recurrence intervals but they have not considered the memory found in real earthquakes time series.
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