Abstract
The ‘Every Earthquake a Precursor According to Scale’ (EEPAS) medium-term earthquake forecasting model is based on the precursory scale increase (Ψ) phenomenon and associated scaling relations, in which the precursor magnitude MP is predictive of the mainshock magnitude Mm, precursor time TP and precursory area AP. In early studies of Ψ, a relatively low correlation between TP and AP suggested the possibility of a trade-off between time and area as a second-order effect. Here, we investigate the trade-off by means of the EEPAS model. Existing versions of EEPAS in New Zealand and California forecast target earthquakes of magnitudes M > 4.95 from input catalogues with M > 2.95. We systematically vary one parameter each from the EEPAS distributions for time and location, thereby varying the temporal and spatial scales of these distributions by two orders of magnitude. As one of these parameters is varied, the other is refitted to a 20-year period of each catalogue. The resulting curves of the temporal scaling factor against the spatial scaling factor are consistent with an even trade-off between time and area, given the limited temporal and spatial extent of the input catalogue. Hybrid models are formed by mixing several EEPAS models, with parameter sets chosen from points on the trade-off line. These are tested against the original fitted EEPAS models on a subsequent period of the New Zealand catalogue. The resulting information gains suggest that the space–time trade-off can be exploited to improve forecasting.
Highlights
Empirical observations of precursory seismicity patterns have an important role in aiding the development of earthquake forecasting models [4,5,6,7,8,9,10]
An order of magnitude change in each scaling factor induced a modest reduction in the log-likelihood
A space–time trade-off of precursory seismicity has been investigated by repeated refitting of the Earthquake a Precursor According to Scale’ (EEPAS) earthquake forecasting model to the catalogues of New Zealand and California
Summary
Medium-term earthquake forecasting with time-varying models is becoming increasingly important for operational earthquake forecasting and the development of seismic hazard models. Empirical observations of precursory seismicity patterns have an important role in aiding the development of earthquake forecasting models [4,5,6,7,8,9,10]. One such pattern is the precursory scale increase (Ψ) phenomenon, which is an increase in the magnitude and rate of occurrence of small earthquakes [11,12]. Individual examples of Ψ were identified by examining the seismicity in arbitrary frames of space and time preceding the occurrence of a major earthquake, such as in Figure 1 for the 2014 Napa, California earthquake
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