The Use of International Alignment in a Long-Range Forecasting Model

1973 ◽  
Author(s):  
Herman M. Weil
Keyword(s):  
Long Range ◽  
Forecasting Model

scholarly journals Effect of tectonic setting on the fit and performance of a long-range earthquake forecasting model

2012 ◽  
Vol 2 (1) ◽  
pp. 3
Author(s):  
David Alan Rhoades ◽  
Paul G. Somerville ◽  
Felipe Dimer de Oliveira ◽  
Hong Kie Thio
Keyword(s):  
Long Range ◽  
Information Gain ◽  
Tectonic Setting ◽  
Depth Resolution ◽  
Earthquake Forecasting ◽  
Forecasting Model ◽  
Testing Period ◽  
Crustal Earthquakes ◽  
Average Information ◽  
Better Than

The Every Earthquake a Precursor According to Scale (EEPAS) long-range earthquake forecasting model has been shown to be informative in several seismically active regions, including New Zealand, California and Japan. In previous applications of the model, the tectonic setting of earthquakes has been ignored. Here we distinguish crustal, plate interface, and slab earthquakes and apply the model to earthquakes with magnitude M≥4 in the Japan region from 1926 onwards. The target magnitude range is M≥ 6; the fitting period is 1966-1995; and the testing period is 1996-2005. In forecasting major slab earthquakes, it is optimal to use only slab and interface events as precursors. In forecasting major interface events, it is optimal to use only interface events as precursors. In forecasting major crustal events, it is optimal to use only crustal events as precursors. For the smoothed-seismicity component of the EEPAS model, it is optimal to use slab and interface events for earthquakes in the slab, interface events only for earthquakes on the interface, and crustal and interface events for crustal earthquakes. The optimal model parameters indicate that the precursor areas for slab earthquakes are relatively small compared to those for earthquakes in other tectonic categories, and that the precursor times and precursory earthquake magnitudes for crustal earthquakes are relatively large. The optimal models fit the learning data sets better than the raw EEPAS model, with an average information gain per earthquake of about 0.4. The average information gain is similar in the testing period, although it is higher for crustal earthquakes and lower for slab and interface earthquakes than in the learning period. These results show that earthquake interactions are stronger between earthquakes of similar tectonic types and that distinguishing tectonic types improves forecasts by enhancing the depth resolution where tectonic categories of earthquakes are vertically separated. However, when depth resolution is ignored, the model formed by aggregating the optimal forecasts for each tectonic category performs no better than the raw EEPAS model.

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