Predictive scaling relations derived from many examples of the precursory scale
increase phenomenon are the basis for the EEPAS forecasting model, in which every
earthquake is regarded as precursor of larger ones to follow it in the long-term. This model
has been applied, with relatively high magnitude thresholds, to the catalogues of New Zealand,
all of California, all of Japan, and Greece, and with relatively low thresholds to Kanto and S.
California. The information value of the model does not vary appreciably with magnitude
threshold. However, differences in the fitted parameters suggest that, at low magnitude
thresholds, the values are affected by the need to accommodate aftershocks in the targeted
magnitude range. A modification is proposed to the magnitude distribution in the EEPAS
model in order to accommodate such aftershocks. The modification should lead to more
consistent parameter values and improved performance of the model.
Predictive scaling relations derived from many examples of the precursory scaleincrease phenomenon are the basis for the EEPAS forecasting model, in which everyearthquake is regarded as precursor of larger ones to follow it in the long-term. This modelhas been applied, with relatively high magnitude thresholds, to the catalogues of New Zealand,all of California, all of Japan, and Greece, and with relatively low thresholds to Kanto and S.California. The information value of the model does not vary appreciably with magnitudethreshold. However, differences in the fitted parameters suggest that, at low magnitudethresholds, the values are affected by the need to accommodate aftershocks in the targetedmagnitude range. A modification is proposed to the magnitude distribution in the EEPASmodel in order to accommodate such aftershocks. The modification should lead to moreconsistent parameter values and improved performance of the model.
การแปล กรุณารอสักครู่..
