EXTREME VALUE RISK MODEL ADAPTED TO SPARSEDATA IN SOUTHEASTERN CHINA OF LOW SEISMICITY
An Weiping 1) ; Qin Changyuan 2) ; and Gen Ailing 3)
1)Earthquake Administration of Shanxi Province,Tai Yuan 0300002)School of Environmental Sciences,University of East Anglia,Norwich NR4 7TJ,UK 3)Earthquake Administration of Hubei Province,Wuhan 430071
Abstract The Gumbel extreme model, especially the third distribution G(M)=exp
[-((ω-m)/(ω-μ))1/λ] is used usually to earthquake risk research because of its prominent advantages, such as the upper bound to magnitude ω etc. Due to the complications of nonlinearity and the limitation of sparse observations, however, its importance has been largely underestimated. The methodology developed here concentrates on two aspects: exploring widening and adaptable use of the observations and finding the proper starting parameters to guarantee the convergence in the nonlinear fitting. Moreover,this new method makes it possible to study the occurrence pattern of large earthquakes in low seismicity regions. In order to expound the distinctive advantage of this method, two data sets from different seismotectonic backgrounds, a low seismicity region from southeastern China and a high seismicity region from western Greece, are analysed. Both of these results are good and stable, as with low fitting errors.
An Weiping ,Qin Changyuan ,and Gen Ailing . EXTREME VALUE RISK MODEL ADAPTED TO SPARSEDATA IN SOUTHEASTERN CHINA OF LOW SEISMICITY [J]. , 2008, 28(3): 27-35.
An Weiping ,Qin Changyuan ,and Gen Ailing . EXTREME VALUE RISK MODEL ADAPTED TO SPARSEDATA IN SOUTHEASTERN CHINA OF LOW SEISMICITY [J]. jgg, 2008, 28(3): 27-35.
