PSHA模型的算法改进与中国地区未来地震概率评估
详细信息 本馆镜像全文    |  推荐本文 | | 获取馆网全文
摘要
考虑到PSHA模型对未来地震预测特别是大震预测能起到很好的参考分析作用,尝试结合能量分布模型对其进行了改进,并以中国及周边地区200a内的地震历史记录为基础,利用原始PSHA模型以及改进后的PSHA模型分别计算了中国及邻近地区2000~2010年间5级以上地震发生的概率,并对两种结果进行了比较分析,最终证明改进后的PSHA模型具有更好的可靠性。
Considering the importance of PSHA model in earthquakes forecasting,by usingthe catalog in the 200years in China,we tried to improve the original PSHA model withcombining seismic energy distribution model,and computed the probability of earthquakes ina specific area in China with time-span Tbased on both the improved PSHA model and the original model.Finally the experimental results show that the improved model is more efficiency and reliable than the original one.
引文
[1]Frankel A.Mapping Seismic Hazard in the Centraland Eastern United States[J].Seismol Res Lett,1995,66(4):8-21
    [2]Cornell C A.Engineering Seismic Risk Analysis[J].Bull Seismol Soc Am,1968,58:1 583-1 696
    [3]秦长源.地震震级误差对b值的影响[J].地震学报,2000,22(4):338-344
    [4]Davis S D,Frolich C.Single-link Cluster Analysisand Earthquake Aftershocks;Decay Laws and Re-gional Variations[J].J Geophys Res,1991,96:6 335-6 350
    [5]Felzer K R.Appendix I:Calculating CaliforniaSeismicity Rates[R].USGS Open File Report,Pas-adena,California,2007
    [6]卢怡利.1993年至2004年嘉南地震之b值[D].台北:中国台湾国立中正大学,1994
    [7]Lapajne J K.Spatially Smoothed Seismicity Model-ling of the Seismic Hazard in Slovenia[J].J Seis-mol,1997(1):73-85
    [8]汤皓,陈国兴.基于GIS和神经网络模型的场地地震液化势风险评价[J].武汉大学学报.信息科学版,2007,32(8):727-730
    [9]Fu Zhengxiang,Lu Xiaojian,Shao Huicheng,et al.Analysis on Statistical Characteristics of b-values ofAftershock Series in China Continent and Its Subre-gions[J].Earthquake,2008,28(3):1-7

版权所有:© 2023 中国地质图书馆 中国地质调查局地学文献中心