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大多数地震损失研究采取易损性清单方法 ,即通过评估研究区内各类建筑结构和设施的预期损失 ,并将分类损失累加得到总损失估值 .这类方法需要建立详细的社会财富分类系统及其数据资料 ,但在世界上许多地区并不容易搜集可使用的这种资料 .因此提出了基于宏观经济指标 (如国内生产总值 (GDP)和人口资料 )进行地震损失预测评估的方法 ,并对搜集到的 1980～ 1995年间全球震灾资料进行分析 ,立足于地震的破坏烈度得到GDP损失率与地震烈度的统计关系 .通过将全球的陆地以 0 .5°× 0 .5°为单元划分网格 ,并根据网格格点的人口和所属区域的人均GDP值计算得到单元格点的GDP值 ,进而使用GDP损失率的经验关系与地震危险性分析给出的地震概率得出预期的地震损失 ,得到全球地震损失分布图 .采用易于更新获取的社会经济数据作为地震易损性分析的基础 ,提出的方法可方便地用于没有详细的建筑设施分类数据的区域进行地震损失评估 ;对于世界上经济发展快速的地区 ,该方法容易通过社会经济数据的及时搜集和更新得到新的地震损失估计结果 .
Most earthquake loss studies use a vulnerability list approach by assessing the expected loss of various types of building structures and facilities in the study area and accumulating classification losses for total loss estimates.This type of approach requires the establishment of a detailed social wealth classification system And its data, it is not easy to collect such information that is available in many parts of the world.Therefore, a method of earthquake damage prediction assessment based on macroeconomic indicators (such as GDP and population data) Based on the analysis of the global earthquake data collected from 1980 to 1995, the statistical relationship between GDP loss rate and earthquake intensity is obtained based on the seismic intensity of destruction.Through the global land with a unit of 0 .5 ° × 0 .5 ° Divide the grids and calculate the GDP value of the grid points according to the GDP per capita of the population of the grid grids and the area to which they belong, and then use the empirical relationship of the GDP loss rate and the earthquake probability given by the seismic hazard analysis to obtain the expected earthquakes Loss and obtain global distribution map of earthquake loss.Using socio-economic data that is easy to update and obtain as the basis of earthquake vulnerability analysis Zone method can be easily used for building facilities no detailed classification of seismic data loss assessment; for the world’s rapid economic development of the region, which is easy to estimate the results obtained new earthquake losses through timely collection and updating of socio-economic data.