earthquake disaster rapid assessment for emergency response supported by high-precision data of hazard bearing body

The earthquake disaster rapid assessment(EDRA)is the core technical support for the post-earthquake emergency response. At present, with the popularization of high-precision population, social and economic data, most of the subordinate units of China Earthquake Administration(CEA)have heightened the precision of hazard bearing body data used in EDRA from the original county-level precision to the 30″×30″ precision. However, while the precision of fundamental data has been heightened, no efforts have been made to improve the main algorithms and the technical process of EDRA. It turns out that the assessment has become more accurate, but the problems of the time-consuming process(10-20 minutes, probably 20 minutes or more in great earthquakes)and the low-precision losses distributions that exposed in EDRA supported by county-level precision data remain unresolved.This paper introduces the high-precision(30″×30″)hazard bearing body data, and describes the principle of EDRA and its implementation under the support of county-level precision data at first. Then the paper elaborates the principle of improving EDRA's data foundation using high-precision hazard bearing body data, the principle of improving the computation efficiency and persisting the data precision in the assessment process by means of the cell-to-cell grid algebraic operation, and the method for improving the assessment speed through the segmentation and reorganization of the technical process of EDRA.It is validated that through the improvements, the EDRA has become more accurate and much less time-consuming(less than 1 minute), and is able to output high-precision(30″×30″)distributions of seismic losses. The high-precision hazard bearing body data of wide range are the simulated data but not the survey data. Though the data have been simulated based on the census data, there is still a gap between their accuracy and the real situation. Further research and optimization on the data are needed.