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

doi:10.3969/j.issn.0253-4967.2015.04.022

Abstract

Abstract:

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.

Key words: high-precision hazard bearing body data, earthquake disaster rapid assessment, earthquake emergency rescue, post-earthquake emergency response

摘要：

地震灾情快速评估是震后地震应急响应的核心技术支撑。目前, 随着高精度人口社会经济数据的推广, 中国地震局下属单位的地震灾情快速评估所采用的承灾体数据大多由原来的县级精度提升到30″×30″(经纬度)的高精度。然而, 基础数据精度得到提升的同时, 灾情评估的主要算法和技术流程却没有改变。结果是评估准确度得到了一定提高, 而县级精度数据支撑下的评估耗时较多(10～20min, 大震时可在30min以上), 震害损失空间分布不够精细等问题仍然没有得到解决。因此, 文中首先介绍了高精度承灾体数据(30″×30″), 描述地震灾情快速评估的原理及县级数据支撑下的实现过程, 之后阐述利用高精度承灾体数据改善地震灾情快速评估数据基础的原理, 在评估过程中利用栅格代数运算提高计算效率和保持数据精度的原理, 以及通过分割和重新组织快速评估技术流程提高评估速度的方法。经试验验证, 改进后地震灾情快速评估的准确度有所提高, 能够产出高精度的(30″×30″)的震害分布, 并且评估耗时缩减到1min以内(不含出图耗时)。大范围的高精度承灾体数据是一种模拟数据而非调查数据, 尽管其基础是普查数据, 但准确度与实际情况仍有一定的差距, 需要进一步研究与优化。

关键词: 高精度承灾体数据, 地震灾情快速评估, 地震应急救援, 震后应急响应

CLC Number:

P316

AN Ji-wen, XU Jing-hai, NIE Gao-zhong, BAI Xian-fu. earthquake disaster rapid assessment for emergency response supported by high-precision data of hazard bearing body[J]. SEISMOLOGY AND GEOLOGY, 2015, 37(4): 1225-1241.

安基文, 徐敬海, 聂高众, 白仙富. 高精度承灾体数据支撑的地震灾情快速评估[J]. 地震地质, 2015, 37(4): 1225-1241.

References

陈振拓, 李志强, 丁文秀, 等. 2012. 面向防震减灾的人口数据空间化研究: 以2007年宁洱地震灾区为例［J］. 震灾防御技术, 7(3): 273—284.
CHEN Zhen-tuo, LI Zhi-qiang, DING Wen-xiu, et al. 2012. Study of spatial population distribution in earthquake disaster reduction: A case study of 2007 Ning'er earthquake ［J］. Technology for Earthquake Disaster Prevention, 7(3): 273—284(in Chinese).
程家喻, 杨喆. 1996. 评估地震人员伤亡的软件系统［J］. 地震地质, 18(4): 462—470.
CHENG Jia-yu, YANG Zhe. 1996. A software system of evaluating earthquake casualties ［J］. Seismology and Geology, 18(4): 462—470(in Chinese).
丁文秀, 张亦梅, 陈振拓, 等. 2014. 湖北省人口数据空间化及在巴东M_S5.1地震灾情盲估中的应用［J］. 大地测量与地球动力学, 34(3): 28—30.
DING Wen-xiu, ZHANG Yi-hai, CHEN Zhen-tuo, et al. 2014. Spatialization of population data for Hubei Province and its application to rapid assessment of earthquake loss: A case of Badong M_S5.1 earthquake ［J］. Journal of Geodesy and Geodynamics, 34(3): 28—30(in Chinese).
傅征祥, 李革平. 1993. 地震生命损失研究［M］. 北京: 地震出版社. 78—126.
FU Zheng-xiang, LI Ge-ping. 1993. Research on the Loss of Life in Earthquake ［M］. Seismological Press, Beijing. 78—126(in Chinese).
高惠瑛, 李清霞. 2010. 地震人员伤亡快速评估模型研究［J］. 灾害学, 25(S1): 275—277.
GAO Hui-ying, LI Qing-xia. 2010. Study on the rapid evaluation model for seismic casualties ［J］. Journal of Catastrophology, 25(S1): 275—277(in Chinese).
韩贞辉, 李志强, 陈振拓, 等. 2013. 人口、房屋数据空间化及其在震灾快速评估中的应用: 以彝良地震为例［J］. 地震地质, 35(4): 894—906. doi: 10.3969/j.issn.0253-4967.2013.04.018.
HAN Zhen-hui, LI Zhi-qiang, CHEN Zhen-tuo, et al. 2013. Population, housing statistics data spatialization research in the application of rapid earthquake loss assessment: A case of Yiliang earthquake ［J］. Seismology and Geology, 35(4): 894—906(in Chinese).
胡少卿, 孙柏涛, 王东明. 2010. 基于建筑物易损性分类的群体震害预测方法研究［J］. 地震工程与工程振动, 30(3): 96—101.
HU Shao-qing, SUN Bai-tao, WANG Dong-ming. 2010. A method for earthquake damage prediction of building group based on building vulnerability classification ［J］. Journal of Earthquake Engineering and Engineering Vibration, 30(3), 96—101(in Chinese).
黄耀欢, 杨小唤, 刘业森. 2007. 人口区划及其在人口空间化中的GIS分析应用: 以山东省为例［J］. 地球信息科学, 9(2): 49—54.
HUANG Yao-huan, YANG Xiao-huan, LIU Ye-sen. 2007. A study on Class-2 population regionalization and its application to population spatial distribution based on GIS: A case of Shandong Province ［J］. Geo-information Science, 9(2): 49—54(in Chinese).
李树桢. 1995. 地震灾害评估［M］. 北京: 地震出版社. 71—100.
LI Shu-zhen. 1995. The Assessments of Earthquake Disaster ［M］. Seismological Press, Beijing. 71—100(in Chinese).
李媛媛, 苏国峰, 翁文国, 等. 2014. 地震人员伤亡评估方法研究［J］. 灾害学, 29(2): 223—227.
LI Yuan-yuan, SU Guo-feng, WENG Wen-guo, et al. 2014. A review of researches on seismic casualty estimation ［J］. Journal of Catastrophology, 29(2): 223—227(in Chinese).
刘金龙, 林均岐. 2012. 基于震中烈度的地震人员伤亡评估方法研究［J］. 自然灾害学报, 21(5): 113—119.
LIU Jin-long, LIN Jun-qi. 2012. Study on assessment method for earthquake casualty based on epicentral intensity ［J］. Journal of Natural Disasters, 21(5): 113—119(in Chinese).
马玉宏, 谢礼立. 2000. 地震人员伤亡估算方法研究［J］. 地震工程与工程振动, 30(4): 140—147.
MA Yu-hong, XIE Li-li. 2000. Methodologies for assessment of earthquake casualty ［J］. Earthquake Engineering and Engineering Vibration, 30(4): 140—147(in Chinese).
聂高众, 安基文, 邓砚. 2011. 地震应急评估与决策指标体系的构建［J］. 震灾防御技术, 6(2): 146—155.
NIE Gao-zhong, AN Ji-wen, DENG Yan. 2011. Construction of the index system for earthquake emergency loss evaluation and decision-making ［J］. Technology for Earthquake Disaster Prevention, 6(2): 146—155(in Chinese).
杨斌, 程紫燕, 郑树平. 2011. 山西地震应急评估系统模型本地化研究［J］. 山西地震, (2): 42—45.
YANG Bin, CHENG Zi-yan, ZHENG Shu-ping. 2011. Study on the localization of Shanxi earthquake emergency responding evaluation system model ［J］. Earthquake Research in Shanxi, (2): 42—45(in Chinese).
尹之潜. 1995. 地震灾害及损失预测方法［M］. 北京: 地震出版社. 100—131.
YIN Zhi-qian. 1995. Methods on the Prediction of Earthquake Disaster and Loss ［M］. Seismological Press, Beijing. 100—131(in Chinese).
Azar D, Engstrom R, Graesser J, et al. 2013. Generation of fine-scale population layers using multi-resolution satellite imagery and geospatial data ［J］. Remote Sensing of Environment, 130: 219—232.
Balk D L, Deichmann U, Yetman G, et al. 2006. Determining global population distribution: Methods, applications and data ［J］. Advances in Parasitology, 62: 119—156.
Batista e Silva F, Gallego J, Lavalle C. 2013. A high-resolution population grid map for Europe ［J］. Journal of Maps, 9(1): 16—28.
Bhaduri B, Bright E, Coleman P. et al. 2002. LandScan: Locating people is what matters ［J］. Geoinformatics, 5(2): 34—37.
Bhaduri B, Bright E, Coleman P, et al. 2007. LandScan USA: A high-resolution geospatial and temporal modeling approach for population distribution and dynamics ［J］. Geo-Journal, 69(1-2): 103—117.
Briggs D J, Gulliver J, Fecht D, et al. 2007. Dasymetric modelling of small-area population distribution using land cover and light emissions data ［J］. Remote Sensing of Environment, 108(4): 451—466.
Cai Q, Rushton G, Bhaduri B L, et al. 2006. Estimating small-area populations by age and sex using spatial interpolation and statistical inference methods ［J］. Transactions in GIS, 10: 577—598.
Dobson J E, Bright E A, Coleman P R, et al. 2000. LandScan: A global population database for estimating populations at risk ［J］. Photogrammetric Engineering & Remote Sensing, 66(7): 849—857.
Li G, Weng Q. 2005. Using Landsat ETM+imagery to measure population density in Indianapolis, Indiana, USA ［J］. American Society for Photogrammetry and Remote Sensing, 71(8): 947—958.
Linard C, Gilbert M, Snow R W, et al. 2012. Population distribution, settlement patterns and accessibility across Africa in 2010 ［J］. PLOS ONE, 7(2): e31743.
Liu X H, Kyriakidis P C, Goodchild M F. 2008. Population-density estimation using regression and area-to-point residual Kriging ［J］. International Journal of Geographical Information Science, 22(4): 431—447.
Silván-Cárdenas J, Wang L, Rogerson P, et al. 2010. Assessing fine-spatial-resolution remote sensing for small-area population estimation ［J］. International Journal of Remote Sensing, 31(21): 5605—5634.
Wegscheider S, Schneiderhan T, Mager A, et al. 2013. Rapid mapping in support of emergency response after earthquake events ［J］. Natural Hazards, 68(1): 181—195.
Xu J, Nyerges T L, Nie G. 2014. Modeling and representation for earthquake emergency response knowledge: Perspective for working with geo-ontology ［J］. International Journal of Geographical Information Science, 28(1): 185—205.
Yue T, Wang Y, Liu J, et al. 2005. Surface modelling of human population distribution in China ［J］. Ecological Modelling, 181(4): 461—478.